Using Data Analytics to Find and Deter Procure to Pay Fraud

3/11/2014
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Copyright © FraudResourceNet LLC
Using Data Analytics to Find
and Deter Procure‐to‐Pay
Fraud
March 12, 2014
Special Guest Presenter:
Rich Lanza
About Jim Kaplan, MSc, CIA, CFE
• President and Founder of
AuditNet®, the global resource for
auditors (now available on Apple
and Android devices)
• Auditor, Web Site Guru,
• Internet for Auditors Pioneer
• Recipient of the IIA’s 2007
Bradford Cadmus Memorial
Award.
• Author of “The Auditor’s Guide to
Internet Resources” 2nd Edition

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About Peter Goldmann, MSc., CFE
President and Founder of White Collar Crime 101
• Publisher of White-Collar Crime Fighter
• Developer of FraudAware® Anti-Fraud Training
• Monthly Columnist, The Fraud Examiner, ACFE
Newsletter
Member of Editorial Advisory Board, ACFE
Author of “Fraud in the Markets”
• Explains how fraud fueled the financial crisis.
Richard B. Lanza, CPA, CFE, CGMA
• Over two decades of ACL and Excel software usage
• Wrote the first practical ACL publication on how to
use the product in 101 ways (101 ACL
Applications)
• Has written and spoken on the use of audit data
analytics for over 15 years.
• Received the Outstanding Achievement in Business
Award by the Association of Certified Fraud
Examiners for developing the publication
Proactively Detecting Fraud Using Computer Audit
Reports as a research project for the IIA
• Recently was a contributing author of:
• Global Technology Audit Guide (GTAG #13) Fraud
in an Automated World - IIA
• Data Analytics – A Practical Approach - research
whitepaper for the Information System
Accountability Control Association.
• “Cost Recovery – Turning Your Accounts Payable
Department into a Profit Center” – Wiley & Sons.
Please see full bio at www.richlanza.com

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Disclaimers

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Today’s Agenda
 Costliest procurement frauds to beware of (with
case studies)
 Indicators of possible collusive procurement-
vendor schemes
 Red flags to look for in audits of procurement
using data analytics
 How to conduct the audit using data analytics
 What to do if you find vendor or procurement
fraud during an audit
Issues in Vendor Master
Management
Too many active vendors
(with no activity)
Improper segregation
of duties
Difficult to understand or
improve spend
management
Difficult to manage or
improve payment terms
Invalid Tax IDs leading to B
notices
Too many active vendors
(with no activity)
Improper segregation
of duties
Difficult to understand or
improve spend
management
Difficult to manage or
improve payment terms
Invalid Tax IDs leading to B
notices
Duplicate vendors
representing parents and
subs, for different
companies, or just
mistakes = duplicate pays
Duplicate vendors
subs, for different
companies, or just
Duplicate vendors
subs, for different
companies, or just
Vendors are confused and
may see opportunities to
take advantage of the
company
Vendors are confused and
may see opportunities to
take advantage of the
company
Lack of vendor
management …increases
opportunity for fraud
Lack of vendor
Lack of vendor

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Vendor Billing Fraud/Corruption
Is #1 or #2 No Matter Where You Go
Top Fraud Schemes By
Department

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Primary Weaknesses Leading to
the Fraud
1. Whistleblowing hotline
2. Signed code of conduct
3. Train employees
4. Background checks
5. Look for and respond to fraud
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Most companies have these procedures in
place but the question is….how effective
are they?
The Top Procedures
Per SAS 99 – Appendix B

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Where Are You Using Data
Analytics?
AuditNet – 2012 Data
Analysis Software
Survey
Polling Question 1
What is not one of the top occurring frauds per the ACFE
study?
A. Billing
B. Corruption
C. Overstated Revenue
D. Expense Reimbursements

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Mapping Red Flags to Analytics
Report Brainstorm Tool

