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Innovations in Detecting Suspicious Claims 1 MEASURE, MANAGE, & REDUCE RISK SM.

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Presentation on theme: "Innovations in Detecting Suspicious Claims 1 MEASURE, MANAGE, & REDUCE RISK SM."— Presentation transcript:

1 Innovations in Detecting Suspicious Claims 1 MEASURE, MANAGE, & REDUCE RISK SM

2 MEASURE, MANAGE, & REDUCE RISK SM Agenda Impact of insurance fraud Resisting fraud effectively Building fraud detection solutions – Keep up with changing scams – Maximize value from structured data Business rules Predictive modeling – Leverage textual data assets – Exploit claim networks

3 Why Focus on Fraud? It is a big problem – of personal injury claims contain elements of fraud 1 – $50 to $100 of policyholder premiums go to pay fraudulent claims 2 It is widespread – Fraudsters operate across touch points and verticals – New entrants driven by the economy It keeps changing and morphing! 26% study conducted by the Insurance Bureau of Canada 2

4 MEASURE, MANAGE, & REDUCE RISK SM Resisting Fraud Effectively Corporate culture – Fighting fraud must be a core responsibility – Organizational measurements must be aligned e.g., fraud investigation impact on cycle time Effective process – Effective antifraud training programs – Well-defined processes for detection, referral, and investigation – Integration with technology/solutions Systematic fraud detection solutions – Best-in-class solutions that evolve to stay current – Multiple techniques to cover different angles and types of data

5 MEASURE, MANAGE, & REDUCE RISK SM Building Fraud Detection Solutions Understand Fraud red flags, schemes, and scams Build Systematic fraud detection mechanisms Score Process to score claims for fraud potential Refer Business thresholds to refer claims to SIU Evaluate SIU investigation and feedback on evolving scams

6 MEASURE, MANAGE, & REDUCE RISK SM Example Scams Staged auto accidents – Swoop-and-squat – Car in front of you stops suddenly – Wave-on – claimant indicates it is safe for you to merge or pull out of a parking space, but then runs into you Repair shop scams – Airbag fraud – bill for new airbags but replace with stolen or salvaged – Burying the deductible – inflate estimates to make insurer pay the deductible (collusion with insured) Owner give-ups – Owners report their used car stolen and then set it on fire. Total loss ensures insurance pays off the entire car loan Auto glass fraud – Bill for a windshield replacement when only a chip repair was done – Soliciting glass claims

7 MEASURE, MANAGE, & REDUCE RISK SM Scams Change and Evolve Increasing PIP fraud Rise in property scams (e.g., hail) Effects of the new economy – Auto give-ups – Glass claims Fraud costs in Ontario top those in other parts of the country… according to panelists at an RBC Insurance roundtable on fraud. Those costs represent an estimated $1.3 billion of $9 billion in premiums in the province, the insurance executives noted during the July 28 [2010] discussion… The average cost of a claim in Ontario rose from $30,000 in 2005 to $53,000 in 2009, according to Insurance Bureau of Canada (IBC) data. That’s markedly more than average claims costs in Alberta ($3,689) or Nova Scotia ($5,904). Fraud costs in Ontario top those in other parts of the country… according to panelists at an RBC Insurance roundtable on fraud. Those costs represent an estimated $1.3 billion of $9 billion in premiums in the province, the insurance executives noted during the July 28 [2010] discussion… The average cost of a claim in Ontario rose from $30,000 in 2005 to $53,000 in 2009, according to Insurance Bureau of Canada (IBC) data. That’s markedly more than average claims costs in Alberta ($3,689) or Nova Scotia ($5,904).

8 MEASURE, MANAGE, & REDUCE RISK SM Changing Scams Source - NICB ForeCAST Report - 3Q Referral Reason Analysis (Ann Florian, Strategic Analyst )

9 USING STRUCTURED DATA

10 MEASURE, MANAGE, & REDUCE RISK SM Structured Data in Claim Systems Policy details – Insured details (age, sex, etc.), # of years insured, p olicy inception date, etc. Loss details – Date and time of loss, location of loss, details of vehicles involved in loss, etc. Claimant details – # of claimants, injuries, treatment dates and amounts Representation – Attorneys involved (if any), date of engagement, etc.

11 MEASURE, MANAGE, & REDUCE RISK SM Business Rules: SIU Scorecard 1.For each claim, determine indicators that apply 2.Add the corresponding points 3.If total points > 99, refer to SIU 1.For each claim, determine indicators that apply 2.Add the corresponding points 3.If total points > 99, refer to SIU Scoring & Referral

12 MEASURE, MANAGE, & REDUCE RISK SM Predictive/Statistical Modeling Supervised models – If target flag (suspicious/not-suspicious) tags are available on a historical body of claims – Many model forms available Naïve Bayes models Decision trees Logistic regression Neural network classifiers Etc.

