Varieties Statistical Fraud Models: 30 Models in 30 Minutes

1. Varieties Statistical Fraud Models:30 Models in 30 Minutes Daniel Finnegan, CFE ISO Innovative Analytics Quality Planning Corporation

2. Benford’s Law in Accounting Fraud

3. Tests for Manufacture Numbers • Frequency or equidistribution test (possible elements should occur with equal frequency); • Serial test (pairs of elements should be equally likely to be in descending and ascending order); • Gap test (runs of elements all greater or less than some fixed value should have lengths that follow a binomial distribution); • Coupon collector's test (runs before complete sets of values are found should have lengths that follow a definite distribution); • Permutation test (in blocks of elements possible orderings of values should occur equally often); • Runs up test (runs of monotonically increasing elements should have lengths that follow a definite distribution); • Maximum-of-t test (maximum values in blocks of elements should follow a power-law distribution).

4. IRS Audit Selection System 1964 Rule-Based Scoring System 1970’s TCMP Statistical Audit System 2003 NRP System: • Random Audits of Sample of Returns • Identification of Returns “In Need of Examine” • Statistical Model of DIF score of “Probability of Need to Examine” • Monitoring and Update of System

5. Text Mining for Fraudulent Medical Bills • Search for identical typos • Search for identical prognosis • Search for date discrepancies • Holidays • Claimant out of town/dead

6. Medical Usage Pattern Fraud Analysis • Uniformly high numbers of treatments (Normed on Diagnosis) • High number of modalities per treatment • Few Patients Recover Quickly • Low Percentage of Objective Injuries • Treatment Ends Abruptly at Payment of Claim

7. FAIS Money Laundering Statistical Detection • Link Analysis with Known Criminal Elements • Pattern Analysis such as Large Sum Deposited and Immediately Withdrawn • Benford Distribution of Deposits and Withdrawals • Circular Movements of Funds

8. Network Analysis of Auto Accidents

9. Staged Accident Ring

10. Sequential Handling of Questionable Claims • Random Sample of 3,000 BI Claims • Decision Flow Model

11. Timing Claims Curves

12. Other Threshold Fraud Models • Adding Coverage for Comp • Two-Year New Vehicle Replacement • School Lunch Eligibility

13. Deviant Purchase Patterns for Credit Card Fraud • Identification of Individual Purchase Patterns (Neural Net Models) • Identification of Typical Fraud Purchase Patterns (Electronics, International Spending) • Movement out of Typical Toward Fraud Patterns • Expert Patterns Such Geographic Dispersion of Purchases

14. Geographic Analysis of Staged Accidents

15. Geographic Analysis of Staged Accidents

16. Driver’s License Translator Fraud • Pass Rate: • 51% vs 95+% • Time to Complete • 30-60 Minutes vs 10-15 Minutes

17. Insider Stock Dealing • MonITARS: Fuzzy Logic, Neural Nets, Genetic Algorithms for London Stock Exchange • Advanced Detection System (ADS) for Nasdaq matches rule-based sequential trading patterns • SONAR matches wire stories to stock trading using pattern analysis to detect stock manipulation

18. WC Premium Audit Selection Model • Statistical Modeling of 4 Years of Audit Results • Holdback of 5th Year of Results • Combined Expert Theory and Inductive Modeling • Final Model Built with Multiple Statistical Methods: • Decision Trees, MARS, GLM • Model Concentrated on Key Ratios by Industry • Results more than Doubled Audit Returns

19. University Student Aid Fraud • Very High and Similar Hardship Deductions (High Medical Bills) • Identical Applications for Student Financial Aid (High Aid with No Audit) • Fraud Clusters by Successful Sports Teams

20. Work Load Analysis of Medical Billing Fraud • Psychiatrist billing 80 hour work days • Billing on 365 day years • Billing from distant locations • Billing for 200 patients per day

21. Adjuster – Vendor Pairing Models • Billing Pattern Analysis for 5 Million Claims and 12 Million Payments • Dozen Questionable Patterns Identified: • Relative High Payment Average for Adjuster and Vendor • Identification of Vendors with Multiple Payments to PO Box with Single Adjuster

22. Social Security Disability Model • Random Sample File Review • Identified Decision Errors/Fraud • Built Multiple Models • Econometric • Decision Trees, GLM, Hybrid • Rule Violation • Decision Maker Focused • Final Artificial Intelligence Model

23. Sales Agent Rating Models • Sales Agents Mileage Model • Low to Expectations • Below Rating Cut Points • Missing Drivers • Teenagers Low to Expectations • High Permissive Use Claims • Frequent Claims After Comp Added

24. Food Stamp Store Investigation System • Prior System Viewed as a Success • Random Investigation of 2,000 Stores • Statistical Analysis of Discovered Violations

25. Food Stamp Investigation Outcomes

26. VIPER System • Statistical Pattern Targeting • Random Component for Updating • Geographic Clustering Component • Tripled Discovered Violations • Doubled Investigator Productivity

27. Thresholding Cell Phone Accounts • 6-8 Percent Cell Phone Costs Fraudulent • High Volume of Calls and Turnover of Fraud Requires Rapid Response • Account “Thresholding” Process Used • 30-Day, Fraud Free, Norming Process • Account Specific Expert Rules on Duration, Location, Timing • Calls Scored Statistical Distance from Norms • Percent of Potential Fraud Calls Monitored • Norms Constantly Updated

28. Identity Theft Scoring Scoring System Includes Variety of Data Matching and Pattern Analysis Variables • High Numbers of Credit Card or Cell Phone Applications from Address • Identity Variable Conflicts • Mail Drop Address • Impossible SSN Dead, Issued Before Born, Un-issued, Impossible

29. Statistical Adjuster Assignment Models • Review of Areas of Fraud Loss • Identification of Best Practices for Handling Questionable Claims • Sample Investigation of Matched Samples of 1,500 Standard Handling and 1,500 Enhanced Handling • Statistical Modeling of Handling Gains

30. Statistical Adjuster Assignment Models

31. Common Elements of Successful Statistical Fraud Control • Statistical Methods Selected to Fit the Problem (One Size Does Not Fit All) • High Input from Substance Area Experts • Feedback Loop Evaluates and Updates System • Strong Integration with Operations