Audit Commander Worksheet analyzer
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The document provides a comprehensive guide on using Audit Commander, a software for auditing data contained in Excel spreadsheets. It enables users such as auditors and researchers to perform over 40 analytical functions efficiently without requiring specialized computer skills. The guide details installation procedures, software functionality, and data analysis techniques, along with extensive instructions on using the software for various audit tasks.
![Audit Commands
4.1.2 Round Numbers
Round numbers
Overview / Use in Audit Procedures
Round numbers are often an indicator of estimates, which may be appropriate in certain cases (e.g.
journal entries), but not appropriate in others (e.g. purchase orders, invoices, expense reports, etc.).
The system can be used to identify the extent (if any) to which round numbers are being used as well as
extract data based upon types of round numbers. The system defines a round number as one which is
a whole number (i.e. no pennies), and contains one or more zeros immediately to the left of the decimal
point, without any intervening digits other than zero. The number of such zeros determines the “order”
of the round number. The chart below indicates examples of various round numbers, as well as their
“order”. If a number is not round, then it will be classified as “NR” (not round).
Example Order
15,000.00 3
10 1
123.19 NR
1,000,000.00 6
20.19 NR
Examples of tests which can be performed are provided below:
In a test of purchase orders, determine the frequency of round numbers for purchase orders. There is
an allegation relating to purchases at store number ‘123’.
Approach – using the “round numbers” command, obtain frequencies for round numbers on purchase
orders, classified as to type of round number.
Audit Command values
Column value – Purchase order amount
Text Box – (empty)
Where – [store number] = 123
Results
Frequencies of round numbers used on purchase orders for store number 123.
Usage Example 2
In a test of journal entries, determine the frequency and extent of round numbers in journal entries for
transactions relating to expenses. Expense account numbers begin with the number 3 for this
company .
Approach – using the “round numbers” command, obtain a frequency count.
Audit Command values
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Column value – Amount
Text Box – (empty)
Where – [account number] like ‘3%’
Results
A report classifying the usage of round numbers for account numbers beginning with ‘3’
The example form below is being used to prepare a round number report for the data column named
“Cost”.
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4.1.3 Benford’s Law
Benford’s Law
The Benford’s Law command is generally used as part of a fraud or other forensic investigation. The
purpose will be to determine if numeric values on a schedule conform with that which is expected using
Benford’s Law. The test should only be applied to numeric values which would be expected to adhere to
that expected using Benford’s Law. More information is available about Benford’s law and its use.
There are six types of tests which can be performed for Benford’s Law:
Tests using Benford’s law must specify the type of test being performed:
F1 – Test of the first digit
F2 – Test of the first two digits
F3 – Test of the first three digits
D2 – Test of the second digit only
L1 – Test of the last digit
L2 – test of the last two digits
Usage Example 1
In a test of physical inventory counts, determine if some of the counts may have been made up. It is
expected that actual inventory counts would follow Benford’s law, i.e. a frequency distribution of
inventory counts would align with that expected using Benford’s law. There is an allegation relating to
counts at warehouse 5713.
Approach – using the “benford” command, obtain frequencies for physical inventory counts and compare
those with that expected using benford’s law
Audit Command values
Column value – Inventory count
Text Box – F1
Where – [warehouse] = 5713
Results
Frequencies of first digits of inventory counts, along with a chart and analysis comparing the
results with that expected using benford’s law.
Usage Example 2
In a test of accounts payable, determine if particular vendor invoices have leading digit frequencies as
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would be expected using benford’s law. The vendors in question all have vendor numbers starting with
the letters “R” – “V”.
Approach – using the “benford” command, obtain a frequency count.
Audit Command values
Column value – [Invoice Amount]
Text Box – F1
Where – [Vendor number] like ‘[R-V]%
In the example below, the auditor is testing whether the first digits of the column named cost adhere with
that expected using benford’s Law.
Output results
Benford’s Law
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In a test of accounts payable, stratify the amounts of invoices for sample planning. One objective of the
analysis is to classify the amounts such that 80% of the value can be tested with one procedure and the
remaining 20% with another audit procedure. Only invoices at location ABC are to be classified.
Approach – using the “stratify” command, obtain a data stratification.
Audit Command values
Column value – [Invoice Amount]
Text Box – 0 500 20000 50000 100000
Where – location = ‘ABC’
Results
A report classifying the invoice amounts at location ‘ABC’ into the ranges specified. The results
also include a chart.
Data stratification
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Column value – [Expense Amount]
Text Box – Region, [employee number]
Where – [expense type] = ‘travel’ and month([expense date]) = 6
Results
A report summarizing all travel amounts for the month of June, by region and employee. In
addition to summaries, counts, minima, maxima, averages and standard deviations are shown.
A simpler example is shown in the example below – summarize cost by location and life. All rows are to
be summarized.
Output results
Data summarization
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Usage Example 2
Identify the first five assets which have a net negative book value
Approach – using the “bottomn” command, list the details pertaining to the ten asset records having the
least net book value. This will include any which have a negative net book value. Note that the input
data does not need to be pre-sorted.
Audit Command values
Column value – [asset cost] – [accumulated depreciation]
Text Box – 5
Where – (empty)
Results
A list of the fixed asset records for the 5 assets having the smallest net book value (which will
include negative values if there are any).
In the example below, the auditor wishes to identify the ten asset records which have the largest cost
amounts.
Output results
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4.1.7 Histograms
Histograms
Histograms provide a visual representation for the values or transactions being analyzed. The results
are identical to that of the population statistics, and boxplot commands, except that a different chart is
produced.
Three reports are produced:
1. Basic statistics
2. Histogram data
3. Percentile report
Basic statistics include information such as counts, totals, minimum and maximum values, etc. This
information alone can be used to perform certain audit steps such as agreeing transaction supporting
details to ledger amounts, testing for procedural compliance, etc. Examples of basic statistics reports
can be found in the work papers referenced below:
Usage Example 1
For purposes of audit testing, prepare a histogram of employee expense report amounts.
Approach – using the “histo” command, prepare a chart and detail report as to expense report amounts
at region XYZ.
Audit Command values
Column value – [expense report amount]
Text Box – (empty)
Where – region = ‘XYZ’
Results
A histogram chart of expense report amounts at region XYZ, along with a text report containing
the numeric values.
Usage Example 2
For purposes of testing inventory values, prepare a histogram of inventory unit cost amounts.
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Approach – using the “histo” command, prepare a chart and detail report as to inventory unit cost
amounts.
Audit Command values
Column value – [inventory cost]
Text Box – (empty)
Where – (empty)
Results
A histogram chart of unit inventory costs, along with a text report containing the numeric values.
Where – (empty)
Results
The invoice amounts for each range specified are totaled and counted. Invoices for less than -
$5,000 or ore than $100,000 (the extreme values) are tallied separately.
The example below shows a histogram of cost values is to be prepared.
Output results
Histograms
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4.1.8 Box Plot
Box Plot
The Box Plot command is used to separate a population of numeric values into quartiles in order to see
the values and to also envision how the population is distributed. This provides a little more information
than just the minimum, maximum and median. Except for the chart, the command is identical to the
Population statistics and the histogram command.
Usage Example 1
As part of an audit of accounts payable, the range of invoice costs needs to be determined.
Approach – using the “boxplot” command, prepare a chart and detail report as to invoice costs for
invoices dated after 6/30/2008.
Audit Command values
Column value – [invoice amount]
Text Box – (empty)
Where – [invoice date] > #6/30/2008#
Results
A box plot chart of invoice amounts for invoices dated after 6/30/2008, along with a text report
containing the numeric values.
Usage Example 2
Daily sales ranges needs to be determined for a particular store.
Approach – using the “boxplot” command, prepare a chart and detail report as to daily sales ranges at
store ABC.
Audit Command values
Column value – [sales total]
Text Box – (empty)
Where – [store number] = ‘ABC’
Results
A box plot chart of daily sales ranges, along with a text report containing the numeric values.
The example below will prepare a box plot of cost values for all transactions. This plot could have been
narrowed down by specifying the “Where” information.
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4.1.9 Random numbers
Random numbers are commonly required as part of the sampling process. Excel has a built in
function for the generation of random numbers, “=RAND()”. The Excel RAND function generates
pseudo random numbers evenly distributed between 0 and 1. For many purposes, the pseudo
random number generated using the RAND function may be adequate.
Microsoft documentation at http://support.microsoft.com/support/kb/articles/q86/5/23.asp
(knowledge base article Q86523 ) describes the process used. The starting number is
determined based upon the time of day.
The RAND function is just one of a number of random number generators (RNG). The quality of
a random number generator can be tested using the “DieHard” test suite developed by the
National Institute of Standards (NIST). More information is available at
http://csrc.nist.gov/groups/ST/toolkit/rng/batteries_stats_test.html.
One of the free random number generators is called the Mersenne Twister.
The following description is provided from Wikipedia on the Mersenne Twister
“The Mersenne twister is a pseudorandom number generator developed in 1997
by Makoto Matsumoto (松本 眞?) and Takuji Nishimura (西村 拓士?)[1] that is
based on a matrix linear recurrence over a finite binary field F2. It provides for
fast generation of very high-quality pseudorandom numbers, having been de-
signed specifically to rectify many of the flaws found in older algorithms.
Its name derives from the fact that period length is chosen to be a Mersenne
prime.
The commonly used variant of Mersenne Twister, MT19937 has the following
desirable properties:
1. It was designed to have a period of 219937 − 1 (the creators of the algorithm
proved this property). In practice, there is little reason to use a larger period, as most ap-
plications do not require 219937 unique combinations (219937 is approximately 4.3 × 106001;
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4.2 Date
4.2.1 Holiday Extract
Holiday Extract
Often it is desirable to check if any transaction dates fall on a federal holiday such as the Independence
Day, etc. Although it may be possible to visually check for these dates, it becomes more complicated
when the date falls on a weekend and is therefore celebrated on the preceding Friday (or the following
Monday). This function can analyze all the dates within a specified range and quantify the number that
fall on each of the holiday dates. There are two functions related to holidays. One prepares a summary
of counts of holiday dates and the other extracts transactions whose dates fall on federal holidays.
Usage Example 1
In a test of general ledger, an extract of all journal postings on a federal holiday needs to be obtained.
Approach – using the “holiday” command, extract a list of all journal entries posted on holidays. The
date format being used is month – day – year (mdy).
Audit Command values
Column value – [journal posting date]
Text Box – mdy
Where – (empty)
Results
A list of any journal entry transactions which have been posted on a date which is a federal
holiday. In addition, a summary chart of holiday transactions is prepared.
Usage Example 2
Determine if any receiving reports exist for dates falling on a federal holiday. Date format is mdy.
Approach – using the “holiday” command, extract a list of receiving transactions falling on a federal
holiday.
Audit Command values
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Column value – [receiving report date]
Text Box – mdy
Where – (empty)
Results
A list of any receiving report transactions which occurred on a federal holiday. In addition, a
summary chart of holiday transactions is prepared.
Date format – “mdy” for mm/dd/yyyy or “dmy” – dd/mm/yyyy
Country code – “US” or “CA”.
Note: The default values: US and mdy will be used if no values are specified.
The command example below checks for any records which have an acquisition date falling on a federal
holiday in the United States.
Output results
Holiday Extract
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4.2.2 Week days
Week days
In many instances the auditor wishes to extract just certain data within Excel based upon days of the
week. In this instance one column or row will contain dates which the auditor wishes to examine.
Usage Example 1
In a test of certain expense, an extract is needed for expenses incurred on a Friday or Saturday.
Approach – using the “wd” command, extract a list of all such transactions. The date format being used
is month – day – year (mdy).
Audit Command values
Column value – [expense date]
Text Box – Friday, saturday
Where – (empty)
Results
A list of any expense transactions which fell on a Friday or Saturday are prepared.
Usage Example 2
An audit test is to be performed to identify any travel expense transactions on Saturdays, which is not
allowed at this company.
Approach – using the “wd” command, extract a list of all such transactions. The date format being used
is month – day – year (mdy).
Audit Command values
Column value – [expense date]
Text Box –Saturday
Where – [travel code] = ‘airline’
Results
A list of any expense transactions which fell on a Saturday is prepared.
The day of the week must include at least the first three letters of the week day name. case does not
matter. Thus, Sunday could be specified using any of the following: “sun”, “Sunday”, “sund”, etc.
The example below is used to extract all transactions which fall on either a Saturday or a Monday. Note
that additional selection criteria could have been applied, e.g. store = ‘ABC’ to isolate the extract to just
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4.2.3 Holiday summary
Holiday Summary
In certain instances it is desirable to extract just those transactions in a file which fall on a federal
holiday. These transactions can then be reviewed separately. The holiday extract command can be
used in conjunction with date ranges, location codes or any other criteria which should be applied as
part of the extract.
Usage Example 1
In a test of general ledger, an extract of all journal postings on a federal holiday needs to be obtained.
Approach – using the “holiday” command, extract a list of all journal entries posted on holidays. The
date format being used is month – day – year (mdy).
Audit Command values
Column value – [journal posting date]
Text Box – mdy
Where – (empty)
Results
A list of any journal entry transactions which have been posted on a date which is a federal
holiday. In addition, a summary chart of holiday transactions is prepared.
Usage Example 2
Determine if any receiving reports exist for dates falling on a federal holiday. Date format is mdy.
Approach – using the “holiday” command, extract a list of receiving transactions falling on a federal
holiday.
Audit Command values
Column value – [receiving report date]
Text Box – mdy
Where – (empty)
Results
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4.2.4 Ageing
Ageing
During a review of applications which use both dates and amounts, it is very common to "age" the data
for various purposes - e.g. reasonableness testing, checking for stale or obsolete items, data
classification, etc. The procedure to age data is straightforward:
The date to be used for ageing “Ageing Date”
The width of the ageing range, e.g. 30 days
The name of the column with the date to be aged, e.g. “Due Date”
The name of the column with the amount to be aged, e.g. “Balance Due”
Usage Example 1
In a test of accounts receivable, an ageing of customer account balances is needed.
Approach – using the “age” command, prepare an ageing report for customers in ABC region. Ageing is
to be done as of June 30, 2008. Ageing width is 30 days.
Audit Command values
Column value – [invoice date]
Text Box – invoice date, invoice amount, 6/30/2008, mdy
Where – region = ‘ABC’
Results
An ageing report is prepared for those customer in region ABC as of June 30, 2008.
Usage Example 2
In a test of accounts payable, an ageing of vendor invoices is needed.
Approach – using the “age” command, prepare an ageing report for vendor invoices. Ageing is to be
done as of September 30, 2007. Ageing width is 30 days.
Audit Command values
Column value – [invoice date]
Text Box – invoice date, invoice amount, 6/30/2007, mdy
Where – (empty)
Results
An ageing report is prepared for vendor invoices as of September 30, 2007.
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4.2.5 Date Near
Date Near
Selection of a range of transactions based upon date value is a very common data extraction procedure.
Examples include cut-off testing, re-testing balances for a specified period, etc.
There are two equivalent procedures for doing such an extraction -
1. DateRange - the auditor specifies a starting and ending date, and
2. DateNear - the auditor specifies a date and the maximum number of days from the date (e.g.
three days before or after July 4th)
Usage Example 1
For cutoff testing, the auditor wants to identify any sales made within 5 days of June 30, 2008.
Approach – using the “datenear” command, prepare a list of any such transactions.
Audit Command values
Column value – [sales date]
Text Box – 6/30/2008, 5
Where – (empty)
Results
A list of any sales transactions within five days of June 30, 2008, i.e. June 25, 2008 – July 5,
2008.
Usage Example 2
For accrual testing, the auditor wants to identify any accruals posted within 15 days of June 30, 2008.
Only account numbers beginning with either a ‘2’ or a ‘3’ are to be selected.
Approach – using the “datenear” command, prepare a list of any such transactions.
Audit Command values
Column value – [journal date]
Text Box – 6/30/2008, 15
Where – [account number] like ‘[2-3]%’
Results
A list of any accruals posted within 15 days for the account numbers specified.
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4.2.6 Date Range
Date Range
The date range test is the same as “date near”, except specific dates are provided.
Usage Example 1
For cutoff testing, the auditor wants to identify any sales made between 6/25/2008 and 7/5/2008.
Approach – using the “daterange” command, prepare a list of any such transactions.
Audit Command values
Column value – [sales date]
Text Box – 6/25/2008, 7/5/2008
Where – (empty)
Results
A list of any sales transactions within the specified range, i.e. June 25, 2008 – July 5, 2008.
Usage Example 2
For accrual testing, the auditor wants to identify any accruals posted within 15 days of June 30, 2008.
Only account numbers beginning with either a ‘2’ or a ‘3’ are to be selected.
Approach – using the “daterange” command, prepare a list of any such transactions.
Audit Command values
Column value – [journal date]
Text Box – 6/15/2008, 7/14/2008
Where – [account number] like ‘[2-3]%’
Results
A list of any accruals posted within 15 days for the account numbers specified.
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4.2.7 Week days Report
Week days report
The week days report summarizes the count of transactions by day of week. This test may be used for
reasonableness tests, audit planning, etc. The report consist of both text and a chart.
Usage Example 1
In an audit of expense reports, the counts of expenses by day of week are needed.
Approach – using the “wdreport” command, summarize such transactions.
Audit Command values
Column value – [expense report date]
Text Box – mdy
Where – (empty)
Results
A summary of counts of expense report transactions by day of week.
Usage Example 2
In an audit of purchasing, the counts of purchase orders issued by day of week are needed.
Approach – using the “wdreport” command, summarize such transactions.
Audit Command values
Column value – [purchase order date]
Text Box – mdy
Where – (empty)
Results
A summary of counts of purchase order transactions by day of week.
Date format – “mdy” for mm/dd/yyyy or “dmy” – dd/mm/yyyy
Country code – “US” or “CA”.
Note: The default values: US and mdy will be used if nothing is specified.
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4.3.4 Same, Same, Different
Same, Same, Different
Unusual or error conditions may be detected using the “same, same, different” test. An example during
a review of invoice transactions would be two invoice payments which had the same vendor, same
invoice number, same date, but different amounts. Similarly, during a review of the employee master
file, two records might be identified which have the same employee last name, same employee first
name, same city, same street, but different social security numbers. The purpose of the same, same,
different procedure is to identify any such records, if they exist.
The test is performed using the names of the columns to be tested.
The names of each column to be tested for same, same different, separated
by commas. The last column specified is that which is tested for being
different. For example, in the invoice example above, the testing
specification would be “[Vendor Number],[Invoice Number],[Invoice date],
[Invoice Amount]” (without the quotes).
Usage Example 1
In an audit of accounts payable, test for the unusual situation described above.
Approach – using the “ssd” command, analyze the transactions.
Audit Command values
Column value – [blank]
Text Box – [Vendor Number],[Invoice Number],[Invoice date],[Invoice
Amount]
Where – (empty)
Results
A schedule of any transaction pairs which have the same vendor number, invoice number,
invoice date, but a different invoice amount.
Usage Example 2
In an audit of payroll transactions, check for any pair of records which have the same employee last
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name, same employee first name, same street address, but different employee numbers. Tests are to
be made only for those employees in Florida, Georgia and Alabama.
Approach – using the “ssd” command, analyze such transactions.
Audit Command values
Column value – [empty]
Text Box – [last name],[first name], [street address], [employee number]
Where –state in (‘FL’,’GA’,”AL’)
Results
Schedule of any such records identified.
The example below illustrates the procedure for identifying instances of fixed asset records which have
the same tag number but a different location.
Output results
Same, Same, Different
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Usage Example 1
In an audit of accounts payable, the auditor wishes to see a trend as to invoice totals for a specified
vendor, by quarter, in order to view the overall trend and to see if there may be any unusual items such
as “spikes”, missing data, etc.
The date column to be used is called “invoice date”, and the amount column to be analyzed is called
“invoice amount”. Tests are to be done at a 95% confidence level. The command would be as follows:
[invoice date], [invoice amount], q, sum, .95
Usage Example 2
Continuing with the same example, the auditor now wants to see transaction counts by month. The
command would then be as follows:
[invoice date], [invoice amount], m, count, .95
The command box above performs a time line analysis of asset acquisitions using the “cost” column,
and specifying a period of “q” (quarterly) with a precision of 95%.
The chart produced is shown below.
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4.3.11 Check SSN
Validity of Social Security Numbers
The purpose of testing for Social Security number validity is to identify any social security numbers
which would be considered invalid according to the criteria published on the site of the Social security
Administration. The test considers several factors:
• Ranges of numbers issued
• Certain digits or ranges which are automatically invalid
• The highest number assigned for an area
Note: The social security number ranges are published monthly by the Social Security
Administration.
Warning: Social security numbers of deceased persons will not be identified.
Usage Example 1
A test of validity of social security numbers is to be performed on data where the social security number
column is named “SSN”.
Audit Command values
Column value – [SSN]
Text Box – (empty)
Where – (empty)
Results
A list of all records where the social security number is invalid.
The input form used to perform the checking is shown below.
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4.3.13 Calculated Values
Calculated Values
In many instances the auditor wishes to add a column of data, e.g. a calculated amount, based
upon values contained in other columns. Calculated values
A common procedure used during the analysis of data in Excel is to insert one or more columns and
calculate their value using formula which based on values contained in other columns. Although
this procedure is effective, it has the drawback that column letters must be used instead of column
names which makes interpreting and verifying the formulae used more difficult.
The purpose of the calculated values procedure is to add one or more columns to a work sheet us-
ing formula with column names. Often the formula will consist of mathematical operations, but any
SQL function may be used (see list of functions in description of where clause values).
The syntax for the calculated values is "expression1 as name1, expression2 as name2" etc. where
"expression" is a calculated value. The word "as" must be used without change, and "name" must
be a description beginning with a letter and consisting of only letters, numbers and the special char-
acters "$", "_". If the name contains any embedded spaces, then the entire name must be enclosed
in brackets, e.g. "[cost amount]".
Examples -
Add a column called net book value computed as cost less accumulated depreciation
Other info - [cost] - [accumulated depreciation] as [net book value]
(Note the use of brackets due to embedded spaces in the names)
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4.3.15 Fuzzy Match (Regular Expression)
Fuzzy Match (regular expression)
Selection of subsets of data within a worksheet based upon more complex matching patterns is possible
using the "fuzzy match" command. As an example, the auditor may wish to select all records for asset
tag numbers that begin with "98", followed by any character or digit and then contain the digit "5". Other
examples include all store locations beginning with the letters "A' through "C", followed by two digits and
then one or more of any characters. All of these matches can be done using the technique of "regular
expressions".
There is fairly extensive documentation on how regular expressions work, but they generally consist of
one or more special search characters with the following meanings -
• ? - match any single character
• * - match any one or more characters
• [A-H] - match any single letter between "A" and "H"
• [!A-H] - match any single character, except the letters "A" through "H"
In order to do fuzzy matching, the auditor sets
Usage Example 1
A search is to be made of employee last names where the first letter is “H” and the second letter is
any of the characters “E” through “I”. The last name to be matched can contain two or more letters
in total. The search specification is shown in the form below.