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Proactively Detecting Fraud
Using Computer Audit Reports
IIA Research Paper / CPE Course
See the IIA’s website at www.theiia.org
The purpose of this document is to assist
auditors, fraud examiners, and
management in implementing data analysis
routines for improved fraud prevention and
detection.
A comprehensive checklist of data
analysis reports that are associated
with each occupational fraud category
per the Association of Certified Fraud
Examiners’ classification system.
Visualizing the Cost Recovery
and Savings Process

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Profit Opportunities Outweigh
Analytic Costs
 Accounts Payable
 Audit Fee Benchmarking
 Advertising Agency
 Document Fleet
 Freight
 Health Benefits
 Lease
 Media
 Order to Cash
 Proactive Fraud Detection
 Project Fraud
 Real Estate Depreciation
 Sales & Use Tax / VAT / R&D
tax
 Strategic Sourcing
 Telecom
 Travel and Entertainment
 Utilities
Process to Report Mapping

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Fraud Task to Report Mapping
Fraud Task to Supplier Map

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Polling Question #2
What are example cost recovery areas
associated with the P2P cycle?
A. Freight
B. Order to cash
C. Healthcare
D. Accounts Payable
Using an Analytic Process to Detect
Fraud

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Best Practice Approach on
Fraud:Find It Before It Finds You
Think Prevention vs. Future Detection
 Identify issues earlier in their lifecycle
 Build a aura of deterrence within procure to pay
Build a Continuous Review Process
 Technology
 Minimal staff time / external assistance
Keep Improving the Model
 Model on identified frauds
 Remove false positives to isolate interesting events
Analytic Command Center
Analytic Command Center
1. Accounts Payable
2. Accounts Receivable
3. Financial Statement
4.General Ledger
5. Inventory
6.Payroll
7. Revenue
Data Mart
Local Analytic
Toolkit
Feedback from
all locations
Recovery Auditors
Shared Services

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The Overall Fraud
Analytic Process
 Get the Most Useful Data for Analysis
General Ledger / Accounts Payable
Other? / Use external data sources
 Develop Fraud Query Viewpoints
The 5 Dimensions
Brainstorm report ideas
 Analytically Trend
Benford’s Law
Statistical averages and simple trending by day, month, day of week
Post dated changes
 Use Visualization Techniques
Clear Data Request

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Fraud Data Considerations for
the P2P Cycle
A/P and G/L Review Factors
Accounts that are sole sourced
Accounts that have too many vendors
Categories that map to the “recovery list”
Assess to industry cost category benchmarks
Top 100 vendors
Trend analysis over time
Trend analysis by vendor (scatter graph)
Purchase Order / Price List
Match to invoice payments to assess price
differences
Strategic sourcing vendor review
Distribution Analysis
 Remove subtotals for improved visibility
 Focus on sole source and multi source vendors
 Scroll out and drill to details as needed

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Query Viewpoints
It’s The Trends….Right?
Trend categories (meals, hotel, airfare,
other)
Trend by person and title
Trend departments
Trend vendors
Trend in the type of receipts
Trend under limits (company policy)
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Number Ranking
 Summarize each amount (Pivot or ACL)
 Rank each number in order of occurrence
 Score each item in a sliding scale
 May be easiest to use a stratified score
 Decide if unique is weirder than non-unique
 Relate this summarized list back to the original
Some Data Mining Approaches
Personnel Analysis
 Adjustments by employee
 Processing by employee
Contextual Summarizations
 Transaction types
Time Trending
 Month, week, and day / Also by department
 Last month to first 11 months
 Transactions at the end of and start of a fiscal year

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Stratify Data ‐ Results
Is Your Organization Working
With Banned Companies?
EPLS is the excluded party list service of the U.S. Government as
maintained by the GSA
WWW.EPLS.GOV
Now
WWW.SAM.GOV

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Is Your Organization
Working With Terrorists?
Are Your Vendors Real?
IRS TIN Matching Program
Validates U.S. Tax Identification Numbers
Can submit up to 100,000 TIN submissions at a
time
Make sure all punctuation is removed
See http://www.irs.gov/taxpros/ and enter “TIN
matching program” in the search box

