Detecting Suspicious Claims : An Operational Perspective

1. Detecting Suspicious Claims : An Operational Perspective Marty Ellingsworth Director, Operations Research Customer Research and Strategies Fireman’s Fund Insurance Company November 14, 2001

2. National Insurance Crime Bureau (NICB)Most common insurance fraud scams of 2000.Each has its own set of features for detection - if you can find them. • Bodily Injury Fraud • often associated with staged or caused auto accidents • involve fabricating physical injuries • often with dishonest doctors and lawyers (conspiracy and collusion) • Auto Repair Fraud • claimant gets high appraisal, in cooperation with an unscrupulous repair shop • gets a vehicle repaired; pocketing the difference • Homeowners Claim Fraud • arson for profit • fabricating claims for phony burglaries • padding of legitimate claims for theft or damage to the home • Workers Compensation Fraud • faking injuries or exaggerating the extent of a minor injury • claiming work relatedness for an injury sustained at home

3. Exercise on problem solving: Move a tree • Define the problem • Formulate a solution • Get the ‘right’ tools • Learn how to use them • Adapt to the situation • Assess the results of your actions • Make improvements Taking action on an individual claim can be challenging.

4. Fraud and Abuse: Problem Segmentation Many current data tracking systems can not delineate the specific behavior(s) that resulted in the claim going to the SIU, nor its ultimate outcome. Because of this, all of these different ‘signals’ get lumped together for modeling historical SIU as Yes/No. • Claimant Opportunistic Build-up • Exaggerating / Padding / Inflating / Rounding • Fictional Claims • Premium Fraud • Adjuster Fraud • Agent Fraud • Identity Fraud • Organized Crime • Planned / Staged Accident • Attorney/Provider Collusion • False Billing • Previous Damage/Injury • Faking Disability • Not related to the accident

5. ? ? ?? Presumed Legitimate Claimant “Build -up” Padded Estimates Exaggerated Lost Earnings Inflammatory Red Flag Being on “Watch List” False Identity Stolen goods Faked the Loss Caused the Accident False/exaggerated Disability Match to ‘Bad Guy’ Data base Large Data base Link Analysis Connected to a Crime Ring Multiple Red Flags Collusion, Conspiracy, Extensive Claims History Look for fraud in the “ Life of a Claim “ Fraud and abuse can occur at any time during a claim. Investigate Evaluate Negotiate Settle

6. How do we detect fraud and abuse? “Adjuster Centric” referral systems often do not collect data electronically and frequently do not get applied consistently between adjusters over the life of the claim. Many different methods of intelligent data gathering and analysis can be successfully employed for effectively detecting fraud. • Training Adjusters • Claim-based “Red-Flags” • Manual, On-line, or Batch Processing • Database Submissions and Searches • Automated, Directed • Expanded Data collection and feedback of claim outcomes • Expert Systems (Bill Review) and Business Rules • Statistical Modeling • Likelihood, Outliers, Dissimilarity within latent groups, • Variance from Expected Behavioral ‘Signature’ • Visualization • Timelines, Geographic mapping • Link Analysis (especially with Industry Databases)

7. What can we do to resist fraud and abuse? If a claim is suspected of ‘hard’ fraud, then we should work with Federal, State, and Local authorities to resolve the claim - both criminal and civil issues. In many cases, build-up claims are negotiated by the adjuster after considering the medical damages submitted. Oftentimes, the SIU is not involved, or it is notified after all of the medical treatment has accumulated. With Evidence of Fraud: Close Claim with no payment Refer Claim to Authorities Assist in criminal prosecution Seek civil damages

8. Lessons LearnedWe need automated referrals made on timely, accumulated information to be most effective in resisting fraud and abuse, and to get the most efficient productivity from our resources. • Data mining can add considerably to the Manual / • communication methods now in place • Time is of the essence for making an impact on treatment • Big hurdle in initially building a data set for analysis • Company skill set, hardware, and dedicated resources • Some important factors were not historically collected • Text Mining as an information extraction tool is quite valuable • Fielding sophisticated models can depend significantly on IT • Continue to collect feedback on referrals to improve models over time • Industry data would be useful for moving beyond claimants.

9. Case Example: Auto 3rd Party BI Business Objective Reduce Unnecessary Losses Paid Due to Fraudulent and Abusive Claims Increase Efficiency of SIU Resources - Sharpen our recognition of potentially fraudulent claims (find more claims) - Shorten the time it takes to get an SIU resource involved (find them quicker) - Reduce unqualified referrals generated by quotas - Reduce time spent by SIU staff on training adjusters LIKELY ACTION STEPS Interdiction of build-up during treatment Negotiate ‘Build-Up’ Litigate ‘Hard Fraud’

10. 3rd Party ABI Fraud - Classification of Suspicion • The historical data show suspicion (1) or not suspicion (0) as indicated by the SIU’s non-administrative presence in a claim • Many different methods can be applied to rank order claims to differentiate highly suspicious claims from not suspicious. • We decide to send a claim for SIU review based on a precision criteria • “Fast Track” claims can also be filtered • Precision and Recall criteria can be balanced to SIU resource availability Predicted 100% Fraud No Fraud True Positives (want to maximize) False Negatives (want to minimize) Fraud Suspicion of Fraud Observed False Positives (want to minimize) True Negatives (want to maximize) No Fraud 0% Low High Level of Independent Variable(s)

11. Data Mining Methods The complex resources needed to attack many of the fraud segments leave many insurers using ‘low tech’ red-flag systems and emphasizing better communication between adjusters and investigators. Data mining adds summarized data information to the process for better results. Conditional Logic Discovery Patterns and Associations Trends and Variations Data Mining Predictive Modeling Outcome Prediction Deviation Detection Forensic Analysis Link Analysis