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4.4 Patterns
4.4.1 Round Numbers
An example will best illustrate the concept of pattern testing for round numbers. Consider a
case where journal entries are prepared at the end of each month. Generally, journal entry
postings will contain some round numbers. Although somewhat tedious, the auditor could
determine the count of round numbers posted for the year. For example, there might be a total
of 2,000individual journal entry postings for the year. Of those, 100 (or 5%) were round
numbers, possibly indicating an estimate. If the round number postings were fairly evenly
spread throughout the year, this would indicate that possibly nothing unusual exists, based upon
a comparative test of round numbers. However, if the concentration is in the last month of the
fiscal year (or the first month of the next fiscal period), then this could be a different situation.
Pattern testing is based upon the overall concept outlined above. The procedure first obtains
counts or totals for the entire transaction population. Then the procedure separates the
population based upon criteria specified by the auditor (in the example above posting month)
and then systematically compares each subgroup with the overall population. The system then
reports each group based upon how different it is from the overall population as measured by
the statistical test “Chi Square”.
This same test can also be applied using metrics other than round numbers – e.g. counts by day
of week, counts by holidays, counts by data stratification, etc.
Usage Example 1
In an audit of accounts payable, a comparative analysis is to be made of purchase orders by
buyer to determine which buyers purchase orders are the most different from all others as
measured by the type and quantity of round numbers.
Approach – using the “patternrn” command, check the purchase orders.
Audit Command values
Column value – [purchase order amount]
Text Box – [buyer number], [purchase order amount]
Where – (empty)
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Results
A list of the results of pattern matching for all buyers. The list is in descending order, first
showing the buyer whose pattern is the most different.
Note: The transactions do not need to be “pre-sorted”.
Usage Example 2
A test is to be performed for usage of round numbers in general journal entries by the person
preparing the journal entry. The column name for the journal entry preparer is “preparer”.
Approach – using the “patternrn” command, check the journal entries.
Audit Command values
Column value – [journal amount]
Text Box – [preparer], [journal amount]
Where – (empty)
Results
A list of the results of pattern matching for all preparers. The list is in descending order,
first showing the preparer whose pattern is the most different.
Usage Example 3
A test is to be performed for usage of round numbers in fixed asset costs by location.
Pattern analysis using round numbers
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4.4.2 Data Stratification
Pattern analysis using data stratification
An example will best illustrate the concept of pattern testing using stratification. Consider a case where
inventory is being taken at the end of each month at separate warehouse locations. Unless the
warehouses have a significantly different “mix” of items, a stratification of the inventory values by item
will generally follow the same pattern of counts and values. Although somewhat tedious, the auditor
could stratify the amounts manually and then visually compare the results. For example, one
warehouse might have a much larger number of low (or high) value items than the others. Certainly this
could be a valid situation, but it might also represent an error as well.
Pattern testing is based upon the overall concept outlined above. The procedure first obtains counts or
totals for the entire transaction population. Then the procedure separates the population based upon
criteria specified by the auditor (in the example above warehouse) and then systematically compares
each subgroup with the overall population. The system then reports each group based upon how
different it is from the overall population as measured by the statistical test “Chi Square”.
Usage Example 1
In an audit of inventory, the inventory values are known to be clustered in a certain pattern.
Approximately 20% of all inventory items have a value under $100. Then 50% have a value under $200
and 80% have a value under $500. The stratification ranges used to obtain these results were the bin
values of 0, 100, 200, 500
A test is to be made to identify the warehouse location which has inventory value which are the most
different from this pattern as measured using data stratification and the bin values above,
Approach – using the “Pattern - stratification” command, analyze the inventory values. .
Audit Command values
Column value – [unit cost]
Text Box – [location],[unit cost], 0, 100, 200, 500
Where – (empty)
Results
A list, by location, of the measures of the difference between the values at that location and
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4.4.3 Day of Week
Pattern analysis using day of week
An example will best illustrate the concept of pattern testing by day of week. Consider a case for the
retail environment. Generally, sales tend to be concentrated on Fridays, Saturdays and Sundays, with
much lesser amounts on say Monday and Tuesday. If the auditor is looking at a group of locations
(stores), then this test can identify which stores have sales patterns that are the most statistically
different, as measured using standard statistical tests. Although differences in patterns may be
explainable, they may also result from errors. Alternative tests can be performed using month of year
instead of store location, etc.
Usage Example 1
In an audit of revenue in a retail environment, determine which store’s revenue was the most different,
based upon analysis by day of week.
Approach – using the “patternwd” command, analyze such transactions.
Audit Command values
Column value – [trans date]
Text Box – [store number],[transdate]
Where – (empty)
Results
A listing of summary results, by store location, in descending order
Usage Example 2
In an audit of journal entries, determine which account’s postings were the most different, based upon
the day of the week they were posted.
Approach – using the “patternwd” command, analyze such transactions.
Audit Command values
Column value – [ posting date]
Text Box – [account number], [posting date]
Where – (empty)
Results
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4.4.4 Holidays
Pattern analysis using holidays
An example will best illustrate the concept of pattern testing by holiday. Consider a case for the retail
environment. In some cases, sales tend to be concentrated on certain holidays. If the auditor is looking
at a group of locations (stores), then this test can identify which stores have sales patterns that are the
most statistically different, as measured using standard statistical tests. Although differences in patterns
may be explainable, they may also result from errors. Alternative tests can be performed using month of
year instead of store location, etc.
Usage Example 1
In an audit of revenue in a retail environment, determine which store’s revenue was the most different,
based upon analysis by sales on holidays.
Approach – using the “patternhol” command, analyze such transactions.
Audit Command values
Column value – [trans date]
Text Box – [store number],[transdate]
Where – (empty)
Results
A listing of summary results, by store location, in descending order
Usage Example 2
In an audit of journal entries, determine which account’s postings were the most different, based
postings made on holidays.
Approach – using the “patternhol” command, analyze such transactions.
Audit Command values
Column value – [ posting date]
Text Box – [account number], [posting date]
Where – (empty)
Results
A listing of summary results, by account number, in descending order
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4.4.5 Benford’s Law
Pattern analysis using Benford’s Law
Many accounting transaction amounts will tend to follow that expected using Benford’s law unless there
is a compelling reason that they should not (e.g. upper or lower transaction limits, recurring amounts,
etc.).
The pattern test for Benford’s law separates the population into groups and then computes the expected
and observed values using Benford’s law for that group. An example might be inventory counts taken at
various warehouses. Inventory counts should conform with that expected using Benford’s Law. By
applying a pattern test by warehouse, it is possible to identify which warehouse had inventory counts
that differed the most from that expected using Benford’s law.
Usage Example 1
In an audit of expense reports, a test is to be made to determine which employee’s expense reports
were the most different from all other expense reports, based upon Benford’s Law.
Approach – using the “patternben” command,analyze expense report transactions.
Audit Command values
Column value – [expense amount]
Text Box – [employee number], [expense amount], F1
Where – (empty)
Results
A listing of summary results, by employee number, in descending order
Usage Example 2
In an audit of inventory counts, a test is to be made to determine which inventory counts were the most
different from all other warehouse locations , based upon Benford’s Law.
Approach – using the “patternben” command, analyze inventory count transactions.
Audit Command values
Column value – [inventory count]
Text Box – [warehouse], [inventory count], F1
Where – (empty)
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6.1 Numeric
Function Example Description
Numeric equality [asset cost] = 1000 Asset cost is exactly 1,000
Greater than [asset cost] > 1000 Asset cost is greater than 1,000
Less than [asset cost] – Net Asset cost is less than 1,000
[accumulated
depreciation]< 1000
Greater or equal [asset cost] >= 1000 Asset cost is greater than or equal
Less than or equal [sales amount] * .04 <= 10 Tax amount at 4% is less than or equal to
10
Not equal [asset cost] <> 1000 Asset cost is not 1,000
Mod [asset cost] mod 10 – 2 Asset cost ends in 2
Mod [asset cost] mod 100 – 0 Evenly divisible by 100
Abs Abs([asset cost] – 100) <= Asset cost is within $.02 of 100, i.e.
.02 99.98 – 100.02
Rnd Rnd() A random number
Is numeric Isnumeric Isnumeric(amount) = -1
Round Round(cost,2) Round the cost to the penny
6.2 Text
Function Example Description
Length Len(location) = 6 Length of location name is six characters
Mid Mid(location,2,3) Character positions 2 3 and 4
Left Left(location,2) = ‘AB’ Left most two characters
Right Right(location,2) = ‘XY’ Location name ends in XY
Instr Instr(location,’test’) > 0 Location contains the text ‘test’
LCase Lcase(lastname) = “smith’ Lower case value for last name
Ucase Ucase(lastname) = Upper case values
‘SMITH’
Trim Trim(lastname) = ‘smith’ Remove left and right blanks
Ltrim Ltrim(lastname) Remove blanks on the left
Rtrim Rtrim(lastname) Remove blanks on the right
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6.4 Logical tests
Function Example Description
OR Cost < 100 or life > 7 Test that at least one of the conditions is
true
AND Cost < 100 and life > 7 Test that both conditions are true
NOT Obtain second portion of Left most two characters
date/time value
BETWEEN Trandate between Values between a date range
#7/1/2005# and
#6/30/2006#
BETWEEN Amount between 100 and Value between 100 and 900
900
BETWEEN Location between ‘AB’ Value between ‘AB’ and ‘LM”
and ‘LM”
IN Location Value is one of three specified values
in(‘103’,’105’,’106’)
LIKE Location like ‘10%’ Location name starts with 10
6.5 Combinations
Functions can be combined using the logical tests described in section 7.4. For example, to test
asset records acquired during a specific fiscal period which also have useful lives exceeding ten
years the criteria would be specified as follows using the “AND” connectior:
([installation date] between #7/1/2007# and #6/30/2008#) and ([useful life] > 7)
6.6 Nesting functions
Often several functions need to be applied at the same time. For example to test if the first three
letters of the last name are ‘Bla’, without considering case the following criteria would be applied:
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Ucase(left([last name],3)) = ‘BLA’
If the last name may also have blanks to the right of the last character, then an
additional function (“trim”) could be first applied before the remaining tests:
Ucase(left(trim([last name]),3)) = ‘BLA’
6.7 Selection criteria
There are at least three separate techniques for the identification of ranges or
multiple values:
1. Between
2. In
3. Like
The between operator allows the specification of a range of values which may be
text, numeric or date – e.g.
Between #7/1/2007# and #6/30/2008#
Between ‘A’ and ‘M’
Between 100 and 2000
The in operator allows the specification of a number of text values, each separated
by a comma, e.g. to test if a specific state code has been located:
[State Code] in (‘FL’,’GA’,’AL’,’NC’)
The like operator allows tests for patterns.
Operator Meaning
[last name] like ‘BLA%’ Last name starts with ‘BLA’
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Audit Commander Worksheet analyzer
- 1. Auditing Data contained in Excel Worksheets Audit Commander Audit Guide Data analysis made easier… EZ-R Stats, LLC Auditing data on Excel worksheets
- 2. Audit Commander The software described in this document makes data analysis easier, particularly if it is contained in an Excel work book. The software may be freely downloaded and used without restriction for any purpose – commercial, educational or personal. Additional information about the audit software is available at the web site. Although a significant amount of testing has been performed, there is no guarantee that every function works as documented. All comments and suggestions are welcome. Comments The software is currently being used to teach auditing concepts, statistical sampling and data mining. EZ-R Stats, LLC is registered with the North Carolina State Board of Certified Public Accountant Examiners as a provider of Continuing Professional Education. Auditing data on Excel worksheets
- 3. Auditing data on Excel worksheets Document History Revision History Revision Revision Date Summary of Changes Author Number 1.0 10-17-2009 Initial Version M. Blakley 1.1 11-12-2009 Trend Line and additional M. Blakley error checking. New style of input form.
- 4. Auditing data on Excel worksheets Table of Contents 1 ABOUT THIS GUIDE................................................................................................1 1.1 Who Should Use It...........................................................................................................................................1 1.2 Typographical Conventions ...........................................................................................................................1 1.3 Purpose.............................................................................................................................................................2 1.4 Scope.................................................................................................................................................................2 1.5 Intended audience............................................................................................................................................3 1.6 Hardware requirements..................................................................................................................................3 1.7 Software requirements....................................................................................................................................3 2 GETTING STARTED.................................................................................................4 2.1 Working with Excel data.................................................................................................................................4 2.2 Audit objectives................................................................................................................................................5 2.3 Accomplishing audit objectives......................................................................................................................5 3 USING THE SOFTWARE.........................................................................................6 3.1 Opening form...................................................................................................................................................7 3.2 Analyzing data on Excel worksheets............................................................................................................10 3.2.1 Selecting the data for analysis..................................................................................................................10 3.2.2 Selecting the columns for analysis...........................................................................................................12 3.2.3 Select chart colors....................................................................................................................................14 3.2.4 Select the command to be processed........................................................................................................15 3.2.5 Specifying selection criteria.....................................................................................................................20
- 5. Auditing data on Excel worksheets 3.2.6 The logging facility..................................................................................................................................21 4 AUDIT COMMANDS.................................................................................................1 4.1 Numeric............................................................................................................................................................2 4.1.1 Population Statistics...................................................................................................................................2 4.1.2 Round Numbers..........................................................................................................................................7 4.1.3 Benford’s Law..........................................................................................................................................11 4.1.4 Stratify......................................................................................................................................................15 4.1.5 Summarization..........................................................................................................................................19 4.1.6 Top and Bottom 10...................................................................................................................................22 4.1.7 Histograms................................................................................................................................................25 4.1.8 Box Plot....................................................................................................................................................29 4.1.9 Random numbers......................................................................................................................................33 4.2 Date.................................................................................................................................................................37 4.2.1 Holiday Extract.........................................................................................................................................37 4.2.2 Week days.................................................................................................................................................41 4.2.3 Holiday summary.....................................................................................................................................44 4.2.4 Ageing......................................................................................................................................................48 4.2.5 Date Near..................................................................................................................................................52 4.2.6 Date Range...............................................................................................................................................54 4.2.7 Week days Report.....................................................................................................................................56 4.3 Other...............................................................................................................................................................59 4.3.1 Gaps in Sequences....................................................................................................................................59 4.3.2 Data Extraction.........................................................................................................................................62 4.3.3 Duplicates.................................................................................................................................................66 4.3.4 Same, Same, Different..............................................................................................................................69 4.3.5 Trend Lines...............................................................................................................................................72 4.3.6 Time Line analysis....................................................................................................................................75 4.3.7 Confidence Band......................................................................................................................................82 4.3.8 Confidence Band (Time Series)...............................................................................................................85 4.3.9 Invoice Near Miss....................................................................................................................................89 4.3.10 Split Invoices..........................................................................................................................................92 4.3.11 Check SSN..............................................................................................................................................94 4.3.12 Check PO Box........................................................................................................................................97
- 6. Auditing data on Excel worksheets 4.3.13 Calculated Values.................................................................................................................................100 4.3.14 Fuzzy Match (LD)................................................................................................................................103 4.3.15 Fuzzy Match (Regular Expression)......................................................................................................105 4.3.16 Sequential Invoices...............................................................................................................................108 4.4 Patterns.........................................................................................................................................................110 4.4.1 Round Numbers......................................................................................................................................110 4.4.2 Data Stratification...................................................................................................................................114 4.4.3 Day of Week...........................................................................................................................................117 4.4.4 Holidays..................................................................................................................................................120 4.4.5 Benford’s Law........................................................................................................................................123 4.5 Sampling.......................................................................................................................................................126 4.5.1 Attributes – Unrestricted: Stop and Go..................................................................................................126 4.5.2 Variable Sampling – Unrestricted Stop and Go......................................................................................133 4.5.3 Stratified Variable Sampling – Population.............................................................................................139 4.5.4 Stratified Variable Sampling – Assessment............................................................................................142 4.5.5 Stratified Attribute Sampling – Population............................................................................................144 4.5.6 Stratified Attribute Sampling – Assessment...........................................................................................147 5 ACCESS DATABASES AND EXCEL WORKBOOKS.........................................149 5.1 Overview.......................................................................................................................................................149 5.2 The “Excel/Access” menu item...................................................................................................................150 5.3 An example...................................................................................................................................................151 5.4 Working with text files................................................................................................................................155 5.5 The “File” tab...............................................................................................................................................155 5.6 An example...................................................................................................................................................156 6 TECHNIQUES FOR “DRILL DOWN”..................................................................160 6.1 Numeric........................................................................................................................................................162 6.2 Text................................................................................................................................................................162
