Transcript Slide 1

Using Data Analytics To Detect Fraud
94th Annual Professional Conference –
Engaging Your New World
The Unique Alternative to the Big Four®
Session Agenda
Utilizing data analysis to detect fraud and strengthen internal
controls.
 Risk assessment process
How data analysis can be used in detecting fraud
Fine-tuning a successful data analytics program
The continuous monitoring process
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Definition of Fraud
 Webster’s Dictionary: “Deceit, trickery; cheating, intentional deception to
cause a person to give up property or some lawful right.”
 AICPA EDP Fraud Review Task Force: “Any intentional act, or series of
acts, that is designed to deceive or mislead others and that has an impact
or potential impact on an organization’s financial statements.”
 The Accountant’s Handbook of Fraud & Commercial Crime: “Fraud is
criminal deception intended to financially benefit the deceiver.”
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Fraud Triangle
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Fraud Triangle
DETECTION
PREVENTION
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INVESTIGATION
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Fraud Risk Universe
External
Board
Suppliers
Customers
Management
Competitors
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Employees
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Evolution of Fraud
1950
 Straight-line reporting
 Manual processes
 Single suppliers
 Step-up salary structure
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2000
 Matrix organizations
 Automation
 Autonomous authority
 Multiple vendors
 Performance-based pay
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Spotting Potential Risks - People
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Should Fraud Be A Concern?
 Of 4,000 high school students with A and B averages, 75 percent
admit to cheating to get ahead. Ninety- two percent of those who
said they cheated were never caught. - Who’s Who Among
American High School Students
 Almost 80 percent of college students admit to cheating at least
once. - The Center for Academic Integrity
 The percentage of resumes and job applications that contain lies
and exaggerations has been estimated between 30 and 80
percent. - Security Management Magazine
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A Study of White Collar Crime Profiles
10 % of the people will never steal
10 % of the people will steal regardless of the
circumstances
80 % of the people will steal if given the right
motive or pressure
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Behavioral Red Flags of Perpetrators
Source: 2010 ACFE Report To The Nation
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Primary Internal Control Weakness Observed
Source: ACFE 2010 Report To The Nation
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The Whistleblower
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Benefits of Using Data Analytics
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Efficiency
Increased analytical capacity
Variety of output representation
Repeatable process
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Data – Excellent Source of Fraud Evidence
1. Data is objective.
2. Data can be searched and analyzed without arousing
suspicion.
3. Data analysis can provide evidence that helps with other
areas of the investigation.
4. Provide facts needed to gain confessions during
interviews.
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Data Mining Best Practices
Establish expectations:
• What do I expect to see in the data
• What do I expect not to see in the data
• What do I expect an exception to look like
• What exceptions should I expect
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Steps to Detect Fraud
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Understand the Industry, Business, Unit
What Fraud Schemes Could Occur
Identify the Red Flags Associated With The Fraud Scheme
Test For The Red Flags
Resolve Identified Red Flags
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A Series of Examples – Total Disbursements
Sort the total payments made to each vendor in
descending order.
Vendor Name
Fabric Distributors Inc.
Silk Designer Patterns Inc.
Men’s Apparel Option Inc.
Timothy Wineguard
Threads Unlimited LLC
Wool Makers Incorporated
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Disbursement To Date
$1,873,980
$1,792,621
$1,021,426
$1,004,372
$ 981,982
$ 942,533
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A Series of Examples – Check Disbursements
Vendor ID
362862
382868
373920
Vendor Name
Disbursement To Date
TAILOR MADE SOLUTIONS
$32,975
TAILOR MAIDE SOLUTIONS
$35,583
TAILOR MADE SOLUITIONS
$59,012
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A Series of Examples – Disbursements by Vendor
Know your Alphabet Vendors
Vendor
AT&T
AT&T
AT&T
AT&T
AT&T
Check Date
01/03/09
02/02/09
02/05/09
02/23/09
03/02/09
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Check Amount
$1,493.43
$1,394.99
$2,049.63
$1,032.88
$1,382.21
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A Series of Examples – Disbursements by Frequency
Determine if a Vendor is receiving payments more
frequently than expected.
