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Your AP Data is Telling
You Something
Five Analytics to Identify Duplicate
Payments and Other Irregularities
2 ©2022 Crystallize Analytics. All rights reserved.
Who Are We?
In 2007 eprentise was founded
on its original product, FlexField
 Enables customers to make
unprecedented changes to their financial
chart of accounts while maintaining
transactional history and data integrity.
In 2009 we introduced our Consolidation,
Divestiture, and Reorganization products
 Transformational software which can copy, change, filter, or
merge all elements of Oracle EBS financial systems to
address ever-changing business needs, such as regulatory
compliance and growth opportunities.
In 2020 we began expanding to new markets with our
C Collection analytics suite, and our Audit Automation software
 C Collection analytics provides transparency
and identifies potential problem areas with
transactional data. This allows users to
reduce costs, leverage opportunities across
the enterprise, improve business processes,
and increase the confidence level of the users
in their data, processes, and operations.
Transformation to Optimization
One-time usage to subscription model
 Automated Audit provides finance teams with
drill-down data from a balance sheet report into
the transaction-level detail. The software covers
hundreds of substantive procedures for the
entire enterprise domain and builds in consistent
audit processes and workflows across the
organization.
3 ©2022 Crystallize Analytics. All rights reserved.
 Learn Benford’s analysis to identify
manipulated or adjusted invoice amounts
 Create a periodic (annual/monthly) flux
report to identify unusual or altered vendor
purchasing patterns
 Learn three rule of thumb analytics to
identify unusual vendor billings and
payments
Learning Objectives
4 ©2022 Crystallize Analytics. All rights reserved.
 Standard & Data Driven Risk Assessment
 Getting AP Data Out of Your ERP
 Macro Analysis
 Benford’s Law Analysis
 Periodic Flux Test (Monthly, Annually)
 AP Analytics to Identify Wonky Invoices
 Duplicate Amounts Test
 Duplicate Amounts in the Same Month
 Invoice Number Format Test
 Duplicate Amounts with Duplicate Invoices
Agenda
5 ©2022 Crystallize Analytics. All rights reserved.
Standard Audit Risk and Control Assessment-
 Identify the critical business processes
Example: Procure to Pay
 Purchasing
 Accounts Payable
 Cash Disbursements
 Identify the risks (what could go wrongs)
 Identify a control that mitigates the risk
 Accounts payable
What could go wrong?
• A duplicate, incorrect, or fraudulent invoice could be
submitted and paid
• Control?
• ERP won’t allow vendor duplicate invoice numbers
Standard & Data Driven Risk Assessment
6 ©2022 Crystallize Analytics. All rights reserved.
Standard & Data Driven Risk Assessment
7 ©2022 Crystallize Analytics. All rights reserved.
Data Driven Audit Risk and Control Assessment-
 Interrogate the data to identify what is going wrong
Example: Procure to Pay
 Purchasing
 Accounts Payable
 Cash Disbursements
 Accounts payable
What did go wrong?
Let the data tell you.
Standard & Data Driven Risk Assessment
8 ©2022 Crystallize Analytics. All rights reserved.
 Two Comparable Time Periods (Year, Month)
 Required Key Data Fields
 Invoice Number
 Invoice Date
 Supplier Name and Supplier Number
 Invoice Amount
 Description
 Add a Year Column - year()
 Add a Month Column – month()
Getting AP Data Out of Your ERP
9 ©2022 Crystallize Analytics. All rights reserved.
Example Oracle EBS
10 ©2022 Crystallize Analytics. All rights reserved.
