Using Data Analytics To Detect Fraud

Using Data Analytics To Detect Fraud

94th Annual Professional Conference – Engaging Your New World

© 2011 Crowe Horwath LLP 2Audit | Tax | Advisory | Risk | Performance

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 fraudFine-tuning a successful data analytics program The continuous monitoring process



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.”



Fraud Triangle



PREVENTION

DETECTION

INVESTIGATION

Fraud Triangle



External

Fraud Risk Universe

Board



Evolution of Fraud

1950 Straight-line reporting Manual processes Single suppliers Step-up salary structure

2000 Matrix organizations Automation Autonomous authority Multiple vendors Performance-based pay

7



Spotting Potential Risks - People



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



A Study of White Collar Crime Profiles

10 % of the people will never steal

80 % of the people will steal if given the right motive or pressure

10 % of the people will steal regardless of the circumstances



Behavioral Red Flags of Perpetrators

Source: 2010 ACFE Report To The Nation



Primary Internal Control Weakness Observed

Source: ACFE 2010 Report To The Nation



The Whistleblower



Benefits of Using Data Analytics

Efficiency Increased analytical capacity Variety of output representation Repeatable process



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.



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



Steps to Detect Fraud

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



A Series of Examples – Total Disbursements

Sort the total payments made to each vendor in descending order.

Vendor Name Disbursement To Date

Fabric Distributors Inc. $1,873,980

Silk Designer Patterns Inc. $1,792,621

Men’s Apparel Option Inc. $1,021,426

Timothy Wineguard $1,004,372

Threads Unlimited LLC $ 981,982

Wool Makers Incorporated $ 942,533



A Series of Examples – Check Disbursements

Vendor ID Vendor Name Disbursement To Date

362862 TAILOR MADE SOLUTIONS $32,975

382868 TAILOR MAIDE SOLUTIONS $35,583

373920 TAILOR MADE SOLUITIONS $59,012



A Series of Examples – Disbursements by Vendor

Know your Alphabet Vendors

Vendor Check Date Check Amount

AT&T 01/03/09 $1,493.43

AT&T 02/02/09 $1,394.99

AT&T 02/05/09 $2,049.63

AT&T 02/23/09 $1,032.88

AT&T 03/02/09 $1,382.21



A Series of Examples – Disbursements by Frequency

Determine if a Vendor is receiving payments more

frequently than expected.

Vendor # of Payments in 12 months

Fabric Distributors Inc. 11

Silk Designer Patterns Inc. 10

Men’s Apparel Option Inc. 11

Timothy Wineguard 43

Threads Unlimited LLC 10

Wool Makers Incorporated 11



A Series of Examples – Duplicates

Identify common disbursement amounts, invoice numbers.

Vendor Amount Date Invoice

TAILOR MADE SOLUTIONS $83,298.23 10/02/09 CO9291

TAILOR MAIDE SOLUTIONS $85,001.22 11/02/09 C09293





A Series of Examples – Invoices

Ck. Date Payee Invoice # Ck # Ck Amount

03/12/09 Fabrics Ltd. 203920 2044 $112,400

03/20/09 Fabrics Ltd. 203921 2053 $112,400

03/31/09 Fabrics Ltd. 203922 2072 $112,500

04/05/09 Fabrics Ltd. 203023 2187 $152,839

04/05/09 Fabrics Ltd. 203025 2187 $153,839

Test for shell companies based on invoice numbers issued.



Searching For Addresses

Must be consistent in entering data for both the Employee Master and the Vendor Master

St. vs. Street NE vs. North East 123 Main Street, Apartment B vs. 123 Main Street B 123 Main Street

Apartment B



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



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.



CASE STUDY 2

• Accounts Receivable Exception to normal registration Credit Card payments

• Discovered – Bank procedures



CASE STUDY 3

• Department Head Purchases delivered to home Asset hard to value

• Discovered – Asset Audit when DH left employment



THE VISIONAssess where you are today, define where you want to be and begin to close the gap.

ROI RISK

Raw Data – System Reports

Ad Hoc Analysis

Repeatable Analysis

INFORMATION

INTELLIGENCE

DISPARATE DATA



QUESTIONS



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

For more information, contact:

Brenda Buetow

Direct 317.269.6697

Mobile 765.318.0919

[email protected]

Using Data Analytics To Detect Fraud

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