- 7. Auditing data on Excel worksheets 6.3 Date / Time...................................................................................................................................................163 6.4 Logical tests..................................................................................................................................................164 6.5 Combinations...............................................................................................................................................164 6.6 Nesting functions..........................................................................................................................................164 6.7 Selection criteria..........................................................................................................................................165 7 APPENDIX – SOFTWARE INSTALLATION........................................................167 8 COMMENT FORM ...............................................................................................173
- 8. 1 About this guide This document is divided into the following chapters: • Chapter 1 – Overview • Chapter 2 – Getting started • Chapter 3 – Auditing data on Excel work sheets • Chapter 4 –The commands and how to use them • Chapter 5 –Access databases and Excel workbooks • Chapter 7 –“Drill down” • Appendix – Software installation 1.1 Who Should Use It Auditors, researchers, business analysts and academics who use data analysis to perform their jobs. • Auditors: can use the software to for a variety of common audit tasks. Altogether, over 40 useful analytical audit functions are included • Researchers: use the software for: • Data analysis, trend investigation • Preparation of statistical reports and charts 1.2 Typographical Conventions This document uses the following typographical conventions: Auditing data on Excel worksheets Page 1
- 9. Auditing data on Excel worksheets • Command and option names appear in bold type in definitions and examples. • Screen output and code samples appear in mono space type. 1.3 Purpose The purpose of this monograph is to provide a practical guide to auditing data contained on Excel work sheets using the Audit Commander. Over 40 useful audit tests and data analyzes can be performed. Although the primary source of data will be that contained on Excel work sheets, the technique described also applies to certain other data sources such as Excel workbooks, Access databases, as well as text files that are in a specific format (“tab separated values”). The auditor does not need special computer skills in order to be able to perform these tests because they are largely menu driven with “fill in the blanks”. Development of the software began in August 2005 when the author searched fruitlessly for a relatively easy to use, economical software package for analyzing data on Excel work sheets (and other). During its development, suggestions and improvements were made by a variety of audit practitioners. More information about the system is available from the website, More information is also available about the author. 1.4 Scope This guide explains how to install the software, the general purpose of the functions provided, as well as examples of use. Auditing data on Excel worksheets Page 2
- 10. Auditing data on Excel worksheets 1.5 Intended audience The software is intended for use by both internal and external auditors, researchers, program monitors, students learning data analysis, business analysts and anyone else interested in analyzing data contained on Excel work sheets in a more efficient and effective manner. 1.6 Hardware requirements At least 512 MB of memory (more if possible). Minimum disk space is 27 MB. 1.7 Software requirements Works only in Windows XP, Vista or Windows 7. Requires ActiveX Data Objects which is part of SP1. (ActiveX Data Objects can be downloaded from the Microsoft web site at no charge) Auditing data in Excel Page 3 worksheets
- 11. Auditing data on Excel worksheets 2 Getting Started 2.1 Working with Excel data Although Excel is a powerful tool, some audit analyzes are difficult or time consuming to perform. The worksheet analyzer is a stand-alone program which is suitable for performing more than 30 of the most commonly needed analytical tests. This program also includes very powerful “drill- down’ capabilities to enable the auditor or researcher to quickly isolate and locate the data that is of special interest. This system does not require that the data be pre-sorted or specially formatted. The worksheet analyzer is generally used to analyze all or portions of single Excel spread sheets. However, it can also be used to analyze data contained within MS-Access databases, as well as text files in various formats (e.g. comma separated values, tab separated values, print format, etc.) The worksheet analyzer derives much of its capabilities by leveraging the software provided by Microsoft called “ActiveX Data Objects” which provides significant database capabilities. These database capabilities are in turn incorporated into and used by the software to provide a variety of capabilities of special interest to auditors and data analysts. The primary advantages of the Work sheet analyzer include: • Pre-built functions for the most common audit tasks • Significantly reduced time required to perform more complex extracts and analyzes • No need to “pre-sort” the data • Built-in help functions to simplify the process • Small footprint - doesn’t require a lot of screen “real estate” Auditing data on Excel worksheets Page 4
- 12. Auditing data on Excel worksheets • Logging facility – log work performed, can be shared or used as a basis for future analysis The primary disadvantages of the Work sheet analyzer include : • Is not completely “bullet proof” (some mistyped commands cause it to crash) • Much slower with Excel 2007 than Excel 2003 • Computations for attribute sampling are slow with populations > 1,000 2.2 Audit objectives As each available command is presented, one or more examples of specific audit objectives which might be accomplished using that command will be included and discussed. Often entire audit steps can be accomplished using the commands built into the system 2.3 Accomplishing audit objectives Often, data being audited is available in Excel worksheets, after it has been extracted or downloaded from various data sources. Once this data has been loaded onto one or more Excel work sheets, the analyst should often perform a variety of tests in order to be able to arrive at an audit conclusion. Auditing data in Excel Page 5 worksheets
- 13. Auditing data on Excel worksheets 3 Using the software Although the software is a stand-alone program, by design it is intended for use with Excel, and is small enough that the form can reside along side the Excel workbook which contains the data to be examined. This is done by having both the Excel workbook open as well as the Audit Commander form on the same page while both are open. This makes it easier to transfer data back and forth between the systems while doing a review. An example screen shot is shown below to illustrate a case where a range of data on the worksheet is being analyzed. By intentionally keeping the Audit Commander form small, it becomes easier to transfer the information from the Excel work book to the form, analyze the data and then “paste” the results Auditing data on Excel worksheets Page 6
- 14. Auditing data on Excel worksheets back into the Excel work book. Note that the results of any analysis performed are also stored in the audit directory specified, so it is not necessary to also store the results in Excel. 3.1 Opening form The opening form has three main menu items as shown below. Each of these menu items are used to provide various types of processing information in order to analyze data. The “commands” menu item is used to select the command or type of analysis to be performed. The remaining menu items are “forms” which are used to gather and process information. A summary description of the purpose of each form is provided in the table below. Tab Name Purpose Clipboard Process data that has been copied to the clipboard (generally from Excel sheets but can include others) Text files Analyze data contained in text files (e.g. comma separated value format, tab separated value format, etc.) Auditing data in Excel Page 7 worksheets
- 15. Auditing data on Excel worksheets Excel/Access Analyzing data in Excel workbooks or Access databases Where Specifying and using more complex selection criteria Report View report produced (report is also written to a file) Chart Chart title and color scheme for chart prepared (if applicable) Audit Audit and folder information The typical sequence used for running an audit analysis of data on a worksheet is as follows: 1. If not already done, specify the location where the audit results are to be stored, along with the audit title, audit step number, etc. (“Audit” form) 2. Select the type of analysis to be performed (menu of 40+ commands) 3. Select the data to be analyzed, the columns or rows to be tested, along with any additional information required for the analysis (“Clipboard/MS/Text” form) 4. If specific criteria are to be used (i.e. the test is for an extract of the data), specify this information (“Where” tab) 5. If the data to be tested is from the clipboard, then copy the data to be tested from the worksheet. This is done by first highlighting the data, then copying it to the clipboard using methods such as 1) keyboard combination “Control-C”, 2) menu selection “Edit| Copy”, or 3) right mouse click and select “Copy”. (“Clipboard” form) 6. On the tab labeled “Form”, click the button labeled “Run” (“Clipboard” form) 7. Wait until the analysis is finished, as indicated with a status message on the Status Bar of the Audit Commander form. (“Clipboard” form) 8. View the report (“Report” tab) 9. If desired, the output in the audit folder specified may also be viewed. This includes both a text report as well as any charts prepared (if applicable). 10. Analysis report results can also be copied to the clip board (“Report” tab) 11. Change audit parameters or specify different tests and repeat the steps above Note: If the data to be tested resides in an Excel workbook, Access database or text file, then “MS” or “File” tabs are used instead. Auditing data on Excel worksheets Page 8
- 16. Auditing data on Excel worksheets Each of these steps are illustrated below using an example analysis. In this analysis, the auditor wishes to perform a test of fixed asset costs using Benford’s Law. Step 1 – Specify audit information (if not already done) Clicking on the “Audit” tab displays the information used to store the results for the analysis performed. If any of this information needs to be changed, it can be overtyped and then the button labeled “Update” clicked to store the information. The folder shown (in this case C:testtemp” is the location where the reports and graphics produced by the audit analysis will be stored. The folder name can be selected by clicking on the button labeled “Folder”, or else overtyping the name in the text box. The step number is used to uniquely identify the output. The starting step number is shown above, and will be increased by one every time a procedure is run. Once the information has been entered, click on the button labeled “Update” to save the information. An informational message will be displayed on the status bar to acknowledge that the change has been applied. This change will be in effect until the next change is applied. Warning: Existing report files and graphics can be overwritten if the starting step number is too low. Auditing data in Excel Page 9 worksheets
- 17. Auditing data on Excel worksheets 3.2 Analyzing data on Excel worksheets Once the audit parameter information has been entered (or checked), the data analysis procedures can be performed. If the data to be analyzed is contained on an Excel worksheet, then the analysis process begins with the first tab, which is labeled “Form”. Note: If data in Excel work books, Access databases or text files are to be analyzed, the tables “MS” and “File” should be used instead. 3.2.1 Selecting the data for analysis The first step is to select the data to be analyzed. This is done by highlighting the area on the worksheet to be analyzed and then copying it to the clipboard using any of four methods: 1. Press the keyboard combination “Control – C” 2. Right mouse click and specify “Copy” Auditing data on Excel worksheets Page 10
- 18. Auditing data on Excel worksheets Often, the data to be reviewed will be in vertical format as shown here. However, in some cases the data will be organized horizontally (e. g. in comparative financial statements). If the data is organized horizontally, then the checkbox “rows” on the main form needs to be checked before the data is “pasted” into the form. Auditing data in Excel Page 11 worksheets
- 19. Auditing data on Excel worksheets Use the toolbar “copy” icon 3. Use the menu “Edit|Copy” 3.2.2 Selecting the columns for analysis Once the data to be analyzed has been copied to the clipboard, it can then be “pasted” onto the Audit Commander form. If the first row of the header contains column names, then the checkbox just below the “Paste” button must be checked. When the data is pasted onto the Auditing data on Excel worksheets Page 12
- 20. Auditing data on Excel worksheets form, the column names will be placed into the drop down list so that the column to be analyzed can be selected. If the area copied does not contain column names, then leave the check box unchecked, and the system will assign column names “Col001”, “Col002” and so on. Once the data has been pasted onto the form, the name of the first column is shown, and any other column can be selected from the drop down list. For this test, the second column, named “Cost” will be selected. The test to be performed will be to identify the three largest values. So the command “Largest values” is selected from the command drop down list. If the column name is blanked out, then all the data pasted will be processed in accordance with the information below: Auditing data in Excel Page 13 worksheets
- 21. Auditing data on Excel worksheets The option to process the entire area pasted is available only for those functions which normally process only a single column of data (list is in the table below). Depending upon the function selected, only numeric data, date data or all data will be processed. The type of data processed is shown in the table below. Command Description Type of data processed Numeric functions Benford’s law Numeric only Population statistics Numeric only Histogram Numeric only BoxPlot Numeric only TopN Numeric only BottomN Numeric only Stratify Numeric only Gaps Numeric only Date Functions Weekday Report Date only Weekday Extract Date only Holiday Report Date only Holiday extract Date only Date Near Date only Date Range Date Only Other Functions Fuzzy match – Levenshtein distance (All) Fuzzy match – regular expression (All) 3.2.3 Select chart colors For commands which produce a chart, the chart title and chart colors can be specified using the “Chart” tab. Although all commands will produce a text file report, only certain commands will also prepare a chart. Both the title of the chart and the color scheme used can be specified. The color scheme can be specified in three formats: 1. “pre-set” scheme selected from the drop down list, e.g. “fall” 2. A range of colors between two specified values, e.g. brown – light tan (Note that a dash separates the color names) 3. A range of colors specified for a numbered color group, e.g. turquoise 1 – 4. This is equivalent to the specification turquoise 1 – turquoise 4, but shorter to type. Note that only certain color names have color groups. Auditing data on Excel worksheets Page 14
- 22. Auditing data on Excel worksheets A complete list of color names accepted by the system and how they appear can be seen. Examples of color ranges and how they appear can be seen – examples show a histogram and use a chart title which specifies the color names used in the range. Two documents showing examples are provided, both are predominantly harmonious color schemes. The first shows color ranges for colors in a tight range (conservative). This is a PDF document of 251 pages and is 8.4 MB in size. The second range of colors are less conservative, but still harmonious, and are shown on a PDF document of 226 pages which has a size of 7.6 MB. The case for chart colors can be either upper or lower case. Spaces are ignored. Thus the following three specifications are equivalent: • Turquoise 2 • TURQUOISe2 • Tur quoise 2 3.2.4 Select the command to be processed The next step is to select the command to be processed from the command menu. The commands are organized by function type. Auditing data in Excel Page 15 worksheets
- 23. Auditing data on Excel worksheets Once the command has been selected, a help message is displayed on the status bar indicating what additional information is needed. If no additional information is needed, the status bar will read “(No additional info)” and the info text box will not be displayed. However, if additional information is required, the help message will be displayed on the status bar and the “Info” box will be displayed. The resulting form is as follows: The form now displays a fourth line called “Other info” and also displays an abbreviated help message on the status bar: “number of values, e.g. 10”. The help message indicates that the Other info is required and consists of a single value and the default value is “10”. In order words, for the largest value test, the largest 10 items will be selected. In this case, we want only the largest three values, so the number 3 is then typed into the “Other info” box. Auditing data on Excel worksheets Page 16
- 24. Auditing data on Excel worksheets Since all the needed information has been entered, the “Run” button can be clicked in order to perform the analysis. After clicking the “Run” button, there will be a pause while the system processes the information. Once processing is complete, the location of the output file will be shown on the status bar. If a chart was also produced, it will have the same name as the output text report file, but with a suffix of “.png”. An example of the form appears as follows: As shown on the status bar, the report has been written to the file named “c:testtempstep-2.txt” in the directory requested. The initial portion of the report (up to a maximum of 2,000 characters), can also be viewed by clicking on the tab labeled “Report”. Auditing data in Excel Page 17 worksheets
- 25. Auditing data on Excel worksheets The report lists the three lowest valued cost items in the range selected. Remaining information about these items can be viewed by scrolling the view to the right. Note that the report has also been stored in the report file specified. At this point there are several options: • Return to the “Clipboard” form and select another command to be processed, e.g. Benford’s Law test” • Return to the “Clipboard” form and select another column to be processed, e.g. “AD” (accumulated depreciation) • Return to the “Clipboard” form and “paste” another worksheet area for processing • Switch to any of the other tabs for additional processing. Go to a blank area in the current (or other) worksheet and “paste” the report results into that worksheet. Note: When a command is run, the results of that command can also be pasted to the clipboard by clicking on the “Copy” button, making it easy to do further processing or analysis by pasting this information on a worksheet. Auditing data on Excel worksheets Page 18
- 26. Auditing data on Excel worksheets Results are written to both a text file and a chart. In the example shown, the report was written to the text file “c:testtempstep-8.txt” and a chart was produced and stored with almost the same name, i.e. “c:testtempstep-8.png”. The results were stored in the directory “c:testtemp” because that folder was specified as the Audit folder in this instance (can be changed using the “Audit” form). Auditing data in Excel Page 19 worksheets
- 27. Auditing data on Excel worksheets For the population statistics command, the counts for positive, negative and zero amounts are shown, along with the totals. Note: The default color for the chart is blue and can be overridden using the values under the “Chart” tab. 3.2.5 Specifying selection criteria Auditing data on Excel worksheets Page 20
- 28. Auditing data on Excel worksheets Clicking on the label named “Where?” causes the selection criteria help form above to be shown. This form is useful in reminding you of the syntax for various types of selection that can be performed. Of the templates shown, an example can be selected from the drop down list, then modified and then copied over to the main processing form. 3.2.6 The logging facility A complete record of the processing performed can be recorded automatically in a log file. The log file records the processing performed in “macro” format so that it can be re-performed at a future date or shared with others. To perform logging, only two actions are needed: Specify the name of the log file to be used (only required is a different logfile is used from prior times) For the processing performed, check the box on the form to indicate that logging is desired. This check box can be turned on and off at will. When turned off, no logging is recorded until the check box is turned back on. The primary advantages of logging are: 1. Maintain a complete record of the processing performed 2. Record processing instructions so that the actions can be re-performed, now or in the future 3. Share processing information with others 4. Document the work performed Auditing data in Excel Page 21 worksheets
- 29. Auditing data on Excel worksheets The primary disadvantage of logging is: • Takes a minor amount of disk space and CPU cycle time Logging information is specified using the “Audit” form as shown below. Auditing data on Excel worksheets Page 22
- 30. Auditing data in Excel workbooks 4 Audit Commands Types of queries There are some 40+ queries or audit commands which can be selected for processing. These commands are grouped into five classes based upon the type of function performed – 1) numeric, 2) date, 3) other, 4) patterns and 5) sampling. For each command, a brief explanation of the purpose and use of the command is provided, an explanation of the meaning of any “other information” which must be provided. For each command, there are further examples and example output contained on the CD which is distributed with the software. Auditing data on Excel worksheets Page 1
- 31. Audit Commands 4.1 Numeric 4.1.1 Population Statistics Population Statistics Overview / Use in Audit Procedures The population statistics command is the “work horse” of the system and can be used alone to provide information for many audit steps. Just a few examples include: • Obtaining control totals • Preparing a population distribution for sample or audit planning • Identifying counts and amounts of possible exceptions • Quantifying the number and amount of records meeting various conditions • Identifying counts and amounts of transactions within date ranges The population statistics command produces three text reports and one graphic: 1. Basic statistics 2. Histogram data 3. Percentile report Basic statistics include information such as counts, totals, minimum and maximum values, etc. This information alone can be used to perform certain audit steps such as agreeing transaction supporting details to ledger amounts, testing for procedural compliance, etc. In the example below, a histogram chart and histogram data is to be prepared for fixed asset costs. The purpose of the procedure is to obtain an overview of the fixed assets cost information, identify potential errors or extreme values and provide information for audit planning. The statistics command can be used for a variety of purposes, including: • Obtaining counts of transactions meeting a condition or criteria Auditing data on Excel worksheets Page 2
- 32. Audit Commands • Obtaining transaction totals • Obtaining univariate statistics for the reasonableness tests, sample planning, etc. • Obtaining histogram information • Obtaining percentile information Usage Example 1 In a test of fixed assets, determine the count and amount of fixed assets which have been over depreciated. Approach – using the “population statistics” command, obtain totals and counts where the asset cost less accumulated depreciation is less than salvage. Audit Command values Column value – Cost Text Box – (empty) Where – (cost – ad) < salvage Results Counts, totals, minimum, maximum, etc. for all assets which have been over depreciated. Usage Example 2 For the purposes of sample planning, determine the distribution of values for fixed asset costs in order to be able to plan strata to use for stratified sampling. Approach – using the “population statistics” command, obtain a histogram of fixed asset costs. Audit Command values Column value – Cost Text Box – (empty) Where – (empty) The command shown below produces three reports for cost totals for location ‘ABC’. This is a very basic example of the command. It is possible to specify considerably more complex selection criteria. In addition, it is possible to prepare statistics for certain calculated amounts that are not contained in the file or the worksheet. An example might be statistics for net book value measured by “cost – ad” (cost less accumulated depreciation. Auditing data in Excel Page 3 worksheets
- 33. Audit Commands Output results Population Statistics Auditing data on Excel worksheets Page 4
- 34. Audit Commands Output results (pasted into Excel work sheet) The results above were “copied” from the form and then “pasted” into a worksheet. An alternative would be to import the report as a text file into Excel. Output results Auditing data in Excel Page 5 worksheets
- 35. Audit Commands Histograms Output results (chart) The chart below was specified using a custom color scheme and the title shown. These values are provided using the “Chart” tab on the processing form. Output results - chart Auditing data on Excel worksheets Page 6
