Varieties Statistical Fraud Models: 30 Models in 30 Minutes Daniel Finnegan, CFE ISO Innovative...
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Transcript of Varieties Statistical Fraud Models: 30 Models in 30 Minutes Daniel Finnegan, CFE ISO Innovative...
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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35
1 2 3 4 5 6 7 8 9
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Pattern Analysis such as Large Sum Deposited and Immediately Withdrawn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
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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Week
Cla
ims
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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1-6 7-12 13-18 19-24 25-30 31-36 37-42 43-48
Months
Ac
cid
en
ts
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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1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
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
$0
$200
$400
$600
$800
$1,000
$1,200
$1,400
$1,600
$1,800
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