Example Oracle EBS
Trading Partner Supplier Num Supplier Site Name Invoice Date Invoice Num Invoice Amount Description
Advanced Network Devices 1013 SANTA CLARA-ERS 1-Jan-09 ERS-12289-190052 4,143,423.03 Receipt Invoice automatically created on 01-JAN-09
Erickson, Barry 8056 OFFICE 1-Jan-09 W37620 4,054.06 Sales Overview - Benefit Features
Erickson, Barry 8056 OFFICE 1-Jan-09 W35124 3,929.06 Sales Overview - Benefit Features
Eastern Industrial Products 5033 EIP MAIN 2-Jan-09 ERS-8538-183718 124,124.00 Receipt Invoice automatically created on 02-JAN-09
Lang, Inga R 8024 OFFICE 2-Jan-09 W11621 3,807.99 Financials Demonstration
Lewis, David 20059 OFFICE 2-Jan-09 W11630 6,773.00 Customer Conference
United Parcel Service 1003 UPS - HQ 2-Jan-09 OPS200939084 57,461.60
TT Services 5017 TT SAN FRAN 3-Jan-09 ERS-8537-183717 613,676.00 Receipt Invoice automatically created on 03-JAN-09
TT Services 5017 TT SAN FRAN 3-Jan-09 ERS-8539-183720 76,384.00 Receipt Invoice automatically created on 03-JAN-09
Building Management Inc. 2012 HQ - NYC 3-Jan-09 OPS200939085 144,467.76
Office Supplies, Inc. 1008 OFFICESUPPLIES 4-Jan-09 OPS200939086 27,374.56
Boise Cascade 5030 BC HQ 5-Jan-09 BC-012009 500
Industrial Dressler 2005 US HEADQUATERS 5-Jan-09 IND-012009 1,500.00
Consolidated Supplies 1014 SPRINGFIELD 5-Jan-09 OPS200939087 4,489.80
American Telephone and Telegraph 1005 AT&T - HQ 6-Jan-09 OPS200939088 65,262.76
McGwire, Patrick 20025 OFFICE 7-Jan-09 W11614 4,300.91 Management Meeting
Advanced Network Devices 1013 FRESNO 7-Jan-09 OPS200939089 110,342.18
Staples 5029 STAPLES LA 9-Jan-09 ERS-8544-183789 477,400.00 Receipt Invoice automatically created on 09-JAN-09
Bailey, Sara 8020 OFFICE 9-Jan-09 W11620 3,739.97 Team Meeting
Beckman, Lisa 20065 OFFICE 9-Jan-09 W11625 2,927.00 Regional Meeting
Brown, Casey 8022 OFFICE 9-Jan-09 W11618 3,426.69 Applications Demo - Kansas City
United Parcel Service 1003 UPS - HQ 9-Jan-09 OPS200939091 57,461.60
Building Management Inc. 2012 HQ - NYC 10-Jan-09 OPS200939092 144,467.76
Office Supplies, Inc. 1008 OFFICESUPPLIES 11-Jan-09 OPS200939093 27,374.56
11 ©2022 Crystallize Analytics. All rights reserved.
Purpose: To Identify Unusual Data Pattern in AP
that May Indicate Manipulation, Errors, or Other
Irregularities
“Briefly explained, Benford's Law maintains that the numeral 1
will be the leading digit in a genuine data set of numbers 30.1% of
the time; the numeral 2 will be the leading digit 17.6% of the time;
and each subsequent numeral, 3 through 9, will be the leading
digit with decreasing frequency. This expected occurrence of
leading digits can be illustrated as shown in the chart ‘Benford's
Law.’”
https://www.journalofaccountancy.com/issues/2017/apr/excel-and-benfords-law-to-detect-
fraud.html
Genuine data sets are driven by the tendency to purchase more
$1,000 items than $9,000 items. Real world purchases conform
closely to the Benford’s First Digit Expected Distribution. This is
true because it is harder to justify or gain permission to
purchase the larger dollar amounts.
Benford’s Law Analysis
12 ©2022 Crystallize Analytics. All rights reserved.
Benford’s Law- Setting Up the Analytic
Extract the First Digit
Analyze the 1st Digit Distribution Against the Benford’s Expectation
Conclusion- Some Variance- Slight bias toward amounts beginning in 3.
Significant variance would indicate a manipulated data set.
13 ©2022 Crystallize Analytics. All rights reserved.
Periodic Flux Test (Monthly, Annually)
Purpose: To Identify Significant Variances in Vendor Purchasing
Patterns that May Indicate Greater Risk or a Need to
Understand the Change (Note: Same AP Data as Prior Slides)
14 ©2022 Crystallize Analytics. All rights reserved.