- 36. Audit Commands 4.1.2 Round Numbers Round numbers Overview / Use in Audit Procedures Round numbers are often an indicator of estimates, which may be appropriate in certain cases (e.g. journal entries), but not appropriate in others (e.g. purchase orders, invoices, expense reports, etc.). The system can be used to identify the extent (if any) to which round numbers are being used as well as extract data based upon types of round numbers. The system defines a round number as one which is a whole number (i.e. no pennies), and contains one or more zeros immediately to the left of the decimal point, without any intervening digits other than zero. The number of such zeros determines the “order” of the round number. The chart below indicates examples of various round numbers, as well as their “order”. If a number is not round, then it will be classified as “NR” (not round). Example Order 15,000.00 3 10 1 123.19 NR 1,000,000.00 6 20.19 NR Examples of tests which can be performed are provided below: In a test of purchase orders, determine the frequency of round numbers for purchase orders. There is an allegation relating to purchases at store number ‘123’. Approach – using the “round numbers” command, obtain frequencies for round numbers on purchase orders, classified as to type of round number. Audit Command values Column value – Purchase order amount Text Box – (empty) Where – [store number] = 123 Results Frequencies of round numbers used on purchase orders for store number 123. Usage Example 2 In a test of journal entries, determine the frequency and extent of round numbers in journal entries for transactions relating to expenses. Expense account numbers begin with the number 3 for this company . Approach – using the “round numbers” command, obtain a frequency count. Audit Command values Auditing data in Excel Page 7 worksheets
- 37. Audit Commands Column value – Amount Text Box – (empty) Where – [account number] like ‘3%’ Results A report classifying the usage of round numbers for account numbers beginning with ‘3’ The example form below is being used to prepare a round number report for the data column named “Cost”. Auditing data on Excel worksheets Page 8
- 38. Audit Commands Output results Round numbers Output results (pasted into Excel work sheet) Round Number report: d-stat: .003704 Digits Count Pct Not Round 3,660 90.37% 1 354 8.74% 2 34 0.84% 3 2 0.05% Totals 4,050 100.00% The report indicates that just a little under 10% of the numbers are round. The largest order of round numbers is 3 (and there are two such numbers). The “d-stat” value of “.003704 is a measure of the difference between the expected number of round numbers and the actual number found. The d-stat value ranges from a low of zero (indicating conformity with that expected) to a high of one (indicating a significant difference between observed and expected). Output results Auditing data in Excel Page 9 worksheets
- 39. Audit Commands Round numbers Output results (chart) The chart below was specified using a custom color scheme and the title shown. These values are provided using the “Chart” tab on the processing form. Output results - chart Auditing data on Excel worksheets Page 10
- 40. Audit Commands 4.1.3 Benford’s Law Benford’s Law The Benford’s Law command is generally used as part of a fraud or other forensic investigation. The purpose will be to determine if numeric values on a schedule conform with that which is expected using Benford’s Law. The test should only be applied to numeric values which would be expected to adhere to that expected using Benford’s Law. More information is available about Benford’s law and its use. There are six types of tests which can be performed for Benford’s Law: Tests using Benford’s law must specify the type of test being performed: F1 – Test of the first digit F2 – Test of the first two digits F3 – Test of the first three digits D2 – Test of the second digit only L1 – Test of the last digit L2 – test of the last two digits Usage Example 1 In a test of physical inventory counts, determine if some of the counts may have been made up. It is expected that actual inventory counts would follow Benford’s law, i.e. a frequency distribution of inventory counts would align with that expected using Benford’s law. There is an allegation relating to counts at warehouse 5713. Approach – using the “benford” command, obtain frequencies for physical inventory counts and compare those with that expected using benford’s law Audit Command values Column value – Inventory count Text Box – F1 Where – [warehouse] = 5713 Results Frequencies of first digits of inventory counts, along with a chart and analysis comparing the results with that expected using benford’s law. Usage Example 2 In a test of accounts payable, determine if particular vendor invoices have leading digit frequencies as Auditing data in Excel Page 11 worksheets
- 41. Audit Commands would be expected using benford’s law. The vendors in question all have vendor numbers starting with the letters “R” – “V”. Approach – using the “benford” command, obtain a frequency count. Audit Command values Column value – [Invoice Amount] Text Box – F1 Where – [Vendor number] like ‘[R-V]% In the example below, the auditor is testing whether the first digits of the column named cost adhere with that expected using benford’s Law. Output results Benford’s Law Auditing data on Excel worksheets Page 12
- 42. Audit Commands Output results (pasted into Excel work sheet) Benford Report High digit 3 Chisq 730.89 p-value 0 df 8 D-stat 0.2641 Digit Observed Expected 1 473 1,219 2 432 713 3 464 506 4 463 392 5 435 321 6 419 271 7 454 235 8 456 207 9 454 185 The output results include both the expected and observed vales. Both a chi squared value and a d-stat are provided to measure the difference and assess it. Here the large chi squared value indicates that the data values do not conform with that expected using Benford’s law. Visually, this can be confirmed based upon the chart which is also produced and shown below. Output results Auditing data in Excel Page 13 worksheets
- 43. Audit Commands Benford’s Law Output results (chart) The chart below was specified using a custom color scheme and the title shown. These values are provided using the “Chart” tab on the processing form. The chart indicates that the data distribution is fairly uniform (shown in the light tan) and differs significantly from that which would be expected using Benford’s Law (shown in darker tan). The Chi Square value is shown on the chart. Note that different chart colors and titles may be specified under the “Chart” tab on the form. Output results - chart Auditing data on Excel worksheets Page 14
- 44. Audit Commands 4.1.4 Stratify Data stratification The data stratification procedure classifies numeric amounts into “buckets” or value ranges specified by the auditor. The purpose is to classify numeric amounts in order to determine the most frequently occurring values, largest and smallest values, etc. Stratification is often used for sample planning (stratified sampling, reasonableness tests) as well as audit planning in general. The values to be used for the strata (specified in ascending order and separated by commas or spaces). An example strata specification is “- 1000, -500, 0 300, 2000, 4000, 6000”. Note that the strata values do not need to be evenly spaced. If any values are found outside the end ranges of the strata specified, those values are reported separately. Warning: If strata values are not numeric, or not in ascending order, invalid results may be obtained. Do not include commas within a single value – e.g. specify 1000 NOT 1,000 Usage Example 1 In a test of accounts payable, classify the invoice amounts into particular ranges for the purpose of audit planning. Invoices less than $100 do not require a secondary authorization. Invoices over $50,000 requires three authorizations. All invoices over $2,500 require a purchase order. Approach – using the “stratify” command, obtain frequencies and totals for invoices classified into various numeric ranges. Audit Command values Column value – Inventory amount Text Box – -5000 -500 0 100 500 2500 30000 50000 100000 Where – (empty) Results The invoice amounts for each range specified are totaled and counted. Invoices for less than - $5,000 or ore than $100,000 (the extreme values) are tallied separately. Usage Example 2 Auditing data in Excel Page 15 worksheets
- 45. Audit Commands In a test of accounts payable, stratify the amounts of invoices for sample planning. One objective of the analysis is to classify the amounts such that 80% of the value can be tested with one procedure and the remaining 20% with another audit procedure. Only invoices at location ABC are to be classified. Approach – using the “stratify” command, obtain a data stratification. Audit Command values Column value – [Invoice Amount] Text Box – 0 500 20000 50000 100000 Where – location = ‘ABC’ Results A report classifying the invoice amounts at location ‘ABC’ into the ranges specified. The results also include a chart. Data stratification Auditing data on Excel worksheets Page 16
- 46. Audit Commands Output results (pasted into Excel work sheet) Summary for Strata -100 0 100 200 500 1000 5000 7000 9000 12000 Start End Count Amount Pct Cumulative Below Below 0 0 0 0 -100 0 0 0 0 0 0 100 31 1,440.00 0.0001 0.0001 100 200 47 7,345.99 0.0004 0.0004 200 500 108 39,520.48 0.0019 0.0024 500 1000 190 143,419.53 0.007 0.0094 1000 5000 1,665 5,017,302.18 0.2465 0.2559 5000 7000 772 4,624,456.00 0.2272 0.4831 7000 9000 826 6,616,229.14 0.3251 0.8082 9000 12000 411 3,903,915.96 0.1918 1 Above Above 0 0 0 1 Totals totals 4,050 20,353,629.28 Output results Auditing data in Excel Page 17 worksheets
- 47. Audit Commands Data stratification Output results (chart) The chart below was specified using a custom color scheme and the title shown. These values are provided using the “Chart” tab on the processing form. Output results - chart Auditing data on Excel worksheets Page 18
- 48. Audit Commands 4.1.5 Summarization Data summarization The summarization function obtains not only totals by each control break (sort key) specified, but also other information such as minimum and maximum values, averages and standard deviation. There is no limit as to the number of columns which make up the control break. A control break (sort key) may consist of a single column, e.g. sub-totals by vendor would be specified as just a single column name – “vendor”. If subtotals were needed by region by vendor, then the control break specification would be “region, vendor”. Note: The information being summarized does not need to be “pre-sorted”. Usage Example 1 The auditor wishes to summarize sales by region and store in order to identify both the totals, as well as the ranges of values at these stores, i.e. largest single amount and smallest single amount. Approach – using the “summary” command, obtain totals, counts, minima, maxima, standard deviation, average. Audit Command values Column value – Sales amount Text Box – region, store Where – (empty) Results The summarized amount by store by region is produced, showing also the averages, minima, maxima, standard deviation, etc. Usage Example 2 Expense report information is available and includes employee number, region, expense type and expense date. The auditor wishes to summarize expense report costs , by region and employee number for the month of June, for travel expenses only (i.e. expense type = “travel”). Approach – using the “summary” command, obtain a data summarization. Audit Command values Auditing data in Excel Page 19 worksheets
- 49. Audit Commands Column value – [Expense Amount] Text Box – Region, [employee number] Where – [expense type] = ‘travel’ and month([expense date]) = 6 Results A report summarizing all travel amounts for the month of June, by region and employee. In addition to summaries, counts, minima, maxima, averages and standard deviations are shown. A simpler example is shown in the example below – summarize cost by location and life. All rows are to be summarized. Output results Data summarization Auditing data on Excel worksheets Page 20
- 50. Audit Commands Output results (pasted into Excel work sheet – not all is shown) Stand- Minim- ard De- location life Total Average um Maximum Count viation AB 1 1 1 1 1 1 1 AB 2 2 2 2 2 1 1 AB 13 13 13 13 13 1 1 ABC 3 648 3 3 3 216 0 ABC 4 992 4 4 4 248 0 1,285.0 ABC 5 0 5 5 5 257 0 1,572.0 ABC 6 0 6 6 6 262 0 1,722.0 ABC 7 0 7 7 7 246 0 2,088.0 ABC 8 0 8 8 8 261 0 2,115.0 ABC 9 0 9 9 9 235 0 2,160.0 ABC 10 0 10 10 10 216 0 2,497.0 ABC 11 0 11 11 11 227 0 3,132.0 ABC 12 0 12 12 12 261 0 CDS 3 45 3 3 3 15 0 CDS 4 60 4 4 4 15 0 CDS 5 80 5 5 5 16 0 CDS 6 108 6 6 6 18 0 CDS 7 105 7 7 7 15 0 CDS 8 96 8 8 8 12 0 CDS 9 162 9 9 9 18 0 CDS 10 170 10 10 10 17 0 Output results Auditing data in Excel Page 21 worksheets
- 51. Audit Commands 4.1.6 Top and Bottom 10 Top and Bottom 10 (Extreme values) The Top and Bottom 10 commands are used to identify the largest (or smallest) numeric, date or text values from a population (and criteria can be applied). The number of items to be identified can be specified as any value. Generally the command is used to identify extremes among the following types of data: • For numeric values, identify unusually large (or small) items, possible outliers or to focus on just the most significant dollar items. • For date values, identify the latest (or earliest) dates in order to identify date ranges, transactions outside the cutoff date, etc. • For text values, identify high (or low) values of text as would be shown had the data been sorted. Note that the data being analyzed does not need to be presorted. Analysis of subsets of the data can be readily performed. For example, the auditor may wish to know the smallest fixed asset costs for those assets with a useful life of seven years or more and located within one or more regions or states. Other types of criteria can also be applied, depending upon what the analyst wishes to accomplish. Usage Example 1 For purposes of audit testing, the 10 fixed assets with the largest cost need to be identified, but only for assets located in either Florida, Alabama or Georgia. Approach – using the “topn” command, list the details pertaining to the ten asset records having the largest cost. Note that the input data does not need to be pre-sorted. Audit Command values Column value – asset cost Text Box – 10 Where – location in(‘FL’,’GA’,’AL’) Results A list of the fixed asset records for the ten assets having the greatest cost in any of the three states specified. Auditing data on Excel worksheets Page 22
- 52. Audit Commands Usage Example 2 Identify the first five assets which have a net negative book value Approach – using the “bottomn” command, list the details pertaining to the ten asset records having the least net book value. This will include any which have a negative net book value. Note that the input data does not need to be pre-sorted. Audit Command values Column value – [asset cost] – [accumulated depreciation] Text Box – 5 Where – (empty) Results A list of the fixed asset records for the 5 assets having the smallest net book value (which will include negative values if there are any). In the example below, the auditor wishes to identify the ten asset records which have the largest cost amounts. Output results Auditing data in Excel Page 23 worksheets
- 53. Audit Commands Top and Bottom 10 (Extreme values) Output results (pasted into Excel work sheet) – first ten rows in descending order (not all columns shown) Cost TagNo AD Replace Bookval Salvage Depr Life Location 9997 2665 4019.164 2999 5977.84 1999 803.8328 4 DFS 9995 9747 4065.581 2998 5929.42 1999 813.1162 12 ABC 9994.99 2204 4070.435 2998 5924.56 1999 814.0869 10 ABC 9994 9091 4033.723 2998 5960.28 1999 806.7445 12 ABC 9994 3619 4052.277 2998 5941.72 1999 810.4555 9 DFS 9991 5778 4055.282 2997 5935.72 1998 811.0564 7 GSE 9990 5461 4019.03 2997 5970.97 1998 803.806 7 ABC 9988 879 4046.362 2996 5941.64 1998 809.2724 6 XZS 9977 2054 4014.101 2993 5962.9 1995 802.8203 4 ABC 9975 6887 4015.735 2992 5959.27 1995 803.147 12 ABC The records with the largest ten asset costs are shown, listed in descending order. Note that if the data pasted did not have column headers, then the largest values would shown in the leftmost column. For example, if an area of six columns (with no column headers) were pasted and column three (“Col003”) were selected, then the results would be shown with Column3 as the first column, followed by Column 1, 2, 4, 5 and 6. Output results Auditing data on Excel worksheets Page 24
- 54. Audit Commands 4.1.7 Histograms Histograms Histograms provide a visual representation for the values or transactions being analyzed. The results are identical to that of the population statistics, and boxplot commands, except that a different chart is produced. Three reports are produced: 1. Basic statistics 2. Histogram data 3. Percentile report Basic statistics include information such as counts, totals, minimum and maximum values, etc. This information alone can be used to perform certain audit steps such as agreeing transaction supporting details to ledger amounts, testing for procedural compliance, etc. Examples of basic statistics reports can be found in the work papers referenced below: Usage Example 1 For purposes of audit testing, prepare a histogram of employee expense report amounts. Approach – using the “histo” command, prepare a chart and detail report as to expense report amounts at region XYZ. Audit Command values Column value – [expense report amount] Text Box – (empty) Where – region = ‘XYZ’ Results A histogram chart of expense report amounts at region XYZ, along with a text report containing the numeric values. Usage Example 2 For purposes of testing inventory values, prepare a histogram of inventory unit cost amounts. Auditing data in Excel Page 25 worksheets
- 55. Audit Commands Approach – using the “histo” command, prepare a chart and detail report as to inventory unit cost amounts. Audit Command values Column value – [inventory cost] Text Box – (empty) Where – (empty) Results A histogram chart of unit inventory costs, along with a text report containing the numeric values. Where – (empty) Results The invoice amounts for each range specified are totaled and counted. Invoices for less than - $5,000 or ore than $100,000 (the extreme values) are tallied separately. The example below shows a histogram of cost values is to be prepared. Output results Histograms Auditing data on Excel worksheets Page 26
- 56. Audit Commands Output results (pasted into Excel work sheet) Histogram Report Bin Start End Count Amount 1 1 834 146 29,783.99 2 834 1,667.00 332 276,601.51 3 1,667.00 2,500.00 352 586,450.00 4 2,500.00 3,333.00 329 826,848.02 5 3,333.00 4,166.00 357 1,188,139.13 6 4,166.00 4,999.00 337 1,399,458.47 7 4,999.00 5,832.00 355 1,773,214.05 8 5,832.00 6,665.00 325 1,895,888.28 9 6,665.00 7,498.00 318 2,124,745.17 10 7,498.00 8,331.00 335 2,517,380.31 11 8,331.00 9,164.00 348 2,899,833.39 12 9,164.00 9,997.00 516 4,835,286.96 Totals: 4,050 20,353,629.28 The data for the histogram includes both counts and amounts. The counts are plotted on the chart which is prepared. Output results Auditing data in Excel Page 27 worksheets
- 57. Audit Commands Histograms Output results (chart) The chart below was specified using a custom color scheme and the title shown. These values are provided using the “Chart” tab on the processing form. This chart indicates that the most common values are those between 9,164 and 9,997. The fewest counts are between the values of 1 and 834. Output results - chart Auditing data on Excel worksheets Page 28
- 58. Audit Commands 4.1.8 Box Plot Box Plot The Box Plot command is used to separate a population of numeric values into quartiles in order to see the values and to also envision how the population is distributed. This provides a little more information than just the minimum, maximum and median. Except for the chart, the command is identical to the Population statistics and the histogram command. Usage Example 1 As part of an audit of accounts payable, the range of invoice costs needs to be determined. Approach – using the “boxplot” command, prepare a chart and detail report as to invoice costs for invoices dated after 6/30/2008. Audit Command values Column value – [invoice amount] Text Box – (empty) Where – [invoice date] > #6/30/2008# Results A box plot chart of invoice amounts for invoices dated after 6/30/2008, along with a text report containing the numeric values. Usage Example 2 Daily sales ranges needs to be determined for a particular store. Approach – using the “boxplot” command, prepare a chart and detail report as to daily sales ranges at store ABC. Audit Command values Column value – [sales total] Text Box – (empty) Where – [store number] = ‘ABC’ Results A box plot chart of daily sales ranges, along with a text report containing the numeric values. The example below will prepare a box plot of cost values for all transactions. This plot could have been narrowed down by specifying the “Where” information. Auditing data in Excel Page 29 worksheets
- 59. Audit Commands Output results Box Plot Auditing data on Excel worksheets Page 30
- 60. Audit Commands Output results (pasted into Excel work sheet) Percentiles: P 1.0% : 125 P 5.0% : 542 P 10.0% : 1,064.99 P 25.0% : 2,579.00 P 50.0% : 4,960.00 P 75.0% : 7,559.00 P 90.0% : 9,027.00 P 95.0% : 9,503.00 P 99.0% : 9,902.00 Inter quartile range: 4,980.00 The values above are a portion of the data as it appears when pasted into Excel. This report is the same as that for the population statistics and the histogram commands. Output results Auditing data in Excel Page 31 worksheets
- 61. Audit Commands Box Plot Output results (chart) The chart below was specified using a custom color scheme and the title shown. These values are provided using the “Chart” tab on the processing form. Output results - chart Auditing data on Excel worksheets Page 32
- 62. Audit Commands 4.1.9 Random numbers Random numbers are commonly required as part of the sampling process. Excel has a built in function for the generation of random numbers, “=RAND()”. The Excel RAND function generates pseudo random numbers evenly distributed between 0 and 1. For many purposes, the pseudo random number generated using the RAND function may be adequate. Microsoft documentation at http://support.microsoft.com/support/kb/articles/q86/5/23.asp (knowledge base article Q86523 ) describes the process used. The starting number is determined based upon the time of day. The RAND function is just one of a number of random number generators (RNG). The quality of a random number generator can be tested using the “DieHard” test suite developed by the National Institute of Standards (NIST). More information is available at http://csrc.nist.gov/groups/ST/toolkit/rng/batteries_stats_test.html. One of the free random number generators is called the Mersenne Twister. The following description is provided from Wikipedia on the Mersenne Twister “The Mersenne twister is a pseudorandom number generator developed in 1997 by Makoto Matsumoto (松本 眞?) and Takuji Nishimura (西村 拓士?)[1] that is based on a matrix linear recurrence over a finite binary field F2. It provides for fast generation of very high-quality pseudorandom numbers, having been de- signed specifically to rectify many of the flaws found in older algorithms. Its name derives from the fact that period length is chosen to be a Mersenne prime. The commonly used variant of Mersenne Twister, MT19937 has the following desirable properties: 1. It was designed to have a period of 219937 − 1 (the creators of the algorithm proved this property). In practice, there is little reason to use a larger period, as most ap- plications do not require 219937 unique combinations (219937 is approximately 4.3 × 106001; Auditing data in Excel Page 33 worksheets
- 63. Audit Commands this is many orders of magnitude larger than the estimated number of particles in the ob- servable universe, which is 1087). 2. It has a very high order of dimensional equidistribution (see linear congruential generator). This implies that there is negligible serial correlation between successive val- ues in the output sequence. 3. It passes numerous tests for statistical randomness, including the Diehard tests. It passes most, but not all, of the even more stringent TestU01 Crush randomness tests. The Mersenne Twister algorithm has received some criticism in the computer science field, notably by George Marsaglia. These critics claim that while it is good at generating random numbers, it is not very elegant and is overly complex to implement.” Generation of random numbers using Audit Commander is done using the “random” command. A seed value consisting of an integer value between 1 and 2,147,483,647 is used to determine the starting random number. The random numbers generated will consist of uniformly distributed numbers between zero and one. Usage Example 1 For purposes of sampling, generate and assign random numbers to each row of data contained on an Excel work sheet. The starting seed number to be used is 102935427. Command – “random” Column name – “N/A” TextBox – “102935427” Results – An additional column named “Random” is created with a value on the rightmost column between zero and 1. This is a pseudo random number generated using the Mersenne twister algorithm based upon the seed number provided. Random numbers Auditing data on Excel worksheets Page 34
- 64. Audit Commands The example command shown on the next page adds a random number value in the rightmost column. This random number will be between 0 and 1 (exclusive). The starting number is based upon the seed value provided (in this case 1738974 ). The seed value should be a whole number between 1 and approximately 2.1 billion. Random numbers Auditing data in Excel Page 35 worksheets
- 65. Audit Commands Output results (pasted into Excel work sheet – highlighting added for effect, not all columns shown) Life Location Acquisition Accode DispDate Random number 7 DEF 5/17/2008 7:40 A 0 0.974683138 8 DEF 12/19/2001 A 0 0.961858645 12 DEF 1/5/2008 11:31 A 0 0.209254051 3 DEF 10/12/2009 16:33 A 0 0.451545258 8 DEF 11/20/2008 11:16 A 0 0.362094671 10 DEF 1/31/2007 6:00 A 0 0.010547096 5 DEF 8/21/2010 21:21 A 0 0.784745319 4 DEF 3/14/2000 15:07 A 0 0.269402404 3 DEF 4/4/2001 8:38 A 0 0.417646239 3 DEF 7/31/2006 6:57 A 0 0.578761123 8 DEF 11/30/2008 9:07 A 0 0.590210739 9 DEF 1/21/2004 8:09 A 0 0.690726882 7 DEF 7/29/2010 23:31 A 0 0.902005128 8 DEF 8/12/2000 19:12 A 0 0.361275228 7 DEF 7/23/2002 9:07 A 0 0.456829664 8 DEF 5/8/2001 9:07 A 0 0.503349514 8 DEF 4/13/2010 15:36 A 0 0.119554142 9 DEF 9/9/2010 15:07 I 0 0.602501919 7 DEF 12/16/2003 6:57 A 0 0.820769995 7 DEF 6/22/2006 18:28 A 0 0.944822744 Output results Auditing data on Excel worksheets Page 36
