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Digital Analysis to Detect Fraud and Abuse

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Search for Symptoms of Fraud in the Data. Systematic Review (Ongoing) Verify to Source ... Require little user knowledge of analysis techniques ... – PowerPoint PPT presentation

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Title: Digital Analysis to Detect Fraud and Abuse


1
Digital Analysis to Detect Fraud and Abuse
Dave Coderre CAATS Ottawa, Canada ISACA June 30,
2004
2
Digital Analysis Outline
  • Approach to Fraud/Abuse Detection
  • Advanced Analytical Techniques

3
Proactive Fraud Detection
  • Build a Profile of Potential Frauds which can
    then be Tested
  • Develop a Fraud Monitoring/Detection Plan to Look
    for Possible Indicators or Symptoms of Fraud
  • Investigate, Drill Down and Verify Patterns
    Emerging from the Analysis
  • Use Continual Auditing to Automate the Fraud
    Prevention and Detection Process

4
Fraud Monitoring/Detection Plan
  • WHY - the objective of the analysis
  • WHAT - the expected results
  • WHERE - source of the data
  • HOW - analyses to be performed
  • WHAT NEXT - follow-up
  • Corroborating Proof
  • Additional Analysis
  • Development of ACL Batches

5
Fraud Schemes - Approach
  • Fraudsters Perspective
  • Who could Benefit?
  • How could they be Involved?
  • What can they Effect/Manipulate or Control to
    Allow Fraud to Happen?
  • Can they Act Alone or is Collusion Required?

6
Fraud Schemes - Approach
Quantity Received
  • Source - Receipt File
  • Data - Quantity Received Field
  • Analysis - Recd Qty gt Ordered Qty
  • Follow up - Contracting Officer
  • - Receipt Clerk
  • - Vendor

7
Summary
  • Identify Risks
  • Search for Symptoms of Fraud in the Data
  • Systematic Review (Ongoing)
  • Verify to Source

8
Symptoms of Fraud in the Data
  • Known Symptoms
  • Unknown Symptoms
  • Definite Criteria
  • Standard Analysis
  • Scheduled Jobs
  • Anomalies
  • Patterns in the Data

9
Symptom Detection
  • Known Symptoms
  • Basic Tests
  • Filters
  • Extraction Routines
  • Unknown Symptoms
  • Digital Analysis
  • Trends in the Data
  • Patterns
  • Anomalies

10
Credit Card Purchases
  • Known Symptoms / Concerns
  • Personal purchases

?
11
Credit Card Purchases
  • Examples of Known Symptoms / Concerns
  • Top 10 Transactions
  • Weekend Purchases
  • Bypassing Financial Controls
  • Vendor Theft of Card
  • Hotel Charges on Acquisition Card
  • Cash Advances
  • Exceeding Transaction Limit

12
Credit Card - Data Analyses
  • Basic
  • Completeness, Integrity, Duplicates, Gaps
  • Cross Tabs (by Card by Std Object)
  • Advanced
  • Data Profiling
  • Statistics and Anomalies
  • Variance Analysis
  • Ratios - Max / Min and Max / Max2
  • Benfords Law
  • First, First Two, First Three and Second Digits

13
Data Profiling of a Numeric Field
  • Selected Numeric Field
  • Statistical Analysis
  • Min, Max, Average, Highest and Lowest Values
  • Even Amounts
  • Multiples of 1,000
  • Frequently Used Values
  • Multiples of 1,000
  • Least/Most Used Categories
  • Vendor, Credit Card Number, Contract Officer

14
Data Profiling - Results
  • Credit Card Transactions
  • Maximum of 84K - much higher than allowed
  • 22 Transactions for even multiples of 1,000
    totaling 41,000 - problems with 2 credit cards
  • 20.67 was used 170 times
  • One Supplier used 5,092 times
  • Questionable Suppliers

15
Variance Analysis
  • Unit Prices
  • Item High_Price High_Price2 Ratio
  • 198 101.46 98.91
    1.026
  • 773 123.48 57.23
    2.158
  • 861 51.23 50.84
    1.008
  • 634 26.31 11.63
    2.262
  • 992 124.78 124.03
    1.006

16
RATIO ANALYSIS
Variance in Payments - by Vendor
17
Benfords Law
18
Contract/Purchase Order Amounts
  • Frequency

First Digit
Symptom of Possible Kickbacks Over Billing
Contract Splitting etc.
19
Bank Deposit Amounts
Frequency
First Digit
20
Advanced Analysis - Results
  • Variance Analysis
  • Possible problems with Vendor Transactions based
    on Max/Max2 and Max/Min ratios
  • wrong vendors and wrong amounts
  • Unit Price anomalies for certain Products
  • Benfords Law
  • Overall agreement with expected values
  • Possible problems in details

21
Conclusions
  • Benefits of Using Audit Software
  • Many auditors already familiar with the software
  • Pro-active review of transactions
  • Standard tests can be performed across a range of
    industries, locations and systems
  • Require little user knowledge of analysis
    techniques
  • Review 100 of transactions and focus attention
    on Few questionable items requiring follow up
  • Automatic logging capabilities

22
References
  • SAS and ISA AICPA standards etc
  • Occupation Fraud and Abuse, ACFE, Joseph Wells.
  • Fraud Detection Using Data Analysis Techniques
    to Detect Fraud and
  • The Fraud Toolkit
  • The last two publications are available at
    www.ekaros.ca

23
The End (finally)
  • Thank you
  • Any Questions ?????
  • Dave_Coderre_at_hotmail.com
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