AP- Duplicate Invoice Amounts
Purpose: To Identify Invoices with Duplicate Amounts
Formula Explanation: countifs() counts the rows where the Supplier Num
AND the Invoice Amount are both the same. If the count is greater or equal
to 2, then the formula returns a “Yes.”
While it can be useful to see duplicate invoice amounts. The problem
with this analysis is that it is not unusual to pay a vendor the same
amount for as payments or as standard order.
15 ©2022 Crystallize Analytics. All rights reserved.
AP- Duplicate Invoice Amounts in the
Same Month
Purpose: To Identify Invoice Duplicate Amounts w/ Same Month
Formula Explanation: If the invoice amount is a duplicate AND if the Supplier
Num AND the Month AND the Year are the same, a “Yes” is returned.
This is a more useful analytic, because it is more unusual to have a supplier
invoice a duplicate amount in the same month and year and may indicate
an invoicing error and a related duplicate payment if not caught early. But
what if this can be taken a step further?
16 ©2022 Crystallize Analytics. All rights reserved.
AP- Duplicate Invoice Amounts in the
Same Month w a Duplicate Inv. #
Purpose: To Identify Invoice Duplicate Amounts w/ Same Month
Formula Explanation: If the invoice amount is a duplicate AND if the Invoice
Num is the same, a “Yes” is returned. Most ERP systems should prevent a
duplicate invoice number, but an inadvertent or intentional miskeying
Can circumvent this control. For Oracle EBS, a lowercase letter is different
From the same upper case letter. This is why the formula transform the
Invoice number to upper case – upper(). Note Great Rug Company- this upper
lower case control hole has potentially caused a $422,255 duplicate invoice
and potential a duplicate payment.
17 ©2022 Crystallize Analytics. All rights reserved.
 Invoice Number Format Test
 Caused primarily by the invoice process urgency,
one of the more common ways that invoices and
payment are duplicated is altering, prefixing, or
suffixing invoice number to circumvent the system
control that prevents a duplicate invoice number
for the same vendor.
 Invoice numbers that are in a different format,
length, or with unusual characters may indicate a
fraudulent invoicing scheme.
Accounts Payable – Advanced Analytics
18 ©2022 Crystallize Analytics. All rights reserved.
 Invoice Number Format Test
Accounts Payable – Advanced Analytics
This test convert invoice numbers to a code where letters become “A”,
Numbers become “#”, and hyphens are preserved. This allows an automated
Audit routine to compare and identify the unusual invoice # format.
19 ©2022 Crystallize Analytics. All rights reserved.
Accounts Payable – Advanced Analytics
Formula Explanation: If each digit, taken one at a time is a number
ISNUMBER() it is replaced with a “#”, if it is not a number, it is replaced with
an “A”, and if it is a hyphen, it remains a hyphen.
Instead of comparing an exact invoice number match (as presented in the
Prior slides), this allows easier identification of odd or manipulated format.
20 ©2022 Crystallize Analytics. All rights reserved.
 Purchase invoices above authorized limits
 Split purchases
 Invoices containing line items that are
duplicates of line items on other invoices
 Invoices from phantom or unauthorized
vendors
 Redirected payment addresses
 Purchase/invoice pattern recognition
Other AP Analytics – RPA Opportunities
21 ©2022 Crystallize Analytics. All rights reserved.
 Maturity Cycle (Standard)
 Analytics and audit procedures developed
manually in Excel or other scripting tools
 Based on the success of the manual analytics and
audit procedures, write custom scripts or
programs and begin developing dashboards to
report results graphically and summarily
 Continually promulgate and follow up on results.
 Document cleared identified findings to ensure they are
excluded from future results
 Update the developed automated analytics and audit
procedures as requested by users (auditors and
management)
Automating Audit and Assurance
22 ©2022 Crystallize Analytics. All rights reserved.
Your accounts payable data is telling you
something. By implementing periodic and
continual analytics, you can take advantage of
what it is saying.
Conclusion
23 ©2022 Crystallize Analytics. All rights reserved.
Thank you!
Thank you!