- 66. Audit Commands 4.2 Date 4.2.1 Holiday Extract Holiday Extract Often it is desirable to check if any transaction dates fall on a federal holiday such as the Independence Day, etc. Although it may be possible to visually check for these dates, it becomes more complicated when the date falls on a weekend and is therefore celebrated on the preceding Friday (or the following Monday). This function can analyze all the dates within a specified range and quantify the number that fall on each of the holiday dates. There are two functions related to holidays. One prepares a summary of counts of holiday dates and the other extracts transactions whose dates fall on federal holidays. Usage Example 1 In a test of general ledger, an extract of all journal postings on a federal holiday needs to be obtained. Approach – using the “holiday” command, extract a list of all journal entries posted on holidays. The date format being used is month – day – year (mdy). Audit Command values Column value – [journal posting date] Text Box – mdy Where – (empty) Results A list of any journal entry transactions which have been posted on a date which is a federal holiday. In addition, a summary chart of holiday transactions is prepared. Usage Example 2 Determine if any receiving reports exist for dates falling on a federal holiday. Date format is mdy. Approach – using the “holiday” command, extract a list of receiving transactions falling on a federal holiday. Audit Command values Auditing data in Excel Page 37 worksheets
- 67. Audit Commands Column value – [receiving report date] Text Box – mdy Where – (empty) Results A list of any receiving report transactions which occurred on a federal holiday. In addition, a summary chart of holiday transactions is prepared. Date format – “mdy” for mm/dd/yyyy or “dmy” – dd/mm/yyyy Country code – “US” or “CA”. Note: The default values: US and mdy will be used if no values are specified. The command example below checks for any records which have an acquisition date falling on a federal holiday in the United States. Output results Holiday Extract Auditing data on Excel worksheets Page 38
- 68. Audit Commands Output results (pasted into Excel work sheet – not all rows and columns are shown, highlighting added for emphasis) AcqDate TagNo Cost AD Replace Bookval Salvage Depr 11/24/2005 1939 6199 2539.986 1860 3659.01 1240 507.9973 1/17/2005 4982 8649 3488.15 2595 5160.85 1730 697.63 1/17/2005 4759 8649 3488.15 2595 5160.85 1730 697.63 5/28/2007 3740 4993 2040.753 1498 2952.25 999 408.1506 7/4/2005 2392 9223 3728.142 2767 5494.86 1845 745.6284 1/2/2006 3543 4267 1726.003 1280 2541 853 345.2006 10/9/2006 2344 7175 2929.244 2152 4245.76 1435 585.8487 1/2/2006 4754 9473 8400 2842 1073 1895 1680 11/24/2005 4887 9867 4009.78 2960 5857.22 1973 801.956 2/19/2007 2035 1615 654.74 484 960.26 323 130.948 11/10/2006 4215 3776 1521.438 1133 2254.56 755 304.2876 10/10/2005 3475 9503 3845.354 2851 5657.65 1901 769.0709 1/1/2007 3166 7941 3240.535 2382 4700.46 1588 648.1071 11/11/2004 3197 2179 889.3601 654 1289.64 436 177.872 2/19/2007 1224 3424 1375.961 1027 2048.04 685 275.1921 12/31/2004 1353 3912 2920 1174 992 782 584 2/19/2007 4232 4544 1835.211 1363 2708.79 909 367.0423 2/20/2006 4194 3068 1251.079 920 1816.92 614 250.2158 12/31/2004 4107 1785 714.4909 536 1070.51 357 142.8982 12/25/2006 5243 1518 614.6649 455 903.34 304 122.933 9/4/2006 5193 6506 2652.665 1952 3853.33 1301 530.5331 Output results Auditing data in Excel Page 39 worksheets
- 69. Audit Commands Holiday Summary Output results (chart) The chart below was specified using a custom color scheme and the title shown. These values are provided using the “Chart” tab on the processing form. This chart indicates that the most frequent holiday for asset acquisitions was President’s Day (19 instances). Output results - chart Auditing data on Excel worksheets Page 40
- 70. Audit Commands 4.2.2 Week days Week days In many instances the auditor wishes to extract just certain data within Excel based upon days of the week. In this instance one column or row will contain dates which the auditor wishes to examine. Usage Example 1 In a test of certain expense, an extract is needed for expenses incurred on a Friday or Saturday. Approach – using the “wd” command, extract a list of all such transactions. The date format being used is month – day – year (mdy). Audit Command values Column value – [expense date] Text Box – Friday, saturday Where – (empty) Results A list of any expense transactions which fell on a Friday or Saturday are prepared. Usage Example 2 An audit test is to be performed to identify any travel expense transactions on Saturdays, which is not allowed at this company. Approach – using the “wd” command, extract a list of all such transactions. The date format being used is month – day – year (mdy). Audit Command values Column value – [expense date] Text Box –Saturday Where – [travel code] = ‘airline’ Results A list of any expense transactions which fell on a Saturday is prepared. The day of the week must include at least the first three letters of the week day name. case does not matter. Thus, Sunday could be specified using any of the following: “sun”, “Sunday”, “sund”, etc. The example below is used to extract all transactions which fall on either a Saturday or a Monday. Note that additional selection criteria could have been applied, e.g. store = ‘ABC’ to isolate the extract to just Auditing data in Excel Page 41 worksheets
- 71. Audit Commands those transactions at store ‘ABC’. Similarly a date range could have also been applied, e.g. acqdate between #7/1/2005# and #9/30/2005#. When specifying dates as part of the extract criteria, the date value must be enclosed in pound signs (‘#’). Output results Week days Auditing data on Excel worksheets Page 42
- 72. Audit Commands Output results (pasted into Excel work sheet – not all rows and columns are shown) AcqDate TagNo Cost AD Replace Bookval Salvage Depr Life 5/26/2007 2547 8258 3346.594 2477 4911.41 1652 669.3188 9 3/4/2006 1299 -3115 1253.43 934 1861.57 623 250.6859 12 3/6/2006 2881 2244 905.4028 673 1338.6 449 181.0806 8 3/17/2007 2791 3039 2431 912 608 608 761.4 12 12/19/2005 4163 3048 1223.804 914 1824.2 610 244.7607 4 4/8/2006 5205 1165 932 350 233 233 95.43749 8 7/10/2006 4219 2500 1022.871 750 1477.13 500 204.5741 3 6/24/2006 3112 1131 460.5792 339 670.42 226 92.11584 3 2/26/2005 1921 7527 3033.435 2258 4493.57 1505 606.6869 4 9/19/2005 4857 6106 2448.247 1832 3657.75 1221 489.6493 9 5/2/2005 2391 4339 1745.635 1302 2593.37 868 349.1269 8 7/17/2006 2205 7858 3195.106 2357 4662.89 1572 639.0212 5 1/20/2007 1639 7073 2870.923 2122 4202.08 1415 574.1847 6 4/16/2007 4964 2410 975.3022 723 1434.7 482 195.0604 7 6/19/2006 4185 6705 2715.957 2012 3989.04 1341 543.1915 4 9/18/2006 4673 7966 3233.326 2390 4732.67 1593 646.6653 3 11/6/2006 3363 6586 2658.405 1976 3927.6 1317 531.6809 3 1/17/2005 4982 8649 3488.15 2595 5160.85 1730 697.63 9 1/27/2007 1501 521 208.4521 156 312.55 104 41.69043 12 3/28/2005 3965 1775 715.3794 532 1059.62 355 143.0759 10 1/17/2005 4759 8649 3488.15 2595 5160.85 1730 697.63 9 1/27/2007 3743 521 208.4521 156 312.55 104 41.69043 12 3/28/2005 5045 1775 715.3794 532 1059.62 355 143.0759 10 11/22/2004 1870 2589 1060.414 777 1528.59 518 212.0829 6 12/5/2005 3391 795 322.078 238 472.92 159 64.4156 5 12/11/2006 5140 4897 1989.455 1469 2907.55 979 397.891 6 5/7/2005 2589 5555 2229.728 1666 3325.27 1111 445.9457 10 Output results Auditing data in Excel Page 43 worksheets
- 73. Audit Commands 4.2.3 Holiday summary Holiday Summary In certain instances it is desirable to extract just those transactions in a file which fall on a federal holiday. These transactions can then be reviewed separately. The holiday extract command can be used in conjunction with date ranges, location codes or any other criteria which should be applied as part of the extract. Usage Example 1 In a test of general ledger, an extract of all journal postings on a federal holiday needs to be obtained. Approach – using the “holiday” command, extract a list of all journal entries posted on holidays. The date format being used is month – day – year (mdy). Audit Command values Column value – [journal posting date] Text Box – mdy Where – (empty) Results A list of any journal entry transactions which have been posted on a date which is a federal holiday. In addition, a summary chart of holiday transactions is prepared. Usage Example 2 Determine if any receiving reports exist for dates falling on a federal holiday. Date format is mdy. Approach – using the “holiday” command, extract a list of receiving transactions falling on a federal holiday. Audit Command values Column value – [receiving report date] Text Box – mdy Where – (empty) Results Auditing data on Excel worksheets Page 44
- 74. Audit Commands A list of any receiving report transactions which occurred on a federal holiday. In addition, a summary chart of holiday transactions is prepared. Date format – “mdy” for mm/dd/yyyy or “dmy” – dd/mm/yyyy Country code – “US” or “CA”. Note: The default values: US and mdy will be used if nothing is specified. Output results Holiday Summary Output results (pasted into Excel work sheet) Holidays: New Year's 14 Martin Luther King 13 President's Day 19 Memorial Day 14 Independence Day 9 Labor Day 8 Columbus Day 7 Veterans Day 8 Thanksgiving 9 Christmas 16 Output results Auditing data in Excel Page 45 worksheets
- 75. Audit Commands Auditing data on Excel worksheets Page 46
- 76. Audit Commands Holiday Summary Output results (chart) The chart below was specified using a custom color scheme and the title shown. These values are provided using the “Chart” tab on the processing form. Output results - chart Auditing data in Excel Page 47 worksheets
- 77. Audit Commands 4.2.4 Ageing Ageing During a review of applications which use both dates and amounts, it is very common to "age" the data for various purposes - e.g. reasonableness testing, checking for stale or obsolete items, data classification, etc. The procedure to age data is straightforward: The date to be used for ageing “Ageing Date” The width of the ageing range, e.g. 30 days The name of the column with the date to be aged, e.g. “Due Date” The name of the column with the amount to be aged, e.g. “Balance Due” Usage Example 1 In a test of accounts receivable, an ageing of customer account balances is needed. Approach – using the “age” command, prepare an ageing report for customers in ABC region. Ageing is to be done as of June 30, 2008. Ageing width is 30 days. Audit Command values Column value – [invoice date] Text Box – invoice date, invoice amount, 6/30/2008, mdy Where – region = ‘ABC’ Results An ageing report is prepared for those customer in region ABC as of June 30, 2008. Usage Example 2 In a test of accounts payable, an ageing of vendor invoices is needed. Approach – using the “age” command, prepare an ageing report for vendor invoices. Ageing is to be done as of September 30, 2007. Ageing width is 30 days. Audit Command values Column value – [invoice date] Text Box – invoice date, invoice amount, 6/30/2007, mdy Where – (empty) Results An ageing report is prepared for vendor invoices as of September 30, 2007. Auditing data on Excel worksheets Page 48
- 78. Audit Commands Output results Ageing Auditing data in Excel Page 49 worksheets
- 79. Audit Commands Output results (pasted into Excel work sheet) Ageing Report as of 6/30/2005 Start End Amount 5/31/2005 6/29/2005 653,891.00 6/30/2005 7/29/2005 664,956.00 7/30/2005 8/28/2005 681,971.00 8/29/2005 9/27/2005 579,429.00 9/28/2005 10/27/2005 602,309.00 10/28/2005 11/26/2005 671,547.00 11/27/2005 12/26/2005 669,969.00 12/27/2005 1/25/2006 85,773.00 Totals 4,609,845.00 Output results Auditing data on Excel worksheets Page 50
- 80. Audit Commands Ageing Output results (chart) The chart below was specified using a custom color scheme and the title shown. These values are provided using the “Chart” tab on the processing form. Output results - chart Auditing data in Excel Page 51 worksheets
- 81. Audit Commands 4.2.5 Date Near Date Near Selection of a range of transactions based upon date value is a very common data extraction procedure. Examples include cut-off testing, re-testing balances for a specified period, etc. There are two equivalent procedures for doing such an extraction - 1. DateRange - the auditor specifies a starting and ending date, and 2. DateNear - the auditor specifies a date and the maximum number of days from the date (e.g. three days before or after July 4th) Usage Example 1 For cutoff testing, the auditor wants to identify any sales made within 5 days of June 30, 2008. Approach – using the “datenear” command, prepare a list of any such transactions. Audit Command values Column value – [sales date] Text Box – 6/30/2008, 5 Where – (empty) Results A list of any sales transactions within five days of June 30, 2008, i.e. June 25, 2008 – July 5, 2008. Usage Example 2 For accrual testing, the auditor wants to identify any accruals posted within 15 days of June 30, 2008. Only account numbers beginning with either a ‘2’ or a ‘3’ are to be selected. Approach – using the “datenear” command, prepare a list of any such transactions. Audit Command values Column value – [journal date] Text Box – 6/30/2008, 15 Where – [account number] like ‘[2-3]%’ Results A list of any accruals posted within 15 days for the account numbers specified. Auditing data on Excel worksheets Page 52
- 82. Audit Commands Note: The default values: US and mdy will be used if nothing is specified. The target date value, and The maximum number of days before or after this date Output results Date near Output results (pasted into Excel work sheet – doesn’t show all rows or columns) TagNo Cost AD Replace Bookval Salvage Depr Life Location Acquisition Accode 840 6032 2421.711 1810 3610.29 1206 484.3423 3 DEF 7/31/2006 6:57 A 4615 6166 2526.535 1850 3639.46 1233 505.307 8 ABC 8/2/2006 11:02 A 2145 6094 2475.97 1828 3618.03 1219 495.194 4 DFS 7/26/2006 0:43 A 1298 6144 2512.487 1843 3631.51 1229 502.4973 3 ABC 7/29/2006 12:14 A 108 6042 2430.326 1813 3611.67 1208 486.0651 8 ABC 7/30/2006 16:04 A 4426 6105 2475.607 1832 3629.39 1221 495.1214 7 ABC 8/4/2006 9:21 I Output results Auditing data in Excel Page 53 worksheets
- 83. Audit Commands 4.2.6 Date Range Date Range The date range test is the same as “date near”, except specific dates are provided. Usage Example 1 For cutoff testing, the auditor wants to identify any sales made between 6/25/2008 and 7/5/2008. Approach – using the “daterange” command, prepare a list of any such transactions. Audit Command values Column value – [sales date] Text Box – 6/25/2008, 7/5/2008 Where – (empty) Results A list of any sales transactions within the specified range, i.e. June 25, 2008 – July 5, 2008. Usage Example 2 For accrual testing, the auditor wants to identify any accruals posted within 15 days of June 30, 2008. Only account numbers beginning with either a ‘2’ or a ‘3’ are to be selected. Approach – using the “daterange” command, prepare a list of any such transactions. Audit Command values Column value – [journal date] Text Box – 6/15/2008, 7/14/2008 Where – [account number] like ‘[2-3]%’ Results A list of any accruals posted within 15 days for the account numbers specified. Auditing data on Excel worksheets Page 54
- 84. Audit Commands Output results Date range Output results (pasted into Excel work sheet – doesn’t include all columns) Acquisition TagNo Cost AD Replace Bookval Salvage Depr 7/31/2006 6:57 840 6032 2421.711 1810 3610.29 1206 484.3423 8/11/2006 21:07 4919 6103 2466.12 1831 3636.88 1221 493.224 8/2/2006 11:02 4615 6166 2526.535 1850 3639.46 1233 505.307 8/10/2006 5:16 4376 6040 2417.777 1812 3622.22 1208 483.5554 8/8/2006 3:50 2149 6073 2445.843 1822 3627.16 1215 489.1685 8/4/2006 9:21 4426 6105 2475.607 1832 3629.39 1221 495.1214 8/11/2006 21:21 7053 6158 2510.114 1847 3647.89 1232 502.0229 8/10/2006 9:50 9235 6113 2475.591 1834 3637.41 1223 495.1182 Output results Auditing data in Excel Page 55 worksheets
- 85. Audit Commands 4.2.7 Week days Report Week days report The week days report summarizes the count of transactions by day of week. This test may be used for reasonableness tests, audit planning, etc. The report consist of both text and a chart. Usage Example 1 In an audit of expense reports, the counts of expenses by day of week are needed. Approach – using the “wdreport” command, summarize such transactions. Audit Command values Column value – [expense report date] Text Box – mdy Where – (empty) Results A summary of counts of expense report transactions by day of week. Usage Example 2 In an audit of purchasing, the counts of purchase orders issued by day of week are needed. Approach – using the “wdreport” command, summarize such transactions. Audit Command values Column value – [purchase order date] Text Box – mdy Where – (empty) Results A summary of counts of purchase order transactions by day of week. Date format – “mdy” for mm/dd/yyyy or “dmy” – dd/mm/yyyy Country code – “US” or “CA”. Note: The default values: US and mdy will be used if nothing is specified. Auditing data on Excel worksheets Page 56
- 86. Audit Commands Output results Week days report Output results (pasted into Excel work sheet) Weekday analysis: Sunday: 539 Monday: 575 Tuesday: 514 Wednesday: 588 Thursday: 551 Friday: 583 Saturday: 536 Output results Auditing data in Excel Page 57 worksheets
- 87. Audit Commands Weekdays report Output results (chart) The chart below was specified using a custom color scheme and the title shown. These values are provided using the “Chart” tab on the processing form. The chart indicates that the most common day of the week for the transactions selected was Wednesday and the least frequent day of the week was Tuesday. Output results - chart Auditing data on Excel worksheets Page 58
- 88. Audit Commands 4.3 Other 4.3.1 Gaps in Sequences Numeric Sequence Gaps A prime indicator of missing documents is a "gap" in a numeric sequence, such as check numbers, purchase orders, sales invoices, petty cash slips, receiving reports, etc. The "gaps" command is used to check a range of data to determine if there are any "gaps" within a range of numbers. Usage Example 1 A check is to be made to determine if all asset tag numbers are accounted for. The purpose of the test id to determine if there are any “gaps” in the numbers assigned for fixed asset tags. No records are to be excluded. The name of the column for the fixed asset tag number is “Tagno”. The command box to perform this test would be as shown below. Usage Example 2 Auditing data in Excel Page 59 worksheets
- 89. Audit Commands In an audit of cash, the auditor wishes to determine of the schedule of checks paid is complete, i.e. are there any missing check numbers which have not been accounted for? The commands to perform this test are shown below. Notre that the name of the column which contains the check numbers is called “Check Number”. All of the data is to be tested, i.e. there are no exclusions for testing, so the “Where” box is blank. This command does not require any other information, so that box is also blank. Output results Numeric Sequence Gaps Auditing data on Excel worksheets Page 60
- 90. Audit Commands Output results (pasted into Excel work sheet – not all of the report is shown) Gaps: Count: 2217 Missing: 6642 3 6 2 9 14 4 15 18 2 19 22 2 22 25 2 25 29 3 29 32 2 33 35 1 35 37 1 37 42 4 43 47 3 49 51 1 52 56 3 56 59 2 59 62 2 62 64 1 64 66 1 66 70 3 70 73 2 This report indicates that for the sequence tested, there were 2,217 gaps which consisted on 6,642 instances of missing numbers. Output results Auditing data in Excel Page 61 worksheets
- 91. Audit Commands 4.3.2 Data Extraction Data extraction is a very common audit procedure whose purpose is to narrow down the transactions or other data which needs to be tested. Only two pieces of information are required – the name of the command which is selected from the drop down list (“Data extraction”) and the specific instructions which are contained in the “Other Info” column. There are many available commands for performing data extraction and they are described in more detail in Chapter 7. In the first example, the audit wishes to extract fixed asset records for those assets which were acquired during the fiscal year ended June 30, 2008, i.e. July 1, 2997 – June 30, 2008. The name of the column for the acquisition date is named “acquisition date”. Example 1 Note that because the column name contains an embedded space, it must be enclosed in brackets. Auditing data on Excel worksheets Page 62
- 92. Audit Commands Usage Example 2 In the second example, the auditor wishes to test for a possible error condition. Few assets with a useful life of more than 10 years would have a cost of less than $1,000. The auditor wishes to run an extract to see if there are any such records. In some cases, the syntax needed for the command may not be obvious. There is a “help” facility available by clicking on the label named “Where?”. This brings up a form of examples, where a command similar to that needed may be selected and edited. Example output Output will be just those rows (if any) which meet the criteria specified. At a minimum a header row will be provided. Data Extraction Auditing data in Excel Page 63 worksheets
- 93. Audit Commands Output results Auditing data on Excel worksheets Page 64
- 94. Audit Commands Data Extraction Output results (pasted into Excel work sheet – not all is shown) This is a schedule of all assets which have been over depreciated, i.e. cost less accumulated depreciation exceeds salvage. Output results Auditing data in Excel Page 65 worksheets
- 95. Audit Commands 4.3.3 Duplicates Duplicates Often it is desirable to check if any transactions are exact duplicates. The auditor specifies what constitutes a duplicate, as ordinarily this will depend upon the values in several columns. As an example, a duplicate invoice might be defined as the same vendor number, same invoice date and same invoice number. Note that one or more columns can be used in the search for duplicate transactions. There is no limit as to the number of columns which may be involved. Usage Example 1 The first example is a test performed as part of an accounts payable audit. A potential duplicate invoice is defined as one which has the same vendor number, invoice number and invoice date. The test is performed using the commands shown below. The command text in the “Other info” is simply the column names separated by commas: Results A schedule of potential duplicate invoices, using the specification provided. Usage Example 2 Auditing data on Excel worksheets Page 66
- 96. Audit Commands In an audit of fixed assets, an audit objective is to determine the accuracy of the records by checking for duplicate asset tag numbers. Tag numbers should be unique within any single location. However, there are certain “generic” tag numbers which begin with the letter “A” and these tag numbers should not be tested. The test is performed using the commands shown below. The command text in the “Other info” is simply the column names separated by commas: Output results Duplicates Auditing data in Excel Page 67 worksheets
- 97. Audit Commands Output results (pasted into Excel work sheet – not all rows and columns are shown, highlighting added for emphasis) location tagno Cost AD Replace Bookval Salvage Depr ABC 19 5766 2357.063 1730 3408.94 1153 471.4125 ABC 19 2575 1042.965 772 1532.03 515 208.5931 ABC 56 3888 1568.307 1166 2319.69 778 313.6614 ABC 56 7557 3036.653 2267 4520.35 1511 607.3306 ABC 110 2735 1102.043 820 1632.96 547 220.4085 ABC 110 5214 2101.48 1564 3112.52 1043 420.2959 ABC 122 8814 3527.223 2644 5286.78 1763 705.4446 ABC 122 2040 826.3205 612 1213.68 408 165.2641 ABC 139 7391 2966.962 2217 4424.04 1478 593.3925 ABC 139 2425 978.3281 728 1446.67 485 195.6656 ABC 233 8410 3424.003 2523 4986 1682 684.8005 ABC 233 4463 3570 1339 893 893 357.7068 ABC 258 2704 1098.159 811 1605.84 541 219.6318 ABC 258 8965 3620.646 2690 5344.35 1793 724.1293 ABC 402 6213 2531.266 1864 3681.73 1243 506.2532 ABC 402 4365 1771.483 1310 2593.52 873 354.2965 ABC 418 2952 1187.545 886 1764.46 590 237.5089 ABC 418 6729 2728.152 2019 4000.85 1346 545.6304 ABC 441 7380 3014.342 2214 4365.66 1476 602.8683 ABC 441 7263 2970.587 2179 4292.41 1453 594.1173 ABC 520 6359 2567.103 1908 3791.9 1272 513.4206 ABC 520 8120 3297.159 2436 4822.84 1624 659.4317 ABC 556 1198 486.1772 359 711.82 240 97.23544 ABC 556 3849 1576.375 1155 2272.63 770 315.2749 ABC 560 3209 1287.226 963 1921.77 642 257.4452 Output results Auditing data on Excel worksheets Page 68
- 98. Audit Commands 4.3.4 Same, Same, Different Same, Same, Different Unusual or error conditions may be detected using the “same, same, different” test. An example during a review of invoice transactions would be two invoice payments which had the same vendor, same invoice number, same date, but different amounts. Similarly, during a review of the employee master file, two records might be identified which have the same employee last name, same employee first name, same city, same street, but different social security numbers. The purpose of the same, same, different procedure is to identify any such records, if they exist. The test is performed using the names of the columns to be tested. The names of each column to be tested for same, same different, separated by commas. The last column specified is that which is tested for being different. For example, in the invoice example above, the testing specification would be “[Vendor Number],[Invoice Number],[Invoice date], [Invoice Amount]” (without the quotes). Usage Example 1 In an audit of accounts payable, test for the unusual situation described above. Approach – using the “ssd” command, analyze the transactions. Audit Command values Column value – [blank] Text Box – [Vendor Number],[Invoice Number],[Invoice date],[Invoice Amount] Where – (empty) Results A schedule of any transaction pairs which have the same vendor number, invoice number, invoice date, but a different invoice amount. Usage Example 2 In an audit of payroll transactions, check for any pair of records which have the same employee last Auditing data in Excel Page 69 worksheets
- 99. Audit Commands name, same employee first name, same street address, but different employee numbers. Tests are to be made only for those employees in Florida, Georgia and Alabama. Approach – using the “ssd” command, analyze such transactions. Audit Command values Column value – [empty] Text Box – [last name],[first name], [street address], [employee number] Where –state in (‘FL’,’GA’,”AL’) Results Schedule of any such records identified. The example below illustrates the procedure for identifying instances of fixed asset records which have the same tag number but a different location. Output results Same, Same, Different Auditing data on Excel worksheets Page 70
- 100. Audit Commands Output results (pasted into Excel work sheet – emphasis added, not all rows and columns shown) location tagno cost AD Replace Bookval Salvage Depr ABC 19 2575 1042.965 772 1532.03 515 208.5931 ABC 19 5766 2357.063 1730 3408.94 1153 471.4125 ABC 56 3888 1568.307 1166 2319.69 778 313.6614 ABC 56 7557 3036.653 2267 4520.35 1511 607.3306 ABC 110 2735 1102.043 820 1632.96 547 220.4085 ABC 110 5214 2101.48 1564 3112.52 1043 420.2959 ABC 122 2040 826.3205 612 1213.68 408 165.2641 ABC 122 8814 3527.223 2644 5286.78 1763 705.4446 ABC 139 2425 978.3281 728 1446.67 485 195.6656 ABC 139 7391 2966.962 2217 4424.04 1478 593.3925 ABC 233 4463 3570 1339 893 893 357.7068 ABC 233 8410 3424.003 2523 4986 1682 684.8005 ABC 258 2704 1098.159 811 1605.84 541 219.6318 ABC 258 8965 3620.646 2690 5344.35 1793 724.1293 ABC 402 4365 1771.483 1310 2593.52 873 354.2965 ABC 402 6213 2531.266 1864 3681.73 1243 506.2532 ABC 418 2952 1187.545 886 1764.46 590 237.5089 ABC 418 6729 2728.152 2019 4000.85 1346 545.6304 ABC 441 7263 2970.587 2179 4292.41 1453 594.1173 ABC 441 7380 3014.342 2214 4365.66 1476 602.8683 ABC 520 6359 2567.103 1908 3791.9 1272 513.4206 ABC 520 8120 3297.159 2436 4822.84 1624 659.4317 ABC 556 1198 486.1772 359 711.82 240 97.23544 ABC 556 3849 1576.375 1155 2272.63 770 315.2749 ABC 560 3209 1287.226 963 1921.77 642 257.4452 This schedule shows those assets which have the same location and tag number, but a different cost amount. Output results Auditing data in Excel Page 71 worksheets