12. What makes a Claim look suspicious?Across business lines similar themes evolve which highlight claimant behavior associated with fraud and abuse claims. Non-claimant fraudsters are much more difficult to pinpoint. • INCONSISTENCY • DENYING PRIOR CLAIM HISTORY • UNCOOPERATIVE // TOO COOPERATIVE • TIME LINE OF EVENTS • DETAILS FOR SETTLEMENT (TOO MANY/TOO FEW) • CIRCUMSTANCES UNLIKELY

13. YYNNYY = 85% Data Exploration: Secret of the Red FlagsA few of the Red-Flags have individual strength in indicating the need for an SIU triage, but most are in the 15 - 30 % ‘Hit Rate’ range. Combining responses into answer vectors can dramatically increase the ‘Hit Rate’. Claimant is demanding an unusually quick settlement 18% Claimant is unusually familiar with insurance terms / procedures 12% Multiple unrelated claimants were represented by the same attorney 35% The claimant’s vehicle was damaged in a prior accident 13% The claimant has been involved in other accidents in the past 3 years 21% The facts of the accident cannot be confirmed 16% The insured felt set up 19% Medical bills lack the detail needed to properly evaluate the claim 14% Claimant refuses to provide information or submit to an IME 16% Claimed injuries are inconsistent with the facts of the accident 20% Treatment received is inconsistent with the claimed injuries 32%

14. What it takes to Create the Data Set Business Line Exec Field Office Staff Claims Trainers CLAIMS Data Analyst Data Collection: I S and Analyst Project Leader Datamart Programmer MEDICALCOSTCONTAINMENT Domain Expert Knowledge: - Auto - GL - Property - Work Comp Personal Commercial SPECIAL INVESTIGATION UNIT RECOVERY UNIT UNDERWRITINGPOLICY DATA

15. What to learn from Structured Data Significant pre-processing of raw data is needed for creating useful informational features out of existing structured data. Rolling-up payment transactions, and collecting and integrating detailed medical bill data with the claim data can result in powerful predictive variables. • Repeatable Patterns • Trends, Seasons, Cycle • Propensities, Likelihood • Causation and Interaction • Ratios between Dollars and Distances • Stakeholder Behavior • Unlikely Occurrences

16. Sophisticated Transformation of Data Data mining end-work-product data record is optimized for outcomes analysis. In this case, everything is rolled up/down to the third party claimant. Claim / Policy / Development / Review / Treatment / Savings /Fees / Provider Claim Master File Policy System File(s) Claim Payment Detail File Claim Reserve History File Bill Review Header File Bill Review Bill Detail File Supplemental Sources Provider/Vendor File(s) ISO, NICB, Litigation Sub-system

17. The Claims “Checkbook”By integrating our claims data with our medical bill review vendors’ data, we can see to whom, when, and where our money is going. This ‘follow the money’ process will give us the details for tracking patterns of collusion in our claims, but with only a fraction of the market share, we’ll need to access Industry data to identify organized rings. Claim System Claim File $x,xxx.xx Bill Review Vendor Payments Medical Payments Medical Bill Review Systems Bill Record Indemnity Payments Expense Payments Bill Line Item Detail • Reduction Reasons • Charged versus Paid • Bill Review Rule • Fee Schedule • U&C Repricing • PPO Discount • Other Savings Reserves Bill Review Rule Reasons Use Review Reduction reasons for negotiating damages.

18. What to learn from Unstructured Data To segment types of fraud and to baseline which Red Flag questions help the most, you can process the ‘free text’ fields in the claim administration system. Both “Text Mining” and Natural Language Processing methods can extract actionable information from text data. • Claim file coding leaves a lot to be desired. • Powerful new variables can be created for millions of claims • without the cost and time lag of manual review • Notes in the file are indicative of events of special interest • - suspicious behavior - legal representation • - subrogation opportunity - injury severity • Notes are “time stamped” so we can see chronologies

19. “felt set up” “suddenly stopped” DATE OF LOSS PROGRESS NOTE Repped in less than 4 Days DESCRIPTION OF LOSS Inconsistency Minor Impact v. Severe Injury RESERVES Text Mining Task - Extracting Information from unstructured data in the claim file DATE OF LOSS 11/07/99 PROGRESS NOTE ProcID Name Date 409F123 Ima Phile-Hanler 11/11/99 “Insured said that they felt set up, this was a mild impact in heavy traffic that happened when the claimant suddenly stopped while other traffic kept moving. Claimant is represented.” DESCRIPTION OF LOSS “ Minor RE in Heavy Traffic” RESERVES ABI $7500 ProcID Name Date

20. 5% 91% Current Detection Capability: Auto 3rd Party BI Using the best of structured and unstructured data features we are able to create a very strong rank ordering of cases for the SIU to review. In our research, 76% of all the historical ‘Bad Guys’ for Auto 3rd Party Bodily Injury claims are found in the top 15% of the ranked cases.

21. Where to next? Change the paradigmA claim can be considered a document where information is added over time until complete. Breakthrough thinking -- you can dynamically route claims much like a newswire subscription service classifies and routes in-coming stories, or like an internet search engine finds web-sites which have the content you want to see. Field additional rules and scoring engines. Search for more powerful predictors. Continuous collection of data and feedback of results. Extend the practical ability of classifying claims using text mining indexing strategies. Pursue using ‘web spidering’ technology to combine information extraction enhanced models with real-time indexing of claim notes for fast and efficient recognition of claims of interest. Integrate feedback loops for a spider based inference engine to dynamically route claims based on emerging information in the file For non-claimant fraud, we will explore methods to combine information with larger data sets to better enable data mining techniques to reach the next level. Name and address standardization and parsing is needed, and ‘similarity’ engines will be invaluable for finding people trying to hide their identities.