Contact:
Brian Lewis, CPA, CIA
President & CFO
―
blewis@eprentise.com
407-591-4951
Demonstrating
Harrison Figura
Senior Product Director
―
hfigura@eprentise.com
Brought to you by:
an eprentise® company

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Your AP Data is Telling You Something: Five Analytics to Identify Duplicate Payments and Other Irregularities

  • 1. Your AP Data is Telling You Something Five Analytics to Identify Duplicate Payments and Other Irregularities
  • 2. 2 ©2022 Crystallize Analytics. All rights reserved. Who Are We? In 2007 eprentise was founded on its original product, FlexField  Enables customers to make unprecedented changes to their financial chart of accounts while maintaining transactional history and data integrity. In 2009 we introduced our Consolidation, Divestiture, and Reorganization products  Transformational software which can copy, change, filter, or merge all elements of Oracle EBS financial systems to address ever-changing business needs, such as regulatory compliance and growth opportunities. In 2020 we began expanding to new markets with our C Collection analytics suite, and our Audit Automation software  C Collection analytics provides transparency and identifies potential problem areas with transactional data. This allows users to reduce costs, leverage opportunities across the enterprise, improve business processes, and increase the confidence level of the users in their data, processes, and operations. Transformation to Optimization One-time usage to subscription model  Automated Audit provides finance teams with drill-down data from a balance sheet report into the transaction-level detail. The software covers hundreds of substantive procedures for the entire enterprise domain and builds in consistent audit processes and workflows across the organization.
  • 3. 3 ©2022 Crystallize Analytics. All rights reserved.  Learn Benford’s analysis to identify manipulated or adjusted invoice amounts  Create a periodic (annual/monthly) flux report to identify unusual or altered vendor purchasing patterns  Learn three rule of thumb analytics to identify unusual vendor billings and payments Learning Objectives
  • 4. 4 ©2022 Crystallize Analytics. All rights reserved.  Standard & Data Driven Risk Assessment  Getting AP Data Out of Your ERP  Macro Analysis  Benford’s Law Analysis  Periodic Flux Test (Monthly, Annually)  AP Analytics to Identify Wonky Invoices  Duplicate Amounts Test  Duplicate Amounts in the Same Month  Invoice Number Format Test  Duplicate Amounts with Duplicate Invoices Agenda
  • 5. 5 ©2022 Crystallize Analytics. All rights reserved. Standard Audit Risk and Control Assessment-  Identify the critical business processes Example: Procure to Pay  Purchasing  Accounts Payable  Cash Disbursements  Identify the risks (what could go wrongs)  Identify a control that mitigates the risk  Accounts payable What could go wrong? • A duplicate, incorrect, or fraudulent invoice could be submitted and paid • Control? • ERP won’t allow vendor duplicate invoice numbers Standard & Data Driven Risk Assessment
  • 6. 6 ©2022 Crystallize Analytics. All rights reserved. Standard & Data Driven Risk Assessment
  • 7. 7 ©2022 Crystallize Analytics. All rights reserved. Data Driven Audit Risk and Control Assessment-  Interrogate the data to identify what is going wrong Example: Procure to Pay  Purchasing  Accounts Payable  Cash Disbursements  Accounts payable What did go wrong? Let the data tell you. Standard & Data Driven Risk Assessment
  • 8. 8 ©2022 Crystallize Analytics. All rights reserved.  Two Comparable Time Periods (Year, Month)  Required Key Data Fields  Invoice Number  Invoice Date  Supplier Name and Supplier Number  Invoice Amount  Description  Add a Year Column - year()  Add a Month Column – month() Getting AP Data Out of Your ERP
  • 9. 9 ©2022 Crystallize Analytics. All rights reserved. Example Oracle EBS