- 101. Audit Commands 4.3.5 Trend Lines The system provides for four primary types of trend line analysis: Briefly, the tests perform the following procedures: Menu name for test Description Regression Best Fit Performs a basic “best fit” linear regression and reports the results as text file. Uses a single column of data for the regression. Trend Line Most flexible type of regression analysis, as it can summarize or aggregate data prior to plotting. Handles various periods, as well as various summarization functions. Confidence Band (summarize Expects time line data, with a column for year, column for data) month, X-axis amount, Y-axis amount Confidence Band Expects an identifier, an X-value and a Y-value Regression best fit Trend lines Auditing data on Excel worksheets Page 72
- 102. Audit Commands The purpose of the trend line procedure is to perform a “best fit” linear regression test on transaction data, and then calculate both confidence intervals and prediction intervals in order to determine if any amounts might lie outside these bounds. Any such amounts might be tested by the auditor to ensure that they do not represent errors. Usage Example 1 Comparative income statements exists for the last five years. In this test, a trend analysis on the Sales amounts will be performed. (The amounts shown are actual from a Standard and Poors report for a Fortune 500 company. Since the data is in horizontal format, the check box “Rows” is checked before the data is copied from Excel and pasted into the form. Auditing data in Excel Page 73 worksheets
- 103. Audit Commands Output results Trend Line Output results show the basic trend line information – intercept, slope and correlation coefficient. The slope is negative because the information goes back in time. The correlation of 83% indicates a fairly consistent trend over time. Output results Auditing data on Excel worksheets Page 74
- 104. Audit Commands 4.3.6 Time Line analysis Time line analysis The purpose of the timeline analysis command is summarize and chart key information from transaction data over a time period in order to see underlying trends or to identify potential anomalies or errors. Built into the functionality is the ability to “drill down” using various criteria and also to view the summarized information using various measures such as counts, totals, averages, etc. Output is a detail report which identifies potential variances, as well as a chart so that the summarized information may be more easily viewed. To run the analysis, five pieces of information are needed: 1. Name of the date column to be used, i.e. the name of the column which contains the transaction date to be used for the analysis. 2. Name of the amount column, i.e. the column containing the numeric information being analyzed 3. The time interval to be used for the analysis, specified as a single letter, and which must be one of the following: a. monthly, specified using ‘m’ b. quarterly, specified using ‘q’ c. annually, specified using ‘y’ d. weekly, specified using ‘w’ e. daily, specified using ‘d’ 4. The type of metric to be applied, which must be one of the following: a. summary, specified as ‘sum’, b. count, specified as ‘count’ c. average, specified as ‘avg’, d. minimum value, specified as ‘min’ e. maximum value, specified as ‘max’, f. standard deviation, specified as ‘stdev’ 5. The confidence level, a number between 0 and 1. The default value is .95, i.e. a 95% confidence level With this information, the system will aggregate the data using the time period specified and the type of aggregation desired. The results will be written out as a text file and also plotted on a chart. Auditing data in Excel Page 75 worksheets
- 105. Audit Commands Usage Example 1 In an audit of accounts payable, the auditor wishes to see a trend as to invoice totals for a specified vendor, by quarter, in order to view the overall trend and to see if there may be any unusual items such as “spikes”, missing data, etc. The date column to be used is called “invoice date”, and the amount column to be analyzed is called “invoice amount”. Tests are to be done at a 95% confidence level. The command would be as follows: [invoice date], [invoice amount], q, sum, .95 Usage Example 2 Continuing with the same example, the auditor now wants to see transaction counts by month. The command would then be as follows: [invoice date], [invoice amount], m, count, .95 The command box above performs a time line analysis of asset acquisitions using the “cost” column, and specifying a period of “q” (quarterly) with a precision of 95%. The chart produced is shown below. Auditing data on Excel worksheets Page 76
- 106. Audit Commands The chart indicates that there were few or no asset acquisitions prior to the first quarter of 2004. To get a more representative picture, the procedure can be re-run, specifying just asset acquisitions made after January 1, 2004. Running this procedure produces the following chart: Auditing data in Excel Page 77 worksheets
- 107. Audit Commands Output results Time line analysis Auditing data on Excel worksheets Page 78
- 108. Audit Commands A chart is produced which shows the invoices totaled by quarter and plotted as a trend line. There is also a text report which has all the details. Below is that data imported into Excel. Output results Auditing data in Excel Page 79 worksheets
- 109. Audit Commands Linear regression report: Equation: y = b + mx Intercept: 1,749,261.72 Slope:21,191.67 Correlation: 1% Precision: 0.95 Lower Lower Upper Upper Desc X Y Predicted Prediction Confidence Predicted Confidence Prediction 2002-01 1 237,272 1,770,453 1,770,447 1,770,449 1,770,453 1,770,458 1,770,460 2002-02 2 1,788,596 1,791,645 1,791,639 1,791,641 1,791,645 1,791,649 1,791,651 2002-03 3 2,742,676 1,812,837 1,812,831 1,812,833 1,812,837 1,812,840 1,812,842 2002-04 4 4,232,764 1,834,028 1,834,023 1,834,026 1,834,028 1,834,031 1,834,034 2003-01 5 736,504 1,855,220 1,855,215 1,855,218 1,855,220 1,855,222 1,855,225 2003-02 6 1,547,613 1,876,412 1,876,407 1,876,410 1,876,412 1,876,413 1,876,417 2003-03 7 1,840,285 1,897,603 1,897,599 1,897,602 1,897,603 1,897,605 1,897,608 2003-04 8 3,446,882 1,918,795 1,918,790 1,918,794 1,918,795 1,918,796 1,918,800 2004-01 9 343,401 1,939,987 1,939,982 1,939,985 1,939,987 1,939,988 1,939,991 2004-02 10 1,631,899 1,961,178 1,961,174 1,961,177 1,961,178 1,961,180 1,961,183 2004-03 11 1,345,257 1,982,370 1,982,365 1,982,368 1,982,370 1,982,372 1,982,375 2004-04 12 3,621,404 2,003,562 2,003,556 2,003,559 2,003,562 2,003,565 2,003,567 2005-01 13 376,953 2,024,753 2,024,748 2,024,750 2,024,753 2,024,757 2,024,759 2005-02 14 2,130,685 2,045,945 2,045,939 2,045,941 2,045,945 2,045,949 2,045,951 2005-03 15 2,759,735 2,067,137 2,067,130 2,067,132 2,067,137 2,067,141 2,067,143 Queries can now be further refined. The next query obtains the same information by month, changing only the period parameter from a ‘q’ to an ‘m’. The results showing monthly amounts are below: Auditing data on Excel worksheets Page 80
- 110. Audit Commands Auditing data in Excel Page 81 worksheets
- 111. Audit Commands 4.3.7 Confidence Band Confidence Band The purpose of the confidence band procedure is to perform a linear regression test on transaction data, and then calculate both confidence intervals and prediction intervals in order to determine if any amounts might lie outside these bounds. Any such amounts might be tested by the auditor to ensure that they do not represent errors. Usage Example 1 In an audit of transportation expenses, there is a need to determine if there is a linear relationship between mileage and annual maintenance expenses Approach – using the “confband” command, test such a relationship. Audit Command values Column value –N/A Text Box – county, mileage, expense, 90 Where – (empty) Results A trend line chart with confidence and prediction intervals for the linear relationship. The results are shown below. Auditing data on Excel worksheets Page 82
- 112. Audit Commands The chart shows that there is a fair overall correlation between the data. (86.3%). However, for one data point the repair costs are well outside the expected range. This might be an area the auditor could focus on. Output results Confidence Band Auditing data in Excel Page 83 worksheets
- 113. Audit Commands Output results (pasted into Excel work sheet – emphasis added, formatting performed for clarity) Linear regression report: Equation: y = b + mx Intercept: 5505.15584475063 Slope:6.61707235425678E-02 Correlation: 35% Precision: 0.9 Desc X Y Predicted Lower PredictionConfidence Lower Predicted Upper Confidence Upper Prediction Comment Wake 19,758.00 6,737.81 6,812.56 -1,028.65 -1,027.45 6,812.56 14,652.56 14,653.76 Mecklenberg 14,097.00 6,248.66 6,437.96 3,231.92 3,234.85 6,437.96 9,641.08 9,644.01 New Hanover 12,518.00 6,180.84 6,333.48 4,418.72 4,423.63 6,333.48 8,243.33 8,248.24 Johnston 12,121.00 6,231.25 6,307.21 4,716.58 4,722.49 6,307.21 7,891.93 7,897.84 Person 11,838.00 6,208.12 6,288.48 4,928.60 4,935.52 6,288.48 7,641.45 7,648.37 observed greater than upper predictionobserved greater than upper Dansbury 7,957.00 8,213.17 6,031.68 4,199.87 4,205.00 6,031.68 7,858.35 7,863.48 confidence Smythe 18,731.00 6,623.40 6,744.60 -255.53 -254.19 6,744.60 13,743.39 13,744.73 Jackson 2,465.00 5,488.28 5,668.27 -658.25 -656.76 5,668.27 11,993.30 11,994.78 Gregory 14,380.00 6,323.13 6,456.69 3,019.05 3,021.78 6,456.69 9,891.60 9,894.33 Altenberg 13,612.00 6,330.88 6,405.87 3,596.66 3,600.00 6,405.87 9,211.74 9,215.08 Jamestown 16,769.00 6,691.96 6,614.77 1,221.32 1,223.06 6,614.77 12,006.49 12,008.23 Flurry 1,880.00 5,430.37 5,629.56 -1,176.03 -1,174.65 5,629.56 12,433.76 12,435.14 Snow 15,366.00 6,443.21 6,521.94 2,277.20 2,279.41 6,521.94 10,764.46 10,766.67 Bear 790.00 5,307.48 5,557.43 -2,140.82 -2,139.60 5,557.43 13,254.46 13,255.68 Rugged 3,488.00 5,615.62 5,735.96 247.16 248.87 5,735.96 11,223.05 11,224.76 PineLake 4,154.00 5,691.17 5,780.03 836.55 838.45 5,780.03 10,721.60 10,723.50 FireStorm 3,083.00 5,427.82 5,709.16 -111.28 -109.67 5,709.16 11,527.99 11,529.60 observed less than Fern Valley 10,354.00 6,032.78 6,190.29 5,993.84 6,049.51 6,190.29 6,331.06 6,386.73 lower confidence Output results Auditing data on Excel worksheets Page 84
- 114. Audit Commands 4.3.8 Confidence Band (Time Series) Confidence Band (Time Series) The purpose of the confidence band (time series) procedure is to perform a linear regression test on transaction data, and then calculate both confidence intervals and prediction intervals in order to determine if any amounts might lie outside these bounds. Any such amounts might be tested by the auditor to ensure that they do not represent errors. Usage Example 1 In an audit of transportation expenses, there is a need to determine if there is a linear relationship between mileage and annual maintenance expenses Approach – using the “confband2” command, test such a relationship. Audit Command values Column value –N/A Text Box – year, month, x, y Where – (empty) Results A trend line chart over time with confidence and prediction intervals for the linear relationship. Auditing data in Excel Page 85 worksheets
- 115. Audit Commands Output results Confidence Band Auditing data on Excel worksheets Page 86
- 116. Audit Commands Output results (pasted into Excel work sheet) Linear regression report: Equation: y = b + mx Intercept: -98,566,325.03 Slope:.75 Correlation: 92% Precision: 0.95 Desc X Y Predicted Lower Prediction Lower Confidence 2006 612,431,244 366,090,524 362,095,393 362,075,542 362,081,464 2006 613,830,062 367,229,455 363,147,564 363,127,884 363,133,880 2006 612,620,399 365,915,304 362,237,673 362,217,845 362,223,777 2006 618,495,141 369,547,857 366,656,567 366,637,446 366,643,700 2006 627,127,285 374,879,234 373,149,538 373,131,398 373,138,180 2006 633,270,865 378,741,151 377,770,648 377,753,157 377,760,358 2007 632,794,709 378,369,860 377,412,490 377,394,950 377,402,118 2007 642,889,555 384,330,410 385,005,684 384,989,116 384,997,055 2007 644,463,504 385,499,489 386,189,586 386,173,156 386,181,226 2007 647,205,315 386,752,684 388,251,935 388,235,738 388,244,043 2007 653,761,539 390,778,601 393,183,429 393,167,743 393,176,647 2007 652,110,029 390,005,684 391,941,188 391,925,380 391,934,128 2007 660,198,698 394,903,316 398,025,366 398,010,110 398,019,649 2007 664,973,395 397,501,158 401,616,822 401,601,837 401,611,873 2007 668,487,813 399,771,977 404,260,315 404,245,502 404,255,913 2007 668,513,159 399,672,729 404,279,380 404,264,568 404,274,982 2007 678,544,943 405,511,744 411,825,140 411,810,679 411,822,128 2007 681,055,251 407,453,084 413,713,356 413,698,949 413,710,617 2008 684,321,972 409,175,851 416,170,535 416,156,179 416,168,076 2008 686,935,005 410,469,415 418,136,020 418,121,687 418,133,702 2008 693,665,939 414,624,926 423,198,929 423,184,588 423,196,558 2008 695,128,158 415,499,082 424,298,789 424,284,432 424,296,327 2008 698,103,060 417,103,640 426,536,466 426,522,064 426,533,753 2008 704,591,803 421,191,848 431,417,202 431,402,636 431,413,719 Output results Auditing data in Excel Page 87 worksheets
- 117. Audit Commands Confidence Band Output results (chart) The chart below was specified using a custom color scheme and the title shown. These values are provided using the “Chart” tab on the processing form. The chart indicates that there is a good correlation (98.7%) between the claim amount and the ffp amount. The correlation should be 100%. Further checking is needed at the account level. Output results - chart Auditing data on Excel worksheets Page 88
- 118. Audit Commands 4.3.9 Invoice Near Miss Invoice “Near Miss” Invoice Near Miss Duplicate invoices may arise due to a variety of circumstances, even when system edits are in place. One example is where two invoices from the same vendor for the same amount are entered, where one invoice number is a slight variation of the other, e.g. a transposition. In cases like this, the system may not necessarily recognize that the invoices are duplicates. The purpose of the near miss procedure is to identify potential duplicate invoices by checking for any combination of two invoices which meet the following criteria: same vendor number difference in invoice amounts is $.02 or less date difference is less than amount specified difference in invoice numbers (as measured by Levenshtein distance) is less than the number spe- cified An example will illustrate: First invoice - vendor 123, amount $100.00, date 8/18/2009, invoice number 10023 Second invoice - vendor 123, amount $100.00, date 9/5/2003, invoice number 10032 If the specification for the identification of duplicates were 30 days and a Levenshtein distance of 2, these two invoices would be flagged as potential duplicates. For this test, the input data does not need to be sorted. However, the comparison process is com- putationally intensive, so that invoices from any one vendor are tested in blocks of up to 200 in count. Generally, the system will identify potentially duplicate invoices based upon the criteria provided, but it is possible that for vendors with a large number of invoices, two potentially duplicate invoices could be missed. Auditing data in Excel Page 89 worksheets
- 119. Audit Commands Output results Invoice “Near Miss” Output results (pasted into Excel work sheet) Near Miss Report Vendno Amt Inv Date Second Date Invno Suspect Invno Closeness V200 103.02 5/31/2007 5/31/2007 2103 4 V200 103.02 6/2/2007 5/31/2007 2103 4 V200 103.02 6/2/2007 5/31/2007 0 V201 186.01 5/26/2007 5/26/2007 2186 2186 0 V202 647.82 4/29/2007 4/29/2007 20647 2647 1 V202 647.82 4/29/2007 4/29/2007 2467 2647 2 V202 647.82 4/29/2007 4/29/2007 2467 20647 2 V202 647.82 4/29/2007 4/29/2007 2647 2647 0 V202 647.82 4/29/2007 4/29/2007 2647 20647 1 V202 647.82 4/29/2007 4/29/2007 2647 2467 2 Auditing data on Excel worksheets Page 90
- 120. Audit Commands Output results Auditing data in Excel Page 91 worksheets
- 121. Audit Commands 4.3.10 Split Invoices Split invoices The purpose of the split invoice test is to determine if an invoice may have been paid as a single amount and then also paid with multiple payments totaling the invoice amount. As an example, an invoice in the amount of $2,700 consisting of three line items of $1,000, $900 and $800 may have been paid once as $2,700 and then three additional payments made of $1,000, $900 and $800. The test for split invoices uses certain auditor parameters to determine whether an invoice amount should be considered, namely the length of time between amounts. The maximum number of days apart two payments are in order to be considered. For example, the auditor may wish to consider only those payments to a vendor that are within 10 days of each other as part of the test for split invoices. Any payment amounts made more than ten days apart would then not be considered as part of the split invoice test. Usage Example 1 A test of invoices is made to determine if any potential “split invoice” payments can be identified. The names of the column values to be tested are as follows: Column name Description Vendor Vendor number InvNo Invoice Number InvDate Invoice Date InvAmt Invoice Amount Tests are to be made for invoices with dates up to 30 days apart. The values entered into the form are shown below. Auditing data on Excel worksheets Page 92
- 122. Audit Commands Output results Split invoices Output results (pasted into Excel work sheet) Split Invoice Report Vendno Inv No Inv No2 Amount Amount2 Amount 3 Diff V201 2186 2186 86.01 186.01 100 2 30 V201 2186 2186 100 186.01 86.01 2 30 These results indicate that there was an invoice paid in the amount of $186.01. In addition, two other invoices to the same vendor, within the specified time period were paid which also totaled to $186.01 = $100.00 + $86.01. Output results Auditing data in Excel Page 93 worksheets
- 123. Audit Commands 4.3.11 Check SSN Validity of Social Security Numbers The purpose of testing for Social Security number validity is to identify any social security numbers which would be considered invalid according to the criteria published on the site of the Social security Administration. The test considers several factors: • Ranges of numbers issued • Certain digits or ranges which are automatically invalid • The highest number assigned for an area Note: The social security number ranges are published monthly by the Social Security Administration. Warning: Social security numbers of deceased persons will not be identified. Usage Example 1 A test of validity of social security numbers is to be performed on data where the social security number column is named “SSN”. Audit Command values Column value – [SSN] Text Box – (empty) Where – (empty) Results A list of all records where the social security number is invalid. The input form used to perform the checking is shown below. Auditing data on Excel worksheets Page 94
- 124. Audit Commands Output results Validity of Social Security Numbers Auditing data in Excel Page 95 worksheets
- 125. Audit Commands Output results (pasted into Excel work sheet- not all rows shown – no social security numbers shown are valid – highlight added for emphasis) SSN LASTNAME FIRSTNAME MIDNAME DOB ADDRESS CITY NOT A REAL SOCIAL SECURITY NUMBER BLACKBURN BLAKE 1/15/1930 P O BOX 196 AGURA HILLS NOT A REAL SOCIAL SECURITY NUMBER NYMAN WOODROW A 1/24/1930 10013 S RHODES MONMOUTH JUNCTION NOT A REAL SOCIAL SECURITY NUMBER MCMULLAN CLAYBORN 1/29/1930 931 E HOPE ST WESTPORT NOT A REAL SOCIAL SECURITY NUMBER WEINREB DEBBIE 5/12/1930 818 KIRKWOOD ST HOLLIS NOT A REAL SOCIAL SECURITY NUMBER DIAZ CHARLENE 5/18/1930 C/O 3420 NE 168TH ST PELHAM NOT A REAL SOCIAL SECURITY NUMBER NANCE YVONNE A 8/15/1930 10 RAINBOW LANE HILLS GRANADA NOT A REAL SOCIAL SECURITY NUMBER RUSSELL MELISSA JAMES 8/30/1930 237 MASTEN RD EGGERTSVILLE NOT A REAL SOCIAL SECURITY NUMBER BARBOUR ANTHONY 10/22/1930 P O BOX 630, #79729-004 ROCKVILLE CTR NOT A REAL SOCIAL SECURITY NUMBER STONER JO MIGUEL 4/17/1931 4595 HYLAND BLVD COLEMAN NOT A REAL SOCIAL SECURITY NUMBER PEPIN LINDA L 6/30/1931 311 BRIDGE ST DECATUR NOT A REAL SOCIAL SECURITY NUMBER MCNAMARA TIMOTHY ALICE 12/30/1931 11120 NW GAINESVILLE ROAD LOS ALTOS NOT A REAL SOCIAL SECURITY NUMBER CASTRO LOUIS L 1/22/1932 300 MAIN STREET ROCHESTER NOT A REAL SOCIAL SECURITY NUMBER CAPLES ANGELA 1/25/1932 P O BOX 8103 READING NOT A REAL SOCIAL SECURITY NUMBER SCHWANDT LOUIS L 1/30/1932 3000 MURWORTH DR, APT 511 SPOKANE NOT A REAL SOCIAL SECURITY NUMBER FISHKIN AVANELL 4/23/1932 P O BOX 496 MIAMI NOT A REAL SOCIAL SECURITY NUMBER MOORE LEROY LANG 7/1/1932 3201 KNIGHT ST, APT 1402 KENNER NOT A REAL SOCIAL SECURITY NUMBER BAJZA MEGAN JEAN 7/9/1933 241 FARNOL ST, SW PRESCOTT NOT A REAL SOCIAL SECURITY NUMBER BROWN BRIDGETTE 8/2/1933 P O BOX 1032, #79399-004 CAMP VERDE NOT A REAL SOCIAL SECURITY NUMBER WHITE MARK K 9/7/1933 269 EAST S STREET GROVE DOWNERS NOT A REAL SOCIAL SECURITY NUMBER BUTCHER HARRIET S 3/13/1934 5771 DEXTER CIRCLE KNOXVILLE NOT A REAL SOCIAL SECURITY NUMBER VANGRAEFSCHEPE JASON PARAMA 3/26/1934 501 N 13TH AVENUE CHARLESTON Output results Auditing data on Excel worksheets Page 96
- 126. Audit Commands 4.3.12 Check PO Box Check for Post Office Box The purpose of the check P.O. Box command is to examine addresses for an indication that it is a Post Office Box. Because there are many ways in which a Post Office Box address can be coded, a procedure devoted to just this type of test is provided. For example, the address may contain “PO Box”, “POB”, “P.O. Box”, etc. In audits of disbursements made based upon an accounts payable system, one of the audit tests commonly performed is to test for vendors whose address is a post office box. Generally, vendors should have a street address where they receive their mail. In certain instances, fraudulent payments have been made to vendors using a post office box in order to disguise the true nature of the payment, which may be associated with an employee of the company making the payment. Although it is possible to visually check for post office boxes in addresses, the process can be tedious and time consuming, especially if a large number of records are involved. One of the challenges is simply the ability to recognize many of the variations possible in the designation of a post office box in an address. For example, the address might be structured in any of the following formats: P.O. Box 123 POB 123 Post office box 123 PO 123 Box 123 pobox 123 Etc. Example 1 Search the column named “Address1” in the vendor master for addresses which might be post office boxes. Auditing data in Excel Page 97 worksheets
- 127. Audit Commands Output results Check for Post Office Box Auditing data on Excel worksheets Page 98
- 128. Audit Commands Output results (pasted into Excel work sheet – not all rows and columns shown, highlighting added for emphasis) ADDRESS LASTNAME FIRSTNAME IDNAME DOB M CITY STATE P O BOX 196 BLACKBURN BLAKE 1/15/1930 AGURA HILLS CA P O BOX 630, #79729-004 BARBOUR ANTHONY 10/22/1930 ROCKVILLE CTR NY P O BOX 8103 CAPLES ANGELA 1/25/1932 READING PA P O BOX 496 FISHKIN AVANELL 4/23/1932 MIAMI FL P O BOX 1032, #79399-004 BROWN BRIDGETTE 8/2/1933 CAMP VERDE AZ P O BOX 820, HIGHWAY 44 TELFORD ANGELA 1/14/1934 LITTLE ROCK AR P O BOX 41617 HYATT BARBARA 8/3/1934 GRAND ISLAND NY P O BOX 638 GURUNIAN ANTHONY 1/4/1937 MIAMI FL P O BOX 8119 ARTMAN ANGELA 2/26/1937 MALIBU CA P O BOX 52362 HARDING ARTHUR 9/10/1937 PALM HARBOR FL P O BOX 7 STONE ANNA 1/27/1938 WARREN MI P O BOX 1813 FAULKNER BONNIE 9/22/1938 RINGWOODJN P O BOX 737 CARR ANGELIQUE 9/28/1938 MASSAPEQUA PARK NY POST OFFICE BOX 3007 ANDERSON AMANDA 12/29/1938 FORT VALLEY GA P O BOX 6001, UNIT D FCI MILLS ARMANDO 6/3/1939 CHICAGO IL P O BOX 2796 ROUTON BETH 9/9/1939 SAN JOSE CA P O BOX 2002 LINCK BILL 1/3/1940 SANTA MONICA CA P O BOX 641 BUANNO ANSA 3/31/1940 DAVIDSONVILLE MD P O BOX 312 SCANDY BERNADETTE 10/10/1940 PORT WASHINGTON NY P O BOX 832 JONES ANGELA 4/9/1941 MASPETH NY P O BOX 60189 BARTLETT ARLENE 9/19/1941 MADISON WI Output results Auditing data in Excel Page 99 worksheets
- 129. Audit Commands 4.3.13 Calculated Values Calculated Values In many instances the auditor wishes to add a column of data, e.g. a calculated amount, based upon values contained in other columns. Calculated values A common procedure used during the analysis of data in Excel is to insert one or more columns and calculate their value using formula which based on values contained in other columns. Although this procedure is effective, it has the drawback that column letters must be used instead of column names which makes interpreting and verifying the formulae used more difficult. The purpose of the calculated values procedure is to add one or more columns to a work sheet us- ing formula with column names. Often the formula will consist of mathematical operations, but any SQL function may be used (see list of functions in description of where clause values). The syntax for the calculated values is "expression1 as name1, expression2 as name2" etc. where "expression" is a calculated value. The word "as" must be used without change, and "name" must be a description beginning with a letter and consisting of only letters, numbers and the special char- acters "$", "_". If the name contains any embedded spaces, then the entire name must be enclosed in brackets, e.g. "[cost amount]". Examples - Add a column called net book value computed as cost less accumulated depreciation Other info - [cost] - [accumulated depreciation] as [net book value] (Note the use of brackets due to embedded spaces in the names) Auditing data on Excel worksheets Page 100
- 130. Audit Commands Output results Calculated Values Auditing data in Excel Page 101 worksheets
