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© 2014 Fair Isaac Corporation. Confidential. This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac Corporation’s express consent. Harnessing Network Analytics to Better Combat Fraud Nitin Basant Senior Scientist, Analytic Science FICO
© 2014 Fair Isaac Corporation. Confidential. Estimated Amount of Annual Losses Caused by Different Types of Fraud/Abuse (US) Credit and Debit Card Retail (Returns and Online Fraud ) Identity Theft Healthcare $125B$49B $12.5B $7.1B Insurance $30B 2
© 2014 Fair Isaac Corporation. Confidential. Networks Help Capture the Big Money Colorado $1 Million in car theft ring Florida $1 Million in worker’s compensation fraud ring New York $279 Million in insurance fraud ring 3
© 2014 Fair Isaac Corporation. Confidential. What Comprises an End-to-End Fraud Solution? Traditional AnalyticsSocial Network AnalysisInvestigation ► Claim/transaction/ provider level ► Rules and/or predictive models Data-Driven End-to-End Fraud Solution ► Network level analytics ► Investigative tool ► Manual referrals ► Alert/case management ► Link analysis 4
© 2014 Fair Isaac Corporation. Confidential. Statistical Outliers Domain Specific Rules Association with Known Bads Analytic Capabilities Predictive Analytics Fraud 5
© 2014 Fair Isaac Corporation. Confidential. High Scoring Claims, pharmacies, etc. Proactive Batch Network Scoring Prioritized Suspicious Network Leads Scoring Engine Link Analysis and Network Analytics All Claims, pharmacies, external data, etc. Proactive Detection NameSuzie Smith Address12395 Cedar Park Rd., Apt 225 CityCleveland StateOH Zip44102 Investigational Review Suspect(s) Matches and Visualization Search/Match Claims Cases Pharmacies Prescribers Members 3 rd Party Private 3 rd Party Public Search/Match Claims Cases Pharmacies Prescribers Members 3 rd Party Private 3 rd Party Public 6
© 2014 Fair Isaac Corporation. Confidential. ► Getting increasingly popular with insurance companies ► Manual/ad-hoc process ► Uses an identified suspicious case/person as seed to build networks ► Helps positively confirm suspicion ► Detection of additional cases ► Identify fraud rings ► Detect fraud centers and bridges between fraud rings Link Analysis 7
© 2014 Fair Isaac Corporation. Confidential. Network for Investigational Review 8
© 2014 Fair Isaac Corporation. Confidential. Attribute/AlgorithmsLoan ApplicantI-SCore™SIU File NameSusan Smith.894Suzanne Smyth Street Address Boerne Drive Bourne St - Apt 202 City, State, ZipAustin, Texas, TX/Round Rock/ SSN *****6789 Telephone Contact Ext 78 Date of Birth4/22/ Apr-70 EmployerTriple A Lawn Care.876AAA Lawn & Garden Aggregate I-SCore™ Match.921 How Identity Resolution Works 9
© 2014 Fair Isaac Corporation. Confidential. Myth: A Big Network Is a Bad Network 10
© 2014 Fair Isaac Corporation. Confidential. Proactive Detection Flow Act 5 Alert, Triage, and Investigate Search LinkAnalyzeMatch 1342 Federated Similarity Search Matching & Relationship Intelligence Social Network Discovery Fraud Network Analysis ! Social Risk Scoring and Triage Alert Processing Visualization ► Policy Data ► Policies, Policyholders, Coverages, Vehicles ► Claims Data ► Claims, Payments, Claimants, Vehicles, Medical ► SIU cases ► Third party data 11
© 2014 Fair Isaac Corporation. Confidential. ► Hypothesis: Inclusion of network variables in regular supervised models should improve the performance of the model ► Design of the experiment ► Used the data from a large auto insurance company ► Identified 20 network based variables ► Networks limited to 2 generations Inclusion of Network Variables in Traditional Models 12
© 2014 Fair Isaac Corporation. Confidential. Boosting Fraud Detection with Network Variables 13
© 2014 Fair Isaac Corporation. Confidential. Boosting Fraud Detection with Network Variables 14 Variable NameRank Vehicle model1 Total paid amount in the network2 Policy holder occupation3 Pre-accident value of the vehicle4 Total number of payments in the network5 Number of phantom vehicles in the network6 Zip code of the policy holder7 Size of the network8 Repairable flag9 Type of accident10 4 out of top 10 variables were contributed by the networks
