Using Data Analytics to Detect and Deter Procure to Pay Fraud

Using Data Analytics to Find
and Deter Procure-to-Pay
Fraud
October 30, 2013
Special Guest Presenter:
Rich Lanza

Copyright © 2013 FraudResourceNet™ LLC

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.


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


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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

The views expressed by the presenters do not necessarily represent the
views, positions, or opinions of FraudResourceNet LLC (FRN) or the
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5

Today’s Agenda
 Who commits P2P fraud
 Red flags of fraud in procurement, receiving and payment
 Finding the red flags using data analytics tools
 How to minimize risk of becoming a victim of P2P fraud
 Best anti-fraud controls for P2P


Asset Misappropriation
Tops The Charts


Vendor Billing Fraud/Corruption
Is #1 or #2 No Matter Where You Go


8

Risk Assessment Departmental
Focus


9

Top Fraud Schemes By
Department


Primary Weaknesses Leading to
the Fraud


11

Detection Methods By
Company Size


12

The Top Procedures
Per SAS 99 – Appendix B

1.
2.
3.
4.
5.

Whistleblowing hotline
Signed code of conduct
Train employees
Background checks
Look for and respond to fraud
Most companies have these procedures
in place but the question is….how
effective are they?

13

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.
B.
C.
D.

Billing
Corruption
Overstated Revenue
Expense Reimbursements


Mapping Red Flags to Analytics


Report Brainstorm Tool


Proactively Detecting Fraud
Using Computer Audit Reports

IIA Research Paper / CPE
The purpose of this document is to assist
Course
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.

See the IIA’s website at www.theiia.org

Visualizing the Cost Recovery
and Savings Process


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


Page 20

Process to Report Mapping


Fraud Task to Report Mapping


Fraud Task to Supplier Map


Polling Question #2

What are example cost recovery areas
associated with the P2P cycle?
 Freight
 Order to cash
 Healthcare
 Accounts Payable


Page 24

Using an Analytic Process to
Detect Fraud


Analytic Command Center
Analytic Command
Center

Shared Services

Data Mart

1. Accounts Payable
2. Accounts
Receivable
3. Financial Statement
4. General Ledger
5. Inventory
6. Payroll
7. Revenue


Local
Analytic
Toolkit

Recovery Auditors

Feedback
from all
locations

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


2
6

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


Page 29

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)


31

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


Page 32

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


Stratify Data - Results


Page 34

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.SAM.GOV


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


Polling Question 3

What is not one of the query viewpoints?
A.
B.
C.
D.

Who
What
How
When


Other T&E Reports
 Unmatched query of cardholders to an active employee
masterfile
 Cards used in multiple states (more than 2) in the same day
 Cards processing in multiple currencies (more than 2) in the
same day
 Identify cards that have not had activity in the 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


Daily Transactional Analysis


40

GeoMapping - Map Point


Page 41

Charting the Score


Scatter Graph


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


Page 45

Polling Question 4

What graph is used to map value to score
for easier selections of data subsets?
A.
B.
C.
D.

Pie
Line
Bar
Scatter



Simple Fraud Vendor Scoring
Analysis – How It Started
 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%) or 3x the standard deviation for
that vendor.
 Vendors paid with a high proportion of manual checks.

48

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!!!

Page 49

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”.

50

Summaries on Various
Perspectives

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


51

Key Control Reports & Scoring


Page 52

Combining the Scores
ACL Code


Page 53

Using Vlookup to Combine
Scores

 Create a record number
 Relate sheets based on VLookup


Page 54

Polling Question #5

What Excel function is mainly used to
organize the scores into a master score?
 SUMIF()
 COUNTIF()
 RAND()
 VLOOKUP()


Page 55

Questions?
 Any Questions?
Don’t be Shy!


Coming Up Next Month
 1. Detecting Fraud in Key Accounts
Using Data Analytics: Nov.12
 2. Detecting and Preventing
Management Override of Anti-Fraud
Controls: Nov. 14
 3. Using Data Analytics to Detect PCard Fraud: Nov. 20


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

Using Data Analytics to Detect and Deter Procure to Pay Fraud

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