  • 10. 10 ©2022 Crystallize Analytics. All rights reserved. Example Oracle EBS Trading Partner Supplier Num Supplier Site Name Invoice Date Invoice Num Invoice Amount Description Advanced Network Devices 1013 SANTA CLARA-ERS 1-Jan-09 ERS-12289-190052 4,143,423.03 Receipt Invoice automatically created on 01-JAN-09 Erickson, Barry 8056 OFFICE 1-Jan-09 W37620 4,054.06 Sales Overview - Benefit Features Erickson, Barry 8056 OFFICE 1-Jan-09 W35124 3,929.06 Sales Overview - Benefit Features Eastern Industrial Products 5033 EIP MAIN 2-Jan-09 ERS-8538-183718 124,124.00 Receipt Invoice automatically created on 02-JAN-09 Lang, Inga R 8024 OFFICE 2-Jan-09 W11621 3,807.99 Financials Demonstration Lewis, David 20059 OFFICE 2-Jan-09 W11630 6,773.00 Customer Conference United Parcel Service 1003 UPS - HQ 2-Jan-09 OPS200939084 57,461.60 TT Services 5017 TT SAN FRAN 3-Jan-09 ERS-8537-183717 613,676.00 Receipt Invoice automatically created on 03-JAN-09 TT Services 5017 TT SAN FRAN 3-Jan-09 ERS-8539-183720 76,384.00 Receipt Invoice automatically created on 03-JAN-09 Building Management Inc. 2012 HQ - NYC 3-Jan-09 OPS200939085 144,467.76 Office Supplies, Inc. 1008 OFFICESUPPLIES 4-Jan-09 OPS200939086 27,374.56 Boise Cascade 5030 BC HQ 5-Jan-09 BC-012009 500 Industrial Dressler 2005 US HEADQUATERS 5-Jan-09 IND-012009 1,500.00 Consolidated Supplies 1014 SPRINGFIELD 5-Jan-09 OPS200939087 4,489.80 American Telephone and Telegraph 1005 AT&T - HQ 6-Jan-09 OPS200939088 65,262.76 McGwire, Patrick 20025 OFFICE 7-Jan-09 W11614 4,300.91 Management Meeting Advanced Network Devices 1013 FRESNO 7-Jan-09 OPS200939089 110,342.18 Staples 5029 STAPLES LA 9-Jan-09 ERS-8544-183789 477,400.00 Receipt Invoice automatically created on 09-JAN-09 Bailey, Sara 8020 OFFICE 9-Jan-09 W11620 3,739.97 Team Meeting Beckman, Lisa 20065 OFFICE 9-Jan-09 W11625 2,927.00 Regional Meeting Brown, Casey 8022 OFFICE 9-Jan-09 W11618 3,426.69 Applications Demo - Kansas City United Parcel Service 1003 UPS - HQ 9-Jan-09 OPS200939091 57,461.60 Building Management Inc. 2012 HQ - NYC 10-Jan-09 OPS200939092 144,467.76 Office Supplies, Inc. 1008 OFFICESUPPLIES 11-Jan-09 OPS200939093 27,374.56
  • 11. 11 ©2022 Crystallize Analytics. All rights reserved. Purpose: To Identify Unusual Data Pattern in AP that May Indicate Manipulation, Errors, or Other Irregularities “Briefly explained, Benford's Law maintains that the numeral 1 will be the leading digit in a genuine data set of numbers 30.1% of the time; the numeral 2 will be the leading digit 17.6% of the time; and each subsequent numeral, 3 through 9, will be the leading digit with decreasing frequency. This expected occurrence of leading digits can be illustrated as shown in the chart ‘Benford's Law.’” https://www.journalofaccountancy.com/issues/2017/apr/excel-and-benfords-law-to-detect- fraud.html Genuine data sets are driven by the tendency to purchase more $1,000 items than $9,000 items. Real world purchases conform closely to the Benford’s First Digit Expected Distribution. This is true because it is harder to justify or gain permission to purchase the larger dollar amounts. Benford’s Law Analysis
  • 12. 12 ©2022 Crystallize Analytics. All rights reserved. Benford’s Law- Setting Up the Analytic Extract the First Digit Analyze the 1st Digit Distribution Against the Benford’s Expectation Conclusion- Some Variance- Slight bias toward amounts beginning in 3. Significant variance would indicate a manipulated data set.
  • 13. 13 ©2022 Crystallize Analytics. All rights reserved. Periodic Flux Test (Monthly, Annually) Purpose: To Identify Significant Variances in Vendor Purchasing Patterns that May Indicate Greater Risk or a Need to Understand the Change (Note: Same AP Data as Prior Slides)
  • 14. 14 ©2022 Crystallize Analytics. All rights reserved. AP- Duplicate Invoice Amounts Purpose: To Identify Invoices with Duplicate Amounts Formula Explanation: countifs() counts the rows where the Supplier Num AND the Invoice Amount are both the same. If the count is greater or equal to 2, then the formula returns a “Yes.” While it can be useful to see duplicate invoice amounts. The problem with this analysis is that it is not unusual to pay a vendor the same amount for as payments or as standard order.
