Innovations in Detecting Suspicious Claims

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

2. 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? 26% 1 2001 study conducted by the Insurance Bureau of Canada 2 http://www.infoassurance.ca/en/preventing/automobile/fraud.aspx • It is a big problem • of personal injury claims contain elements of fraud1 • $50 to $100 of policyholder premiums go to pay fraudulent claims2 • It is widespread • Fraudsters operate across touch points and verticals • New entrants driven by the economy • It keeps changing and morphing!

4. 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. Building Fraud Detection Solutions 1 2 5 3 4

6. 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. Scams Change and Evolve 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). • Increasing PIP fraud • Rise in property scams (e.g., hail) • Effects of the new economy • Auto give-ups • Glass claims

8. Changing Scams Source - NICB ForeCAST Report - 3Q Referral Reason Analysis (Ann Florian, Strategic Analyst )

9. using Structured DATA

10. Structured Data in Claim Systems • Policy details • Insured details (age, sex, etc.), # of years insured, policy 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. Business Rules: SIU Scorecard Scoring & Referral For each claim, determine indicators that apply Add the corresponding points If total points > 99, refer to SIU

12. 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. Decision Tree for Fraud Detection = Settle claim = Refer to SIU = Alert adjuster

14. Text mining for additional lift

15. 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. Questionable Injuries Exaggerated Treatment Low Impact 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.

16. Unique Insights in Text 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 • “Structurized” data • Structured fields created with codes/values extracted using text mining, e.g.: • Near Highway Exit = Y/N • Low Impact = Y/N

17. Better Detection with Text Mining = Settle claim = Refer to SIU = Alert adjuster

18. Mining NETWORK DATA

19. Casualty Property Auto • Homeowners • Farm Owners • Fire • Allied Lines • Commercial • Ocean Marine • Inland Marine • Burglary and Theft • Credit • Livestock • Theft Claims • Theft Conversions • Vehicle Claim System(damage estimates from vendors) • Shipping & Assembly • Salvage Records • Impound Records • Export Data • International Salvage and Thefts • 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 Industry Data: ISO ClaimSearch® >170 Million >36 Million >395 Million Insurers representing 93% of direct written premium, National Insurance Crime Bureau, and law enforcement agencies

20. Querying Claim Networks ISO’s NetMap tool for link analysis and visualization

21. Characterizing Network Measures Density Centrality Betweenness ORA (Organizational Risk Analyzer) from the Center for the Computational Analysis of Social and Organization Systems at CMU

22. Network Measures Add Value = Structured data = Text-mined data = Network data = Settle claim = Refer to SIU = Alert adjuster

23. 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

24. Feedback and Questions • Send feedback to: • Janine Johnson • +1.415.276.4105 • e-mail: janine.johnson@iso.com