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Polling Question 3
What is not one of the query viewpoints?
A. Who
B. What
C. How
D. When
Other T&E Reports
 Unmatched query of cardholders to an active employee
masterfile
 Cards used in multiple states (more than 2) in same day
 Cards processing in multiple currencies (more than 2) in
the same day
 Identify cards that have not had activity in last six months
 Cardholders that have more than one card
 Extract any cash back credits processed through the card
 Extract declined card transactions and determine if they
are frequent for certain cards
 Summary of card usage by merchant to find newly
added merchants and most active

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Daily Transactional Analysis
GeoMapping ‐ Map Point

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Charting the Score
Scatter Graph

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Scatter Graph Explanation
1 – high dollar change and low count (outliers)
2 – charges that make sense
3 – changes that don’t make sense
4 – inefficiency that is developing
Dashboarding Graphing

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Polling Question 4
What graph is used to map value to score for
easier selections of data subsets?
A. Pie
B. Line
C. Bar
D. Scatter
Fraud Scoring for Maximum Impact

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 Vendors on report 1 vs. report 2 of duplicate payments.
 Duplicate transactions paid on different checks.
 Duplicate transactions with debit amounts in the vendor account
 Vendors with a high proportion of round dollar payments.
 Invoices that are exactly 10x, 100x or 1000x larger than another
invoice.
 Payments to any vendor that exceed the twelve month average
payments to that vendor by a specified percentage (i.e., 200%) o
the standard deviation for that vendor.
 Vendors paid with a high proportion of manual checks.
Simple Fraud Vendor Scoring
Analysis – How It Started
The Sampling “Problem”
Bottom Line Numbers
 Modern tests (round numbers, duplicates, missing fields)
identify thousands of ‘suspicious’ transactions, usually about
1 in 5 of all transactions get a ‘red flag’
 Historically at least 0.02 – 0.03 % of all transactions have
real problems, such as a recoverable over-payment
 So roughly 0.00025 / 0.2 = 0.00125 or 1 in 800 ‘red flags’
lead to a real problem.
Imagine throwing a random dart at 800 balloons
hoping to hit the right one!!!

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Transactional Score Benefits
 The best sample items (to meet your attributes) are selected
based on the severity given to each attribute. In other words,
errors, as you define them, can be mathematically calculated.
 Instead of selecting samples from reports, transactions that meet
multiple report attributes are selected (kill more birds with one
stone). Therefore a 50 unit sample can efficiently audit:
 38 duplicate payments
 22 round invoices
 18 in sequence invoices
….and they are the best given they are
mathematically the most “severe”.
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Summaries on Various
Perspectives
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Summarize by
dimensions (and sub
dimension) to pinpoint
within the cube the
crossover between the top
scored location, time, and
place of fraud based on
the combined judgmental
and statistical score

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Key Control Reports & Scoring
Combining the Scores
ACL Code

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Using Vlookup to Combine
Scores
 Create a record number
 Relate sheets based on VLookup
Polling Question #5
What Excel function is mainly used to organize
the scores into a master score?
A. SUMIF()
B. COUNTIF()
C. RAND()
D. VLOOKUP()

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Questions?
Peter Goldmann
FraudResourceNet LLC
800-440-2261
www.fraudresourcenet.com
pgoldmann@fraudresourcenet.com
Jim Kaplan
FraudResourceNet LLC
800-385-1625
www.fraudresourcenet.com
jkaplan@fraudresourcenet.com
Rich Lanza
Cash Recovery Partners, LLC
Phone: 973-729-3944
rich@richlanza.com
Thank You!

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Coming Up
Upcoming March Anti-Fraud
Webinar…
 "How to Use Data Analytics to
Expose Fixed Asset and Inventory
Fraudsters”, March 19
Sign up at:
http://www.fraudresourcenet.com

Using Data Analytics to Find and Deter Procure to Pay Fraud

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