Varieties Statistical Fraud Models:Varieties Statistical Fraud Models:30 Models in 30 Minutes30 Models in 30 Minutes

Daniel Finnegan, CFEISO Innovative Analytics

Quality Planning Corporation

Benford’s Law in Accounting FraudBenford’s Law in Accounting FraudOdds of Obtaining as 1st Digit (%)

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Odds of Obtaining as 1st Digit(%)

Tests for Manufacture NumbersTests 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).

IRS Audit Selection SystemIRS Audit Selection System

1964 Rule-Based Scoring System1970’s TCMP Statistical Audit System2003 NRP System:

A. Random Audits of Sample of ReturnsB. Identification of Returns “In Need of

Examine”C. Statistical Model of DIF score of “Probability

of Need to Examine”D. Monitoring and Update of System

Text Mining for Fraudulent Medical BillsText Mining for Fraudulent Medical Bills

Search for identical typos Search for identical prognosis Search for date discrepancies

Holidays Claimant out of town/dead

Medical Usage Pattern Fraud AnalysisMedical 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

FAIS Money Laundering Statistical FAIS Money Laundering Statistical DetectionDetection

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Pattern Analysis such as Large Sum Deposited and Immediately Withdrawn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

Network Analysis of Auto AccidentsNetwork Analysis of Auto Accidents

Daniel

Glenn Richard

Staged Accident RingStaged Accident Ring

Sequential Handling of Questionable Sequential Handling of Questionable ClaimsClaims

Random Sample of 3,000 BI Claims Decision Flow Model

InitialReview

FraudScore 1

ClearQuestions

SUI

Adjust andSettle

FraudScore 2

Low

High

Middle

Timing Claims CurvesTiming Claims Curves

Claims by Policy Week

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Other Threshold Fraud ModelsOther Threshold Fraud Models

Adding Coverage for Comp Two-Year New Vehicle Replacement School Lunch Eligibility

Deviant Purchase Patterns for Credit Deviant Purchase Patterns for Credit Card FraudCard 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

Geographic Analysis of Staged AccidentsGeographic Analysis of Staged Accidents

Chorpo

Insured

Claimant

Accident

Attorney

Chiropractor

Geographic Analysis of Staged AccidentsGeographic Analysis of Staged Accidents

Chorpo

Insured

Claimant

Accident

Attorney

Chiropractor

Driver’s License Translator FraudDriver’s License Translator Fraud Pass Rate:

51% vs 95+% Time to Complete

30-60 Minutes vs 10-15 Minutes

Accidents by Time Since License

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cid

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Translator

Matched

Insider Stock DealingInsider 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

WC Premium Audit Selection ModelWC 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

University Student Aid FraudUniversity 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

Work Load Analysis of Medical Billing Work Load Analysis of Medical Billing FraudFraud

Psychiatrist billing 80 hour work days

Billing on 365 day years Billing from distant locations Billing for 200 patients per day

Adjuster – Vendor Pairing ModelsAdjuster – 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

Social Security Disability ModelSocial 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

Sales Agent Rating ModelsSales 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

Food Stamp Store Investigation SystemFood Stamp Store Investigation System

Prior System Viewed as a Success

Random Investigation of 2,000 Stores

Statistical Analysis of Discovered Violations

Food Stamp Investigation OutcomesFood Stamp Investigation OutcomesDiscovered Violations

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Random Rate

TragetedInvestigations

VIPER SystemVIPER System

Statistical Pattern Targeting Random Component for Updating Geographic Clustering Component Tripled Discovered Violations Doubled Investigator Productivity

Thresholding Cell Phone AccountsThresholding 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

Identity Theft ScoringIdentity 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

Statistical Adjuster Assignment ModelsStatistical 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

Statistical Adjuster Assignment ModelsStatistical Adjuster Assignment Models

Average per Exposure Cost by Claims History and Handling Method

$1,612

$1,356

$638

$1,261

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$1,200

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Questionable History Unexpectional

Standard Handling

Enhanced Handling

Common Elements of Successful Common Elements of Successful Statistical Fraud ControlStatistical 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