Vendor
Fabric Distributors Inc.
Silk Designer Patterns Inc.
Men’s Apparel Option Inc.
Timothy Wineguard
Threads Unlimited LLC
Wool Makers Incorporated
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# of Payments in 12 months
11
10
11
43
10
11
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A Series of Examples – Duplicates
Identify common disbursement amounts, invoice numbers.
Vendor
TAILOR
TAILOR
TAILOR
TAILOR
MADE SOLUTIONS
MAIDE SOLUTIONS
MADE SOLUTIONS
MADE SOLUTIONS
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Amount
$83,298.23
$85,001.22
$85,001.22
$83,442.35
Date
10/02/09
11/02/09
11/02/09
12/02/09
Invoice
CO9291
C09293
CO9293
CO9360
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A Series of Examples – Invoices
Ck. Date
03/12/09
03/20/09
03/31/09
04/05/09
04/05/09
Fabrics Ltd.
Fabrics Ltd.
Fabrics Ltd.
Fabrics Ltd.
Fabrics Ltd.
Payee
203920
203921
203922
203023
203025
Invoice # Ck #
2044
2053
2072
2187
2187
Ck Amount
$112,400
$112,400
$112,500
$152,839
$153,839
Test for shell companies based on invoice numbers issued.
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Searching For Addresses
Must be consistent in entering data for both the Employee
Master and the Vendor Master
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St. vs. Street
NE vs. North East
123 Main Street, Apartment B vs.
123 Main Street B
123 Main Street
Apartment B
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Geo-Coding
 You entered: 1089 East Harrison Street, Martinsville, IN 46151
The Google geocoder found:
1089 E Harrison St, Martinsville, IN 46151, USA
street address: 1089 E Harrison St ZIP/postal code: 46151 city: Martinsville
county/district: Morgan state/province: IN country: USA latitude, longitude:
39.429443, -86.415746
 39.429443 -86.415746
 N39° 25.7666', W086° 24.9448'
 (precision: address)
Source: http://www.gpsvisualizer.com/geocode
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CASE STUDY 1
• Vice President’s use of Credit Card
 Confidential – do not open
 Mischaracterized expenses
 Duplicate receipts
• Discovered – Final Bill after taking employment elsewhere.
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CASE STUDY 2
• Accounts Receivable
 Exception to normal registration
 Credit Card payments
• Discovered – Bank procedures
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CASE STUDY 3
• Department Head
 Purchases delivered to home
 Asset hard to value
• Discovered – Asset Audit when DH left employment
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THE VISION
Assess where you are today, define where you
want to be and begin to close the gap.
Repeatable Analysis
ROI
RISK
Ad Hoc Analysis
Raw Data –
System Reports
INTELLIGENCE
INFORMATION
DISPARATE DATA
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QUESTIONS
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For more information, contact:
Brenda Buetow
Direct 317.269.6697
Mobile 765.318.0919
[email protected]
Crowe Horwath LLP is an independent member of Crowe Horwath International, a Swiss verein. Each member firm of Crowe Horwath International is a separate
and independent legal entity. Crowe Horwath LLP and its affiliates are not responsible or liable for any acts or omissions of Crowe Horwath International or any
other member of Crowe Horwath International and specifically disclaim any and all responsibility or liability for acts or omissions of Crowe Horwath International or
any other Crowe Horwath International member. Accountancy services in Kansas and North Carolina are rendered by Crowe Chizek LLP, which is not a member
of Crowe Horwath International. This material is for informational purposes only and should not be construed as financial or legal advice. Please seek guidance
specific to your organization from qualified advisers in your jurisdiction. © 2011 Crowe Horwath LLP
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