  • 15. 15 ©2022 Crystallize Analytics. All rights reserved. AP- Duplicate Invoice Amounts in the Same Month Purpose: To Identify Invoice Duplicate Amounts w/ Same Month Formula Explanation: If the invoice amount is a duplicate AND if the Supplier Num AND the Month AND the Year are the same, a “Yes” is returned. This is a more useful analytic, because it is more unusual to have a supplier invoice a duplicate amount in the same month and year and may indicate an invoicing error and a related duplicate payment if not caught early. But what if this can be taken a step further?
  • 16. 16 ©2022 Crystallize Analytics. All rights reserved. AP- Duplicate Invoice Amounts in the Same Month w a Duplicate Inv. # Purpose: To Identify Invoice Duplicate Amounts w/ Same Month Formula Explanation: If the invoice amount is a duplicate AND if the Invoice Num is the same, a “Yes” is returned. Most ERP systems should prevent a duplicate invoice number, but an inadvertent or intentional miskeying Can circumvent this control. For Oracle EBS, a lowercase letter is different From the same upper case letter. This is why the formula transform the Invoice number to upper case – upper(). Note Great Rug Company- this upper lower case control hole has potentially caused a $422,255 duplicate invoice and potential a duplicate payment.
  • 17. 17 ©2022 Crystallize Analytics. All rights reserved.  Invoice Number Format Test  Caused primarily by the invoice process urgency, one of the more common ways that invoices and payment are duplicated is altering, prefixing, or suffixing invoice number to circumvent the system control that prevents a duplicate invoice number for the same vendor.  Invoice numbers that are in a different format, length, or with unusual characters may indicate a fraudulent invoicing scheme. Accounts Payable – Advanced Analytics
  • 18. 18 ©2022 Crystallize Analytics. All rights reserved.  Invoice Number Format Test Accounts Payable – Advanced Analytics This test convert invoice numbers to a code where letters become “A”, Numbers become “#”, and hyphens are preserved. This allows an automated Audit routine to compare and identify the unusual invoice # format.
  • 19. 19 ©2022 Crystallize Analytics. All rights reserved. Accounts Payable – Advanced Analytics Formula Explanation: If each digit, taken one at a time is a number ISNUMBER() it is replaced with a “#”, if it is not a number, it is replaced with an “A”, and if it is a hyphen, it remains a hyphen. Instead of comparing an exact invoice number match (as presented in the Prior slides), this allows easier identification of odd or manipulated format.
  • 20. 20 ©2022 Crystallize Analytics. All rights reserved.  Purchase invoices above authorized limits  Split purchases  Invoices containing line items that are duplicates of line items on other invoices  Invoices from phantom or unauthorized vendors  Redirected payment addresses  Purchase/invoice pattern recognition Other AP Analytics – RPA Opportunities
  • 21. 21 ©2022 Crystallize Analytics. All rights reserved.  Maturity Cycle (Standard)  Analytics and audit procedures developed manually in Excel or other scripting tools  Based on the success of the manual analytics and audit procedures, write custom scripts or programs and begin developing dashboards to report results graphically and summarily  Continually promulgate and follow up on results.  Document cleared identified findings to ensure they are excluded from future results  Update the developed automated analytics and audit procedures as requested by users (auditors and management) Automating Audit and Assurance
  • 22. 22 ©2022 Crystallize Analytics. All rights reserved. Your accounts payable data is telling you something. By implementing periodic and continual analytics, you can take advantage of what it is saying. Conclusion
  • 23. 23 ©2022 Crystallize Analytics. All rights reserved. Thank you! Thank you! Contact: Brian Lewis, CPA, CIA President & CFO ― blewis@eprentise.com 407-591-4951 Demonstrating Harrison Figura Senior Product Director ― hfigura@eprentise.com Brought to you by: an eprentise® company