- 131. Audit Commands Output results (pasted into Excel work sheet – first column highlighted for emphasis) property tax TagNo Cost AD Replace Bookval Salvage Depr Life Location AcqDate 72.49729037 3504 2438 988.0542 731 1449.95 488 197.6108 6 ABC 4/6/2005 97.1394758 4148 3244 1301.21 973 1942.79 649 260.2421 5 ABC 2/3/2006 274.2308104 3302 9163 3678.384 2749 5484.62 1833 735.6768 8 ABC 10/15/2004 146.6431954 3816 4937 2004.136 1481 2932.86 987 400.8272 4 ABC 7/8/2005 240.3376714 3411 8118 3311.247 2435 4806.75 1624 662.2493 5 ABC 2/9/2007 245.5702876 2547 8258 3346.594 2477 4911.41 1652 669.3188 9 ABC 5/26/2007 94.12422075 1701 3143 1260.516 943 1882.48 629 252.1031 11 ABC 9/30/2005 265.6780722 3960 8955 3641.439 2686 5313.56 1791 728.2877 3 ABC 12/8/2005 85.70210075 5056 2885 1170.958 866 1714.04 577 234.1916 5 ABC 3/24/2005 47.82652079 2996 1596 639.4696 479 956.53 319 127.8939 3 ABC 10/7/2005 93.07851995 1299 3115 1253.43 934 1861.57 623 250.6859 12 ABC 3/4/2006 66.92986036 2881 2244 905.4028 673 1338.6 449 181.0806 8 ABC 3/6/2006 30.4 2791 3039 2431 912 608 608 761.4 12 ABC 3/17/2007 155.8641946 1443 5240 2122.716 1572 3117.28 1048 424.5432 12 ABC 11/17/2004 42.23143191 1202 1416 571.3714 425 844.63 283 114.2743 6 ABC 6/5/2007 172.5694554 3567 5776 2324.611 1733 3451.39 1155 464.9222 11 ABC 12/5/2004 79.1798243 5010 2645 1061.404 794 1583.6 529 212.2807 10 ABC 9/28/2006 91.2098218 4163 3048 1223.804 914 1824.2 610 244.7607 4 ABC 12/19/2005 271.3595988 1306 9177 3749.808 2753 5427.19 1835 749.9616 7 ABC 9/17/2006 11.65 5205 1165 932 350 233 233 95.43749 8 ABC 4/8/2006 73.8564635 4219 2500 1022.871 750 1477.13 500 204.5741 3 ABC 7/10/2006 17.93122414 1384 603 244.3755 181 358.62 121 48.8751 12 ABC 1/25/2006 284.3327576 3914 9578 3891.345 2873 5686.66 1916 778.269 4 ABC 8/19/2005 44.18759538 4323 1482 598.2481 445 883.75 296 119.6496 7 ABC 3/16/2007 143.4290984 4758 4829 1960.418 1449 2868.58 966 392.0836 9 ABC 2/3/2006 79.19611735 3213 2669 1085.078 801 1583.92 534 217.0155 11 ABC 5/21/2006 Output results Auditing data on Excel worksheets Page 102
- 132. Audit Commands 4.3.14 Fuzzy Match (LD) Fuzzy Match (Levenshtein distance) The technique of measuring the difference between text values based upon Levenshtein distance was developed by a Russian mathematician. The technique measures the number of steps required to make two character values match based upon additions, changes and deletions of text. It is particularly useful in identifying transpositions or other instances in which the difference between two text strings is minimal. The number of steps required to make the change is referred to as the "Levenshtein distance". Usage Example 1 Fuzzy Match Levenshtein distance The difference between any two character strings may be measured using the "Levenshtein dis- tance". This concept was developed by the Russian physicist Vladimir Levenshtein and defines the distance as the minimum number of character additions, deletions and changes necessary to trans- form one character string into another. For auditors, the concept is applicable to searches for character strings which represent only very minor differences between two character strings. For example, the name "McMillan" is similar, but not identical to "McMillun". In this case the distance would be one, because only a single change from the letter "a" to the letter "u" is necessary for them to be identical. As another example, trans- positions will represent a Levenshtein distance of 2, as both an insertion and a deletion are required in order for the two strings to be identical. Common uses for the algorithm can be found in searches where an exact match is not found, but two or more instances may be identified which are "close". Such searches might be needed in looking at vendor master files, checking for potentially duplicate invoice numbers or any other situ- ation where two or more instances might be found which are close, but not identical. The test can be performed on either a single column by specifying the column name, or else on all columns (by omitting the column name). If the test is to be done ignoring case, then the command "UCASE" should be specified for the column name, e.g. Ucase(lastname). If leading and trailing spaces are to be ignored the "TRIM" command should be specified, e.g. Trim(address). The search specification is made by providing the text to search against, as well as the maximum distance to be considered. The following are examples of usage: Auditing data in Excel Page 103 worksheets
- 133. Audit Commands Check for a last name within a distance of 2 from McMillan. column name - lastname other info - McMillan, 2 Same check, but ignore case column name - Ucase(lastname) other info - MCMILLAN, 2 Check for address like 108 Fallsworth, trim any spaces on left and right column name - trim(address) other info - 108 Fallsworth Same check, but ignore case column name - ucase( trim(address)) other info - 108 FALLSWORTH Output results Fuzzy Match (Levenshtein distance) Output results (pasted into Excel work sheet – not all columns shown, highlighting added for emphasis) LASTNAME FIRSTNAME MIDNAME DOB ADDRESS CITY STATE MCMULLAN CLAYBORN 1/29/1930 931 E HOPE ST WESTPORT T C This schedule is the results of a search for a record with a last name of ‘MCMILLAN’ with a Levenshtein distance of 2. In this example, a single character ‘U’ could be replaced with an ‘I’ to obtain the match desired. This was the only instance identified in the search that was within a Levenshtein distance of 2. Output results Auditing data on Excel worksheets Page 104
- 134. Audit Commands 4.3.15 Fuzzy Match (Regular Expression) Fuzzy Match (regular expression) Selection of subsets of data within a worksheet based upon more complex matching patterns is possible using the "fuzzy match" command. As an example, the auditor may wish to select all records for asset tag numbers that begin with "98", followed by any character or digit and then contain the digit "5". Other examples include all store locations beginning with the letters "A' through "C", followed by two digits and then one or more of any characters. All of these matches can be done using the technique of "regular expressions". There is fairly extensive documentation on how regular expressions work, but they generally consist of one or more special search characters with the following meanings - • ? - match any single character • * - match any one or more characters • [A-H] - match any single letter between "A" and "H" • [!A-H] - match any single character, except the letters "A" through "H" In order to do fuzzy matching, the auditor sets Usage Example 1 A search is to be made of employee last names where the first letter is “H” and the second letter is any of the characters “E” through “I”. The last name to be matched can contain two or more letters in total. The search specification is shown in the form below. Auditing data in Excel Page 105 worksheets
- 135. Audit Commands Output results Fuzzy Match (regular expression) Auditing data on Excel worksheets Page 106
- 136. Audit Commands Output results (pasted into Excel work sheet – not all rows and columns are shown) LASTNAME FIRSTNAME MIDNAME DOB ADDRESS CITY HENRY DARRIN 1/13/1930 844 JEFFERSON ST CLEARWATER HENTHORN PAMELA H 3/25/1936 2070 HIGHWAY NEW GLOUCESTER 30 W HICKS SHIRLEY C 6/20/1936 13317 S W 64 LANE S PADRE ISLAND HILPERT VANESSA A 11/25/1936 1072 FORDHAMSANTA ANA LANE HERNANDEZ BILLIE 2/7/1947 P O BOX 2000, #57621-004 MIAMI HENNEKES DAVID 4/22/1948 830 N FOOTE, APT B CITY YUBA HELMS JOEL MELVIN 7/1/1948 444 W DUARTE SEATTLE RD, #C3 HEADRICK AIDA 3/13/1949 ROUTE 7, BOX 7338 CHICAGO HEGARTY CARLOS 8/30/1950 MORGAN HILL FARM, BOX 62 RUDYARD HENDERSON LINDA L 10/10/1950 315 S 3RD STREET OSHKOSH HENLEY DAVID 3/4/1951 8303 LENNON ROAD WOODLAND HILLS HENRY TOBI ALAN 7/10/1951 111 STERLING DRIVE REDDING HERNANDEZ CHRISTOPH 7/30/1952 95 PALISADE AVENEWINGTON HERING ARTHUR 10/8/1954 P O BOX 589 ALBUQUERQUE HERZOG LUIS L 8/4/1955 3 MAULDIN AVEBARNESBORO HESSER BRENDA 8/11/1955 P O BOX 1439 JUNCOS HENRY MARK K 10/5/1955 269 HANOVER AVE, #202 CULPEPER HINTON RICKY E 5/2/1957 1710 WEINSTOCK ST LAKELAND HENDERSON JOHN MARIE 2/19/1958 4233 SUNLAND WARREN E COURT, S HEATH TERRI ANN 6/17/1958 11614 EAST 18TH STREET CEDAR CREEK Output results Auditing data in Excel Page 107 worksheets
- 137. Audit Commands 4.3.16 Sequential Invoices Sequential invoices Sequential Invoices Generally vendors do not issue sequentially numbered invoices to the same customer, except in un- usual situations or in cases where they have only a single customer. Sequential invoice checking is a test to determine which vendors of your organization may have only one customer - your organiz- ation. Note that the input data does not need to be sorted. The system does the checking by first sorting the invoice data by vendor and invoice number and then checking if any two invoices represent sequential numbers, i.e. they have a numeric difference of one. For any such instance identified, all the detail information for both invoices is listed in a re- pot for review. To perform the test, only the name of the vendor number column and the name of the column con- taining the invoice number need to be provided. As a simple example, suppose that vendor invoice data is to be tested for sequential invoices and that the name of the column identifying the vendor is called "Vend_No" and the name of the column containing the invoice number is "Invoice_No". The command to perform the check would then be "Vend_No, Invoice_No" (without the quotes). Note that any non-numeric values are removed from the invoice number before a comparison is performed. Thus an invoice number "C102345B" would be transformed to "102345" for purposes of the test. Example 1 Vendor invoice data is to be tested to determine if any vendor has issued sequential invoices. The input data is not sorted. The test to be selected is “Sequential invoices” as selected from the drop down list of commands. The name of the column for the vendor number is named “Vendor”. The test is not limited to any records, so the “where” information is left blank. The “other information” is the name of the Auditing data on Excel worksheets Page 108
- 138. Audit Commands vendor column and the name of the column containing the invoice numbers, separated by a comma. Results Output results Sequential invoices Output results (pasted into Excel work sheet) Count of sequentially numbered items V201 : 1 The results indicate that only one vendor (“V201”) had issued a sequential invoice and that vendor (“V201”) issued just one sequential invoice. Output results Auditing data in Excel Page 109 worksheets
- 139. Audit Commands 4.4 Patterns 4.4.1 Round Numbers An example will best illustrate the concept of pattern testing for round numbers. Consider a case where journal entries are prepared at the end of each month. Generally, journal entry postings will contain some round numbers. Although somewhat tedious, the auditor could determine the count of round numbers posted for the year. For example, there might be a total of 2,000individual journal entry postings for the year. Of those, 100 (or 5%) were round numbers, possibly indicating an estimate. If the round number postings were fairly evenly spread throughout the year, this would indicate that possibly nothing unusual exists, based upon a comparative test of round numbers. However, if the concentration is in the last month of the fiscal year (or the first month of the next fiscal period), then this could be a different situation. Pattern testing is based upon the overall concept outlined above. The procedure first obtains counts or totals for the entire transaction population. Then the procedure separates the population based upon criteria specified by the auditor (in the example above posting month) and then systematically compares each subgroup with the overall population. The system then reports each group based upon how different it is from the overall population as measured by the statistical test “Chi Square”. This same test can also be applied using metrics other than round numbers – e.g. counts by day of week, counts by holidays, counts by data stratification, etc. Usage Example 1 In an audit of accounts payable, a comparative analysis is to be made of purchase orders by buyer to determine which buyers purchase orders are the most different from all others as measured by the type and quantity of round numbers. Approach – using the “patternrn” command, check the purchase orders. Audit Command values Column value – [purchase order amount] Text Box – [buyer number], [purchase order amount] Where – (empty) Auditing data on Excel worksheets Page 110
- 140. Audit Commands Results A list of the results of pattern matching for all buyers. The list is in descending order, first showing the buyer whose pattern is the most different. Note: The transactions do not need to be “pre-sorted”. Usage Example 2 A test is to be performed for usage of round numbers in general journal entries by the person preparing the journal entry. The column name for the journal entry preparer is “preparer”. Approach – using the “patternrn” command, check the journal entries. Audit Command values Column value – [journal amount] Text Box – [preparer], [journal amount] Where – (empty) Results A list of the results of pattern matching for all preparers. The list is in descending order, first showing the preparer whose pattern is the most different. Usage Example 3 A test is to be performed for usage of round numbers in fixed asset costs by location. Pattern analysis using round numbers Auditing data in Excel Page 111 worksheets
- 141. Audit Commands Output results Pattern analysis using round numbers Auditing data on Excel worksheets Page 112
- 142. Audit Commands Output results (pasted into Excel work sheet) Key d-stat Chi Square XSF 2.07E-02 6.085982622 AB 2.26E-02 4.260527481 GHF 1.39E-02 1.830195487 FGT 9.12E-03 1.659130377 JHT 9.19E-03 0.747565059 PA 6.26E-02 0.534411792 ABC 2.04E-03 0.500401568 PE 6.26E-02 0.400815832 EFR 1.91E-02 0.392424534 NC 6.26E-02 0.267215216 DSR 1.83E-03 0.162121923 MI 6.26E-02 0.13360994 CF 6.26E-02 0.13360994 This report indicates that the location coded “XSF” is the most different from all other locations as measured by the usage of round numbers. Output results Auditing data in Excel Page 113 worksheets
- 143. Audit Commands 4.4.2 Data Stratification Pattern analysis using data stratification An example will best illustrate the concept of pattern testing using stratification. Consider a case where inventory is being taken at the end of each month at separate warehouse locations. Unless the warehouses have a significantly different “mix” of items, a stratification of the inventory values by item will generally follow the same pattern of counts and values. Although somewhat tedious, the auditor could stratify the amounts manually and then visually compare the results. For example, one warehouse might have a much larger number of low (or high) value items than the others. Certainly this could be a valid situation, but it might also represent an error as well. Pattern testing is based upon the overall concept outlined above. The procedure first obtains counts or totals for the entire transaction population. Then the procedure separates the population based upon criteria specified by the auditor (in the example above warehouse) and then systematically compares each subgroup with the overall population. The system then reports each group based upon how different it is from the overall population as measured by the statistical test “Chi Square”. Usage Example 1 In an audit of inventory, the inventory values are known to be clustered in a certain pattern. Approximately 20% of all inventory items have a value under $100. Then 50% have a value under $200 and 80% have a value under $500. The stratification ranges used to obtain these results were the bin values of 0, 100, 200, 500 A test is to be made to identify the warehouse location which has inventory value which are the most different from this pattern as measured using data stratification and the bin values above, Approach – using the “Pattern - stratification” command, analyze the inventory values. . Audit Command values Column value – [unit cost] Text Box – [location],[unit cost], 0, 100, 200, 500 Where – (empty) Results A list, by location, of the measures of the difference between the values at that location and Auditing data on Excel worksheets Page 114
- 144. Audit Commands those of the entire population, as measured using Chi Square. The list is in descending order. Output results Pattern analysis using data stratification Auditing data in Excel Page 115 worksheets
- 145. Audit Commands Output results (pasted into Excel work sheet) Key d-stat Chi Square ABC 1.26E-03 79.32112 DSR 4.17E-02 58.98408 GHF 2.17E-02 58.02021 JHT 0.079345 57.38157 NC 0.216289 56.3838 AB 2.65E-02 55.07401 FGT 2.73E-02 52.50759 PA 0.230438 51.2955 EFR 6.31E-02 51.25032 PE 0.216289 48.51521 XSF 4.50E-02 47.23957 CF 0.35716 46.83188 MI 0.429626 46.68186 This report indicates that, based upon data stratification, location ‘ABC’ has the largest variance between the results of the data stratification at that location and that of all locations combined. Output results Auditing data on Excel worksheets Page 116
- 146. Audit Commands 4.4.3 Day of Week Pattern analysis using day of week An example will best illustrate the concept of pattern testing by day of week. Consider a case for the retail environment. Generally, sales tend to be concentrated on Fridays, Saturdays and Sundays, with much lesser amounts on say Monday and Tuesday. If the auditor is looking at a group of locations (stores), then this test can identify which stores have sales patterns that are the most statistically different, as measured using standard statistical tests. Although differences in patterns may be explainable, they may also result from errors. Alternative tests can be performed using month of year instead of store location, etc. Usage Example 1 In an audit of revenue in a retail environment, determine which store’s revenue was the most different, based upon analysis by day of week. Approach – using the “patternwd” command, analyze such transactions. Audit Command values Column value – [trans date] Text Box – [store number],[transdate] Where – (empty) Results A listing of summary results, by store location, in descending order Usage Example 2 In an audit of journal entries, determine which account’s postings were the most different, based upon the day of the week they were posted. Approach – using the “patternwd” command, analyze such transactions. Audit Command values Column value – [ posting date] Text Box – [account number], [posting date] Where – (empty) Results Auditing data in Excel Page 117 worksheets
- 147. Audit Commands A listing of summary results, by account number, in descending order In the example below, a test was performed on asset acquisitions, by day of week. Output results Pattern analysis using day of week Auditing data on Excel worksheets Page 118
- 148. Audit Commands Output results (pasted into Excel work sheet) Key d-stat Chi Square ABC 1.26E-03 79.32112 DSR 4.17E-02 58.98408 GHF 2.17E-02 58.02021 JHT 0.079345 57.38157 NC 0.216289 56.3838 AB 2.65E-02 55.07401 FGT 2.73E-02 52.50759 PA 0.230438 51.2955 EFR 6.31E-02 51.25032 PE 0.216289 48.51521 XSF 4.50E-02 47.23957 CF 0.35716 46.83188 MI 0.429626 46.68186 Output results Auditing data in Excel Page 119 worksheets
- 149. Audit Commands 4.4.4 Holidays Pattern analysis using holidays An example will best illustrate the concept of pattern testing by holiday. Consider a case for the retail environment. In some cases, sales tend to be concentrated on certain holidays. If the auditor is looking at a group of locations (stores), then this test can identify which stores have sales patterns that are the most statistically different, as measured using standard statistical tests. Although differences in patterns may be explainable, they may also result from errors. Alternative tests can be performed using month of year instead of store location, etc. Usage Example 1 In an audit of revenue in a retail environment, determine which store’s revenue was the most different, based upon analysis by sales on holidays. Approach – using the “patternhol” command, analyze such transactions. Audit Command values Column value – [trans date] Text Box – [store number],[transdate] Where – (empty) Results A listing of summary results, by store location, in descending order Usage Example 2 In an audit of journal entries, determine which account’s postings were the most different, based postings made on holidays. Approach – using the “patternhol” command, analyze such transactions. Audit Command values Column value – [ posting date] Text Box – [account number], [posting date] Where – (empty) Results A listing of summary results, by account number, in descending order Auditing data on Excel worksheets Page 120
- 150. Audit Commands In the example below, a test was performed on asset acquisitions made on a holiday. Output results Pattern analysis using holidays Auditing data in Excel Page 121 worksheets
- 151. Audit Commands Output results (pasted into Excel work sheet) Key d-stat Chi Square DSR 1.29E-02 10.68799 EFR 2.30E-02 9.871974 GHF 2.07E-02 6.070443 AB 7.69E-03 4.901453 FGT 9.57E-03 3.471517 JHT 2.50E-02 1.765012 ABC 2.82E-03 1.366258 XSF 2.50E-02 1.330289 PA 2.50E-02 0.10236 PE 2.50E-02 7.68E-02 NC 2.50E-02 5.12E-02 MI 2.50E-02 2.56E-02 CF 2.50E-02 2.56E-02 Output results Auditing data on Excel worksheets Page 122
- 152. Audit Commands 4.4.5 Benford’s Law Pattern analysis using Benford’s Law Many accounting transaction amounts will tend to follow that expected using Benford’s law unless there is a compelling reason that they should not (e.g. upper or lower transaction limits, recurring amounts, etc.). The pattern test for Benford’s law separates the population into groups and then computes the expected and observed values using Benford’s law for that group. An example might be inventory counts taken at various warehouses. Inventory counts should conform with that expected using Benford’s Law. By applying a pattern test by warehouse, it is possible to identify which warehouse had inventory counts that differed the most from that expected using Benford’s law. Usage Example 1 In an audit of expense reports, a test is to be made to determine which employee’s expense reports were the most different from all other expense reports, based upon Benford’s Law. Approach – using the “patternben” command,analyze expense report transactions. Audit Command values Column value – [expense amount] Text Box – [employee number], [expense amount], F1 Where – (empty) Results A listing of summary results, by employee number, in descending order Usage Example 2 In an audit of inventory counts, a test is to be made to determine which inventory counts were the most different from all other warehouse locations , based upon Benford’s Law. Approach – using the “patternben” command, analyze inventory count transactions. Audit Command values Column value – [inventory count] Text Box – [warehouse], [inventory count], F1 Where – (empty) Auditing data in Excel Page 123 worksheets
- 153. Audit Commands Results A listing of summary results, by warehouse, in descending order In the example below, the test was performed using cost amounts at various locations. The Benford’s Law test was for first digit, F1. Output results Stop and Go Attribute sampling Auditing data on Excel worksheets Page 124
- 154. Audit Commands Output results (pasted into Excel work sheet) Key d-stat Chi Square ABC 0.257636 520.197645 DSR 0.309237 62.94220806 GHF 0.28177 45.65568845 AB 0.23824 41.67393551 FGT 0.346857 36.99397174 JHT 0.317603 16.02484576 XSF 0.3 12.12429792 EFR 0.275362 5.825805153 PE 0 0 PA 0 0 NC 0 0 MI 0 0 CF 0 0 The report indicates that, as measured using Benford’s Law, location ‘ABC’ is the most different from the population as a whole. Output results Auditing data in Excel Page 125 worksheets
- 155. Audit Commands 4.5 Sampling 4.5.1 Attributes – Unrestricted: Stop and Go Compliance testing often relies on attribute sampling when a test is to be based upon a random sample. If segments of a population are expected to have significantly different rates of compliance for a tested procedure, then stratified attribute sampling maybe appropriate. If not, then unrestricted sampling will be better. If the supporting documents for data being audited are contained in a central location, e.g. no travel or other logistics are involved, then stop and go sampling may be a more efficient and effective method for random sampling for the following reasons: 1. There is no need to compute a required sample size, 2. There is no need to perform a preliminary analysis of the population attributes such as expected error rate, and 3. There is little or no risk in "over sampling", i.e. testing more samples than required and therefore spending excess audit time doing the testing. Stop and Go sampling is a statistically valid process which involves the following steps: 1. Assign a random number to each item in the population (e.g. using "Mersenne Twister" or other statistically valid random number generator) 2. Sort the population by assigned random number, either ascending or descending 3. Select the first 10 - 20 items (auditor judgment as to number), test them and put the results into an Excel spreadsheet. 4. Run a "stop and go" sample report and review the results (see example below) Auditing data on Excel worksheets Page 126
- 156. Audit Commands 5. If the resulting sample precision is too large, then select another group of transactions by sorted assigned random number (auditor judgment as to number) 6. Test the samples and record the results in the same Excel spreadsheet. 7. Run another "stop and go" sample an review the results. 8. Repeat steps 5 through 7 until satisfactory results have been obtained. The report from the Stop and Go Sample will show the intermediate results, sample statistics as well as calculate the estimate of the population at four confidence levels - 80%, 90%, 95% and 98%. The results will also be charted for easy review. The charts show the upper and lower bounds, as well as the point estimate for each calculation. An example of the chart output is shown below (attribute test for signature on documents as tested in 25 samples): Auditing data in Excel Page 127 worksheets
- 157. Audit Commands Figure 14 – Attribute sampling The chart above presents the results of the attribute sample test visually for four confidence levels as follows: 1. 80% confidence the rate is between approximately .015 and .021 2. 90% confidence the rate is between approximately .014 and .022 3. 95% confidence the rate is between approximately .013 and .025 4. 98% confidence the rate is between approximately .0125 and .024 Note: As the confidence level increases, the bands widen. Stop and Go Attribute sampling Auditing data on Excel worksheets Page 128
- 158. Audit Commands How the results are calculated: The upper limit is computed using the following formula (assumes a confidence level of 90%): The lower limit is computed using a similar formula: These formula are based upon the article in The American Statistician: Auditing data in Excel Page 129 worksheets
- 159. Audit Commands Output results Stop and Go Attribute sampling Auditing data on Excel worksheets Page 130