© 2014 Fair Isaac Corporation. Confidential. ► Network mini-models based on suspicious patterns ► Association with a fraud claim/person ► Multiple fire/theft cases ► Third party claimant involved in multiple accidents ► Scores based on ► Number of cases confirming the match ► Generation ► I-SCore™ Network Models 15
© 2014 Fair Isaac Corporation. Confidential. MO #1: Writing scripts for self, family, friends, neighbors, etc MO #5: False store fronts MO #4: Excluded providers MO #3: Sharing member cards MO #2: Phantom provider Addressing Multiple Fraud Schemes Using Network Models Dr. Aaron Davison (684) Dr. Aaron Davison 20 Rx in last 12 months Dr. Aaron Davison Susan Pon Shared Patients Dr. Tabitha Wright Billing Relationship Dr. Janet Little Dr. Jason Smythe Billing Relationship Bank Account Dr. Tabitha Wright Dr. Jason Smythe Dr. Janet Little OIG LEIE 123 Main Street Setauket NY Smythe Patient First DME April 2013 – Oct 2013 Smythe Patient 1 st DME Oct 2013 – Mar Main Street Setauket NY Smythe DME April Dr. Jason Smith Bank Account Susan Pon 16
© 2014 Fair Isaac Corporation. Confidential. ► Fusion processing enables the reviewers to work off a single prioritized list ► Claims exhibiting suspicious patterns across multiple techniques are prioritized to the top ► While still enabling detection of fraudulent claims flagged by only a single technique Fusion Makes the Investigation Simpler Prioritized Leads for Triage Analysis FTM ODM Fraud- Trained Models (Supervised) Outlier Detection Models (Unsupervised) Network Models LA Fusion Processing 17
© 2014 Fair Isaac Corporation. Confidential. Score Weights Results In the first few runs of the fusion processing, 50–60% of the new referred cases were affected by the network scores Severity Network Score 1 Network Score 2 Network Score 3 Network Score 4 Network Score
© 2014 Fair Isaac Corporation. Confidential. FICO Recommended Fraud Solution Traditional AnalyticsSocial Network AnalysisInvestigation ► Claim/transaction/ provider level ► Rules and/or predictive models Data-Driven End-to-End Fraud Solution ► Network level analytics ► Investigative tool ► Manual referrals ► Alert/case management ► Link analysis 19
© 2014 Fair Isaac Corporation. Confidential. Learn Known Patterns to Filter ► E.g. proximity of incident date to policy inception date Learn New Patterns ► Previously unknown patterns to train on (e.g. new medical fraud patterns, new combos of diagnosis code and procedure code) Learn Additional Characteristics ► E.g. # addresses per claimant (via identity resolution) Learn New Fraud Tags ► Including from Outlier Detection Models and Network Models Analytic Techniques Learn from Each Other Claims Fraud Trained Models Outlier Detection Models Network Models Learn 20
© 2014 Fair Isaac Corporation. Confidential. Data-Driven Fraud Detections Universe of Fraud Supervised Models Network Models Unsupervised Models ► Network analytics help enhance traditional models as well as provide a way to go after new types of fraud ► Network models work with supervised and unsupervised model to cast a wide net of fraud detection 21
© 2014 Fair Isaac Corporation. Confidential. This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac Corporation’s express consent. Nitin Basant Thank You! 22
© 2014 Fair Isaac Corporation. Confidential. Learn More at FICO World Related Sessions ► Product Showcase: FICO® Identity Resolution Engine ► FICO Roadmap for Insurance Fraud ► Research Showcase: Exploring Predictions with a Powerful Tool Products in Solution Center ► FICO ® Identity Resolution Engine Experts at FICO World ► Michael Betron ► Liz Lasher White Papers Online ► Busting Fraud Rings with Social Link Analysis Blogs ► 23
© 2014 Fair Isaac Corporation. Confidential. Please rate this session online Nitin Basant 24
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