13 MEASURE, MANAGE, & REDUCE RISK SM Decision Tree for Fraud Detection All Claims (Fraud Rate 2%) # Clmts > 1 (5%) Insd Driver = Female (10%) Insd Vehicle = Luxury (25%) Clmt Vehicle = Older- American (70%) Clmt Vehicle = Older- Japanese (45%) Clmt Vehicle = Newer (10%) Insd Vehicle = Non- Luxury (7%) Insd Driver = Male (3%) # Clmts = 1 (1%) = Refer to SIU = Alert adjuster = Settle claim

14 TEXT MINING FOR ADDITIONAL LIFT

15 MEASURE, MANAGE, & REDUCE RISK SM NO PROP DMG FOR INS AND CLMT AS COLL HIT WAS LOW. CLMT CLAIMS INJ FROM AX AND TRTD W CP AND PT EXTENSIVELY. TX APPEARS EXAGGERATED. Text Mining Adjuster Notes IT APPEARS THAT THIS WAS A LOW-IMPACT COLLISION WHERE THE INSURED’S FOOT SLIPED OFF THE BRAKE, AND SHE ROLLED INTO THE REAR OF THE CLAIMANT. THIS IS CONSSTENT WITH THE FACT THAT THERE WAS NO PROPERYT DAMAGE CLAIM MADE TO THE CLAIMANT VEHICLE. UNDER THE CIRCUMSTANCES, HOW THE CLAIMANT COULD HAVE SUSTAINED SUCH SEVERE SHOULDER INJURIES AS A RESTRAINED DRIVER APPEARS RATHER SUSPECT. Low Impact Exaggerated Treatment Questionable Injuries

16 MEASURE, MANAGE, & REDUCE RISK SM Unique Insights in Text “Structurized” data – Structured fields created with codes/values extracted using text mining, e.g.: Near Highway Exit = Y/N Low Impact = Y/N INSD R/E CLMT VEH WHEN IT BRAKED SUDDENLY NEAR HIGHWAY EXIT. INSD THINKS SPEED OF TRAVEL ABOUT 25 MPH. INSD SUFFERED AIRBAG BURNS. MULTIPLE CLMTS IN VEHICLE WERE INJ BUT WAIVED AMBULANCE. Insured R/E Claimant Near Highway Exit No EMR and/or Ambulance Waived

17 MEASURE, MANAGE, & REDUCE RISK SM Better Detection with Text Mining All Claims (Fraud Rate 2%) # Clmts > 1 (5%) Insd Driver = Female (10%) Insd Vehicle = Luxury (25%) Clmt Vehicle = Older-American (70%) Clmt Vehicle = Older-Japanese (45%) Clmt Vehicle = Newer (10%) Insd Vehicle = Non- Luxury (7%) Insd Driver = Male (3%) Highway Exit = Y (15%) No EMR = Y (50%) # Clmts = 1 (1%) Low Impact = Y (5%) Exaggerated Treatment = Y (40%) = Refer to SIU = Alert adjuster = Settle claim

18 MINING NETWORK DATA

19 MEASURE, MANAGE, & REDUCE RISK SM Industry Data: ISO ClaimSearch® Workers Compensation Automobile Liability Medical Payments Personal Injury Protection Auto Medical Payments Homeowner’s Liability General Liability Disability Personal Injury Employment Practices D&O / E&O Fidelity and Surety Casualty >170 Million Property Homeowners Farm Owners Fire Allied Lines Commercial Ocean Marine Inland Marine Burglary and Theft Credit Livestock >36 Million Theft Claims Theft Conversions Vehicle Claim System (damage estimates from vendors) Shipping & Assembly Salvage Records Impound Records Export Data International Salvage and Thefts Auto >395 Million Insurers representing 93% of direct written premium, National Insurance Crime Bureau, and law enforcement agencies

20 MEASURE, MANAGE, & REDUCE RISK SM Querying Claim Networks ISO’s NetMap tool for link analysis and visualization

21 MEASURE, MANAGE, & REDUCE RISK SM Characterizing Network Measures ORA (Organizational Risk Analyzer) from the Center for the Computational Analysis of Social and Organization Systems at CMU Centrality Density Betweenness

22 MEASURE, MANAGE, & REDUCE RISK SM Network “Measures” Density – The number of edges divided by the number of possible edges not including self-reference Centrality – Nearness of an entity to all other entities Closeness – The inverse of the sum of the shortest distances between each entity and every other entity in the network Degree centrality – Entity with the most connections Betweenness – The extent to which an entity is directly connected only to those other entities that are not directly connected to each other – an intermediary, liaison, or bridge Etc.

23 MEASURE, MANAGE, & REDUCE RISK SM Network Measures Add Value All Claims (Fraud Rate 2%) # Clmts > 1 (5%) Insd Driver = Female (10%) Insd Vehicle = Luxury (25%) Clmt Vehicle = Older- American (70%) Clmt Vehicle = Older- Japanese (45%) Clmt Vehicle = Newer (10%) Density = High (80%) Density = Med (40%) Density = Low (2%) Insd Vehicle = Non-Luxury (7%) Insd Driver = Male (3%) Highway Exit = Y (15%) No EMR = Y (50%) # Clmts = 1 (1%) Low Impact = Y (5%) Exaggerated Treatment = Y (40%) = Structured data = Text-mined data = Network data = Refer to SIU = Alert adjuster = Settle claim

24 MEASURE, MANAGE, & REDUCE RISK SM Summary Undetected fraud impacts the bottom line Effective fraud detection requires – Corporate focus – Process and training – Effective tools and solutions Good solutions exist, but there is more to come – Cross-vertical fraud detection – New data sources (LPR data, cell phone data, etc.) – Geospatial data and technology – More innovations with predictive modeling, text mining, and network mining

25 MEASURE, MANAGE, & REDUCE RISK SM Feedback and Questions Send feedback to: – Janine Johnson – –


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