- 160. Audit Commands Output results (pasted into Excel work sheet) Sampling results: Sample size 82 Errors 5 Error rate 6.10% Population size 5713 Confidence used 95.00% Z-score 1.95996 Point estimate: 348 Lower limit 116 Upper Limit 777 Confidence used 98.00% Lower limit 93 Upper Limit 865 Confidence used 90.00% Lower limit 141 Upper Limit 705 Confidence used 80.00% Lower limit 172 Upper Limit 627 Output results Stop and Go Attribute Output results (chart) The chart below was specified using a custom color scheme and the title shown. These values are provided using the “Chart” tab on the processing form. Auditing data in Excel Page 131 worksheets
- 161. Audit Commands Output results - chart Auditing data on Excel worksheets Page 132
- 162. Audit Commands 4.5.2 Variable Sampling – Unrestricted Stop and Go Monetary amounts can be estimated using stratified sampling, especially if the population can be divided into strata which have less variability. There are techniques for optimizing the selection of sample size, such as Neyman's allocation method. If the supporting documents for data being audited are contained in a central location, e.g. no travel or other logistics are involved, then stop and go sampling may be a more efficient and effective method for random sampling for the following reasons: 1. There is no need to compute a required sample size, 2. There is no need to perform a preliminary analysis of the population attributes such as expected error rate, and 3. There is little or no risk in "over sampling", i.e. testing more samples than required and therefore spending excess audit time doing the testing. Stop and Go sampling is a statistically valid process which involves the following steps (but note that it does not comply with the proposed SAS 39): 1. Assign a random number to each item in the population (e.g. using "Mersenne Twister" or other statistically valid random number generator) 2. Sort the population by assigned random number, either ascending or descending 3. Assign a strata number to each transaction in the population (typically based upon a numeric range of values). 4. Obtain a suggested sample allocation based upon Neyman's allocation (or other method logy) 5. Select the first 10 - 20 items (auditor judgment as to number), test them and put the results into an Excel spreadsheet. 6. Run a "stop and go" sample report and review the results (see example below) 7. If the resulting sample precision is too large, then select another group of transactions by sorted assigned random number (auditor judgment as to number) 8. Test the samples and record the results in the same Excel spreadsheet. 9. Run another "stop and go" sample an review the results. 10. Repeat steps 5 through 7 until satisfactory results have been obtained. The report from the Stop and Go Sample will show the intermediate results, sample statistics as well as calculate the estimate of the population at four confidence levels - 80%, 90%, 95% and 98%. The results will also be charted for easy review. The charts show the upper and lower bounds, as well as the point estimate for each calculation. Auditing data in Excel Page 133 worksheets
- 163. Audit Commands An example of the chart output is shown below (variable test of 14 samples): Figure 15 – Variable sampling The chart above presents the results of the variable sample test visually for four confidence levels as follows: 1. 80% confidence the true population amount is between approximately $110,000 and $218,000 2. 90% confidence the true population amount is between approximately $95,000 and $230,000 3. 95% confidence the true population amount is between approximately $81,000 and $241,000 4. 98% confidence the true population amount is between approximately $67,000 and $259,000 Usage Example 1 Stop and Go Variable sampling Auditing data on Excel worksheets Page 134
- 164. Audit Commands The formula used for variable sampling is as follows: The standard deviation is computed using the following formula: The standard error of the mean is The total standard error is The confidence interval is computed using the Student’s T-value as computed using the “Cephes” software (U.S. Department of Energy). Auditing data in Excel Page 135 worksheets
- 165. Audit Commands Output results Stop and Go Variable sampling Auditing data on Excel worksheets Page 136
- 166. Audit Commands Output results (pasted into Excel work sheet) Sampling results: Sample size 71 Sample mean 563.29 Sample Std Dev 224.98 Population size 5713 Point estimate: 3,218,048.41 Values at 95% confidence 5713 t-value used 1.99444 Lower limit 2,915,688.41 Upper Limit 3,520,408.42 t-value 1.99444 Values at 98% confidence 5713 Lower limit 2,857,114.01 Upper Limit 3,578,982.81 t-value 2.38081 Values at 90% confidence 5713 Lower limit 2,965,341.39 Upper Limit 3,470,755.44 t-value 1.66691 Values at 80% confidence 5713 Lower limit 3,021,911.79 Upper Limit 3,414,185.03 t-value 1.29376 Output results Variable Sampling – Unrestricted Stop and Go Output results (chart) The chart below was specified using a custom color scheme and the title shown. These values are provided using the “Chart” tab on the processing form. Auditing data in Excel Page 137 worksheets
- 167. Audit Commands Output results - chart Auditing data on Excel worksheets Page 138
- 168. Audit Commands 4.5.3 Stratified Variable Sampling – Population Stratified Variable Sampling One of the first steps in performing a stratified variable sample is a determination of the composition of each strata, including its variability, etc. With this information it is then possible to perform either a 1) proportional sample or 2) a disproportionate sample. Generally, auditors will select a disproportionate sample, as typically the population will not be consistent, and thus the sampling should be concentrated in those strata which have the most variability. There is a formula which can be used to determine the optimal counts for sampling, which is referred to as “Neyman’s allocation”. The purpose of the stratified variable population command is to assess the population values by strata and suggest a sample plan based upon Neyman’s allocation, i.e. a disproportionate stratified sample. The formula used are as follows: The estimate of the universe mean: Estimate of universe total: Estimate of the variance of each strata Variance of the entire population: Auditing data in Excel Page 139 worksheets
- 169. Audit Commands A 95% confidence interval for the entire population is The “z-score” is computed using the inverse normal function of the Cephes software (US DOE). Neyman’s allocation is calculated using the following formula: For purposes of the calculation, the costs of sampling ( c sub I and c sub h) are assumed to be uniform. Output results Stratified Variable Sampling Auditing data on Excel worksheets Page 140
- 170. Audit Commands Output results (pasted into Excel work sheet) Strata Count Mean Standard Deviation Total Amount 1 345 47.77 28.3 16,481.44 2 337 140.64 35.34 47,394.01 3 696 281.6 72.74 195,996.05 4 1431 480.8 79.45 688,031.46 5 2213 580.69 111.68 1,285,068.46 6 691 841.77 149.38 581,664.24 All 5713 492.67 N/A 2,814,635.66 Neyman Allocation report Strata N Std Amt Pct Samp Size Next 1 345 28.3 9,763.58 1.82% 1 -344 2 337 35.34 11,908.41 2.22% 1 -336 3 696 72.74 50,630.47 9.44% 3 -693 4 1431 79.45 113,690.49 21.20% 6 -1,425 5 2213 111.68 247,139.50 46.08% 14 -2,199 6 691 149.38 103,222.96 19.25% 6 -685 The first part of the report simply lists the basic statistics for each strata, as exist in the data being analyzed. The second report is the suggested sampling counts using the Neyman allocation and the total number of items to be sampled (in this example 30). Output results Auditing data in Excel Page 141 worksheets
- 171. Audit Commands 4.5.4 Stratified Variable Sampling – Assessment Stratified Variable Sampling Assessing the results of stratified variable sampling. The stratified variable assessment command extrapolates the results of the sample to the entire population. For each strata, the basic statistics of the strata are shown, along with the point estimate, and upper and lower confidence limit using the precision specified. An example of the command is shown below, where: Stratum is the name of the column containing the stratum identifier Audited is the name of the column containing the audited value Selected is the name of the column containing the indicator as to whether the particular row was sampled. This will contain an “X” is the row was selected for sampling. The command in the text box is as follows: Audited, stratum, selected, 30, .95 The “30” value used in the command is used to request Neyman’s allocation values for a total sample size of 30. This value does not affect any of the computations, only provide information to be used in the selection of the next sample. The “.95” is the precision to be used in determining the confidence levels. Output results Auditing data on Excel worksheets Page 142
- 172. Audit Commands Stratified Variable Sampling Output results (pasted into Excel work sheet) Strata N n Mean Standard Deviation Estimate Lower Limit Upper Limit Point 1 345 2 59.49 0 20,524.05 20,524.05 20,524.05 2 337 2 113.4 0 38,214.12 38,214.12 38,214.12 3 696 7 275.13 67.54 191,488.49 99,353.24 283,623.74 4 1431 15 499.98 42.45 715,477.10 596,425.31 834,528.90 5 2213 32 584.25 117.82 1,292,936.26 781,887.74 1,803,984.78 6 691 13 886.61 65.83 612,649.10 701,798.37 523,499.84 All 5713 71 563.29 80.78 3,218,048.41 2,313,501.12 4,122,595.70 Neyman Allocation report Strata N Std Amt Pct Samp Size Next 1 345 28.3 9,763.58 1.82% 1 -344 2 337 35.34 11,908.41 2.22% 1 -336 3 696 72.74 50,630.47 9.44% 3 -693 4 1431 79.45 113,690.49 21.20% 6 -1,425 5 2213 111.68 247,139.50 46.08% 14 -2,199 6 691 149.38 103,222.96 19.25% 6 -685 Output results Auditing data in Excel Page 143 worksheets
- 173. Audit Commands 4.5.5 Stratified Attribute Sampling – Population Stratified Attribute Sampling The stratified attribute population command simply prepares a schedule showing the number of items to be tested within each stratum. Such information provides the auditor a basis for making further decisions as to the composition of the samples to be tested. The data values do not have be sorted by strata. Also, although the strata identifiers shown here are numeric, the strata identifiers may have any value. Each unique value will result in a separate strata for sample testing. Usage Example 1 In the example below, the attribute to be tested is identified as “audited”. The name of the column containing the strata identifier is “stratum” and the name of the column indicating whether the value in the row is to be sampled and tested is named “Selected”. Each value selected for sampling is indicated by placing an “X” in the column labeled “selected” (or other name chosen). For attribute sampling, the audited value will be non-blank if the attribute being tested is found to exist. All this is illustrated in a very simple example below: Row Signature Selected Strata 1 A 2 X B 3X X C 4 A 5 B The data being tested consists of five rows, separated into three strata “A”, “B” and “C”. Only rows 2 and 3 have been selected for sampling. The attribute being tested is a signature on a document. The record for row 2 has a signature, the record for row 3 does not. Auditing data on Excel worksheets Page 144
- 174. Audit Commands Output results Stratified Attribute Sampling Output results (pasted into Excel work sheet) Strata Count 1 594 2 583 3 1132 4 863 5 1399 6 1142 All 5713 Output results Auditing data in Excel Page 145 worksheets
- 175. Audit Commands Auditing data on Excel worksheets Page 146
- 176. Audit Commands 4.5.6 Stratified Attribute Sampling – Assessment Stratified Attribute Sampling The stratified attribute assessment command uses the sample results to extrapolate the results to each strata and in total. For each stratum, the point estimate, as well as upper and lower limits are listed. The data values do not have be sorted by strata. Also, although the strata identifiers shown here are numeric, the strata identifiers may have any value. Each unique value will result in a separate strata for sample testing. The command below prepares an extrapolation based upon attribute sampling. The name of the column containing the stratum identifier is “stratum”, the name of the column containing the results of the test of the attribute is called “audited”, and the name of the column indicating if the row was selected for sampling is called “selected”. The confidence level desired for the results is 97%. This the command in the text box is: Stratum, audited, selected, .97 Note: By default, results at the three confidence levels – 80%, 90% and 95% are produced. An additional confidence level may be specified. Output results Auditing data in Excel Page 147 worksheets
- 177. Audit Commands Stratified Attribute Sampling Output results (pasted into Excel work sheet) Stratified Attribute Report Prepared: 11-12-09 10:45:59 Stratum Sample Items Ratio Universe Projected 1 17 3 17.65% 594 105 2 17 1 5.88% 583 34 3 12 1 8.33% 1132 94 4 12 0 0.00% 863 0 5 12 0 0.00% 1399 0 6 12 0 0.00% 1142 0 Combined 82 5 4.09% 5713 233 Strata Prec 80% Prec 90% Prec 95% Prec 97.3% 1 12.04% 15.45% 18.41% 20.77% 2 7.43% 9.53% 11.36% 12.82% 3 10.62% 13.63% 16.25% 18.33% 4 0.00% 0.00% 0.00% 0.00% 5 0.00% 0.00% 0.00% 0.00% 6 0.00% 0.00% 0.00% 0.00% Lower limit quantity 87 45 9 5 Lower limit percent 1.52% 0.80% 0.17% 0.09% Upper limit quantity 380 421 457 486 Upper limit percent 6.65% 7.38% 8.01% 8.51% Output results Auditing data on Excel worksheets Page 148
- 178. Access Databases and Excel Workbooks 5 Access Databases and Excel Workbooks 5.1 Overview The procedure for working with data contained in Access databases and Excel workbooks is almost identical to that for working with data which has been “pasted” from the Clipboard, with two exceptions: • The name of the Access database or Excel workbook must be provided • In the case of Excel, the name of the worksheet must be provided, or • In the case of Access, the name of the table or query must be provided. All this information is provided using a form and drop down lists. The rest of the information (e.g. column names, textbox information and “where” information is identical. Auditing data on Excel worksheets Page 149
- 179. Access Databases and Excel Workbooks Audit Commands 5.2 The “Excel/Access” menu item The input form is contained under the “MS” tab shown below. The processing consists of the following seven steps: 1. Select the file name by clicking on the “File” button 2. Select the Sheet name by clicking on the item in the drop down list. In the case of Excel this will be the sheet names contained in the workbook. In the case of Access it will be the tables and queries contained within the Access database 3. Once the sheet name has been selected, click on the column name to select the information to be processed 4. Select the command to be processed from the menu 5. If applicable, enter any criteria to be used in narrowing the processing “Where” (Note: to obtain help, click the label “Where?” to bring up a help form) 6. If required, enter any information in “Info” box. Note that a help description is displayed on the status bar to assist. 7. Click the “Run” button Auditing data on Excel worksheets Page 150
- 180. Access Databases and Excel Workbooks Audit Commands 5.3 An example To illustrate the process, the auditor wishes to extract information from a worksheet named “FA” in a workbook named EWP.xls to identify fixed asset records where the fixed asset may have been over depreciated. Below is the process, step by step. Step 1 – select the file The last used directory is shown and the Excel work book named fa.xls is selected. Step 2 – select the work sheet Auditing data in Excel Page 151 worksheets
- 181. Access Databases and Excel Workbooks Audit Commands Step 3 – select the column name of interest Step 4 – select the command name to be processed Auditing data on Excel worksheets Page 152
- 182. Access Databases and Excel Workbooks Audit Commands Step 5 – specify selection criteria (if any) In this example, only the information for the location ‘ABC’ is needed. Step 6 – provide any additional information required for command processing Auditing data in Excel Page 153 worksheets
- 183. Access Databases and Excel Workbooks Audit Commands In this example, no additional information is required. Step 7 – click “Run” When the button labeled “Run” is clicked, the results are written out as a text file report and as a chart to the directory specified under the “Audit” tab. Auditing data on Excel worksheets Page 154
- 184. Access Databases and Excel Workbooks Audit Commands 5.4 Working with text files The procedure for working with text files is almost identical to that for working with data which has been “pasted” from the Clipboard, with two exceptions: • The name of the directory containing the text file must be provided • The name of the text file included within the directory must be specified All this information is provided using a form and drop down lists. The rest of the information (e.g. column names, textbox information and “where” information is identical. 5.5 The “File” tab The input form is contained under the “File” tab shown below. Auditing data in Excel Page 155 worksheets
- 185. Access Databases and Excel Workbooks Audit Commands The processing consists of the following seven steps: 1. Select the name of the directory by clicking the “Folder” button 2. Select the file name by clicking on the name in the drop down list. 3. Once the file name has been selected, click on the column name to select the information to be processed 4. Select the command to be processed from the menu 5. If applicable, enter any criteria to be used in narrowing the processing “Where” (help is available by clicking the label “Where?”) 6. If required, enter any information in “Info” box. Note that a help description is displayed on the status bar to assist. 7. Click the “Run” button 5.6 An example To illustrate the process, the auditor wishes to analyze information from a text file named “FA.txt” in the directory “c:testdata” to identify fixed asset records where the fixed asset may have been over depreciated. Below is the process, step by step. Step 1 – select the directory Auditing data on Excel worksheets Page 156
- 186. Access Databases and Excel Workbooks Audit Commands The last used directory is shown and the Excel work book named fa.xls is selected. Step 2 – select the file Step 3 – select the column name of interest Auditing data in Excel Page 157 worksheets
- 187. Access Databases and Excel Workbooks Audit Commands Step 4 – select the command name to be processed Step 5 – specify selection criteria (if any) In this example, only the information for the location ‘ABC’ is needed. Auditing data on Excel worksheets Page 158
- 188. Access Databases and Excel Workbooks Audit Commands Step 6 – provide any additional information required for command processing In this example, no additional information is required. Step 7 – click “Run” When the button labeled “Run” is clicked, the results are written out as a text file report and as a chart to the directory specified under the “Audit” tab. Auditing data in Excel Page 159 worksheets
- 189. Access Databases and Excel Workbooks Audit Commands 6 Techniques for “Drill Down” Drilling down to information of interest is enabled through the use of the “Where” information. A separate tab is provided in order to enter the information if it is lengthy or complex. Note: This form can also be shown by clicking on the label “Where?”. The form is displayed. Auditing data on Excel worksheets Page 160
- 190. Access Databases and Excel Workbooks Audit Commands There are numerous examples of possible “where” clauses. To help, there is a drop down list of examples which can be selected and then tailored to specific uses. In the screen above, the auditor wishes to extract information within the last 30 days. The example shown provides a mean to do this. All that needs to be done now is to change the name of the column to one that is of interest (unless the column of interest is named “acquisition”). Below are tables which provide examples of some of the functions with a brief description. More complex criteria can be applied using combinations of the functions or “nesting” which is described below. Auditing data in Excel Page 161 worksheets
- 191. Access Databases and Excel Workbooks Audit Commands 6.1 Numeric Function Example Description Numeric equality [asset cost] = 1000 Asset cost is exactly 1,000 Greater than [asset cost] > 1000 Asset cost is greater than 1,000 Less than [asset cost] – Net Asset cost is less than 1,000 [accumulated depreciation]< 1000 Greater or equal [asset cost] >= 1000 Asset cost is greater than or equal Less than or equal [sales amount] * .04 <= 10 Tax amount at 4% is less than or equal to 10 Not equal [asset cost] <> 1000 Asset cost is not 1,000 Mod [asset cost] mod 10 – 2 Asset cost ends in 2 Mod [asset cost] mod 100 – 0 Evenly divisible by 100 Abs Abs([asset cost] – 100) <= Asset cost is within $.02 of 100, i.e. .02 99.98 – 100.02 Rnd Rnd() A random number Is numeric Isnumeric Isnumeric(amount) = -1 Round Round(cost,2) Round the cost to the penny 6.2 Text Function Example Description Length Len(location) = 6 Length of location name is six characters Mid Mid(location,2,3) Character positions 2 3 and 4 Left Left(location,2) = ‘AB’ Left most two characters Right Right(location,2) = ‘XY’ Location name ends in XY Instr Instr(location,’test’) > 0 Location contains the text ‘test’ LCase Lcase(lastname) = “smith’ Lower case value for last name Ucase Ucase(lastname) = Upper case values ‘SMITH’ Trim Trim(lastname) = ‘smith’ Remove left and right blanks Ltrim Ltrim(lastname) Remove blanks on the left Rtrim Rtrim(lastname) Remove blanks on the right Auditing data on Excel worksheets Page 162
- 192. Access Databases and Excel Workbooks Audit Commands 6.3 Date / Time Function Example Description Hour Obtain hour portion of Length of location name is six characters date/time value minute Obtain minute portion of Character positions 2 3 and 4 date/time value second Obtain second portion of Left most two characters date/time value year Obtain yearr portion of Location name ends in XY date/time value month Obtain month portion of Location contains the text ‘test’ date/time value day Obtain day portion of date/ Lower case value for last name time value Weekday Day of week 1 – 7 Weekday(datevalue) = 1 (check for Sunday) Date validity Isdate(datecol) = -1 Check for an invalid date Difference between DateDiff(‘d’,date1,date2) Measure difference between dates in dates days Date arithmetic add DateAdd(‘d’,5,DateValue) Add five days to the date value Date arithmetic add DateAdd(‘m’,3,DateValue) Add three months to the date Date Part DatePart(‘m’,DateValue) Obtain the month Date Part DatePart(‘y’,dateValue) Obtain the year Auditing data in Excel Page 163 worksheets
- 193. Access Databases and Excel Workbooks Audit Commands 6.4 Logical tests Function Example Description OR Cost < 100 or life > 7 Test that at least one of the conditions is true AND Cost < 100 and life > 7 Test that both conditions are true NOT Obtain second portion of Left most two characters date/time value BETWEEN Trandate between Values between a date range #7/1/2005# and #6/30/2006# BETWEEN Amount between 100 and Value between 100 and 900 900 BETWEEN Location between ‘AB’ Value between ‘AB’ and ‘LM” and ‘LM” IN Location Value is one of three specified values in(‘103’,’105’,’106’) LIKE Location like ‘10%’ Location name starts with 10 6.5 Combinations Functions can be combined using the logical tests described in section 7.4. For example, to test asset records acquired during a specific fiscal period which also have useful lives exceeding ten years the criteria would be specified as follows using the “AND” connectior: ([installation date] between #7/1/2007# and #6/30/2008#) and ([useful life] > 7) 6.6 Nesting functions Often several functions need to be applied at the same time. For example to test if the first three letters of the last name are ‘Bla’, without considering case the following criteria would be applied: Auditing data on Excel worksheets Page 164
- 194. Access Databases and Excel Workbooks Audit Commands Ucase(left([last name],3)) = ‘BLA’ If the last name may also have blanks to the right of the last character, then an additional function (“trim”) could be first applied before the remaining tests: Ucase(left(trim([last name]),3)) = ‘BLA’ 6.7 Selection criteria There are at least three separate techniques for the identification of ranges or multiple values: 1. Between 2. In 3. Like The between operator allows the specification of a range of values which may be text, numeric or date – e.g. Between #7/1/2007# and #6/30/2008# Between ‘A’ and ‘M’ Between 100 and 2000 The in operator allows the specification of a number of text values, each separated by a comma, e.g. to test if a specific state code has been located: [State Code] in (‘FL’,’GA’,’AL’,’NC’) The like operator allows tests for patterns. Operator Meaning [last name] like ‘BLA%’ Last name starts with ‘BLA’ Auditing data in Excel Page 165 worksheets
- 195. Access Databases and Excel Workbooks Audit Commands Auditing data on Excel worksheets Page 166
- 196. Access Databases and Excel Workbooks Audit Commands 7 Appendix – Software installation Installation of the software is a straightforward process, using the standard “Setup.exe” method. There are two types of installs: 1. “regular” install 2. “silent” install For a “silent” install, the software is installed with all the default values – no interaction is required. This section of the guide will discuss the “regular” install. Double clicking the file “ACSetup.exe” brings up the splash screen asking if you wish to install the Audit Commander. Auditing data in Excel Page 167 worksheets
- 197. Access Databases and Excel Workbooks Audit Commands Step 1 Step 2 Step 3 Auditing data on Excel worksheets Page 168
- 198. Access Databases and Excel Workbooks Audit Commands Step 4 Step 5 Auditing data in Excel Page 169 worksheets
- 199. Access Databases and Excel Workbooks Audit Commands Step 6 Step 7 Auditing data on Excel worksheets Page 170
- 200. Access Databases and Excel Workbooks Audit Commands Step 8 Auditing data in Excel Page 171 worksheets
- 201. Access Databases and Excel Workbooks Audit Commands Auditing data on Excel worksheets Page 172
- 202. Access Databases and Excel Workbooks Audit Commands 8 Comment Form Windows version ______________________________ Audit Commander version _______________________ Functions described ____________________________ Comments Please send any comments, suggestions or items identified as errors to: Mike.Blakley@ezrstats.com Although I am not able to respond to all such comments and suggestions, I will try to do so as feasible. Registered users of Audit Commander will be notified as revised versions of the manual are released. Auditing data in Excel Page 173 worksheets