Premium Audit Predictive Modeling 2008 PAAS Forum

1. Premium Audit Predictive Modeling2008 PAAS Forum June 2, 2008

2. Agenda What is Predictive Modeling? Premium Audit Challenge and Opportunity ISO Premium Audit Model Development Future Direction

3. Who is iiA? • An independent modeling unit within ISO • Dedicated resources focused on developing and enhancing analytic capabilities to meet market needs • Combines senior-level actuarial, data management, predictive modeling and product development expertise • Calls upon resources as needed across ISO

4. ISO Philosophy for Addressing Needs • Enhance company efforts with tools • Support your company by tapping into experience at all levels of ISO • Explore new data sources and make available in easy to use formats • Engage in active testing and consultation adding to a long term value proposition

5. What is Predictive Modeling?

6. What is Predictive Modeling? • Predictive Modeling • Leverages machine learning techniques and statistical analyses of past experience to generate accurate predications of future results • Provides actionable decision making information • Used across multiple industries for Cross-sell, Retention, Acquisition, Credit Extension, etc External Data Resources Accurate Prediction of Future Results Historic Data Advanced Analytic Methods Expert Risk Modelers

7. Predictive Modeling in Insurance • Predictive Modeling can be used at any decision point in the Insurance Lifecycle • Used extensively for underwriting and rate making • Early adopters enjoyed a competitive advantage • Ratemaking and U/W often employ most of the available analytic resources • Difficult for other areas to get the attention they need Marketing Analytics Operations Analytics Insurance Lifecycle U/W & Actuarial Analytics

8. Advantages of Predictive Modeling Predictive Modeling can … • Efficiently examine more possible factors • Take into account interactions between factors • Class Code within certain industries may be more important than same class in different industry • Give different predictors different relative importance • Efficiently examine and use all the historical data available Results in superior predictions and decision making!

9. Critical Success Factorsfor Predictive Modeling • Technical Expertise • In Statistical Modeling, Data Mining, and Data Management • Resources to build predictive models difficult to get allocated • Intimate Market Awareness • Strong Coordination • With other company units • Underwriting, Loss Control, Claims, Sales/Agents • Senior Executive Commitment and Support • Access to Data ISO and iiA possess the resources to address these success factors and deliver Predictive Model solutions for a number of insurance problems

10. Premium Audit Challenge and Opportunity

11. Premium Audit Challenge Audit Method for Closed Evens Audit Outcome for all Audits Voluntary: 78% of Closed Evens RP: 46% Close Even: 8% AP: 46% Estimated Physical • Premium Dollars are being left on the table • Under-reporting of payrolls, misclassification of workers, and manipulation of Ex-Mods costs hundred of millions of dollars each year • Closed Even Problem • Why would an account Close Even? • Physicals usually up or down outcome • Voluntary Audits account for majority of Closed Evens* • Income Tax Analogy • Need to identify these issues ahead of time *Source:: iiA Historical Data for Industry Model

12. Limited Resources People – need to train Recruiting/retaining Limited Time Decision on whether and/or how to audit Limited Funds Need to show value of audit process ROI More work than people Workers Comp Predictive Model Development Group Identified Auditor Concerns Premium Audit Challenge • Pressures • Time, turnaround, goal attainment • Identify "best bang for buck" audits • Measure of Audit’s value/success • Market getting softer (turning) • More written premium • More policies • Less accuracy • More “catches” from audit Key need is to efficiently allocate scare resources to maximize Premium Audit’s value proposition

13. Premium Audit Opportunity • More Premium Dollars Faster • Identify biggest bang for the buck • Better Allocation of Premium Audit Resources • Quality audits selected vs. quantity of audits selected • Easier workload management • Make auditor more effective • They know what to look for (e.g., ask the right questions) • Smarter Underwriting • Data mine results and transfer to underwriting to get premium upfront • Auditor training based on the information from the model

14. Tapping the Opportunity • Premium Audit Models allow you to tap into these opportunities • Decision strategies from model: • Ordering of audits can be optimized (within contract parameters) to maximize NPV of audit activities • Example: • Premium Audit group produces annual net AP of $100 million • Move cash flow forward 30 days by putting the largest APs at the head of the queue… • Almost $1 million can be generated from increased investment returns alone! ** • Decide which accounts to audit based on expected additional premium generated (where allowed by statute and business rules) • For those accounts that are audited, determine the most efficient allocation of mail, telephone, and physical audits • Realize biggest bang for your buck from physical audits • Reduces total cost to perform audits, which increases NET result ** Assumes 12% annual investment return

15. ISO Premium Audit Model Development

16. Model Development • Model development is progressing rapidly • Historic data has provided by several development partners • Partners supply data in exchange for a customized model • Data experts have been extracting, formatting, cleaning, and understanding the submitted data • iiA has been identifying, licensing, and managing the required data from third party data sources

17. DevelopmentPartners • Select group of insurers • Insurers have already submitted data • Additional insurers have committed to providing data • Provides data to be combined with industry, ISO and third-party data • 5 policy years of data • Free customized version of the model • Data not shared with others • Unique data used for insurer’s custom model only

18. Key Carrier Data • Audit Method Type • Physical • Voluntary/Mail • Telephone • Internet • Service Audits • Waived • Auditor Code • Direct employee (company auditor) (code) • Outside audit service company (code)

19. Key Carrier Data (cont’d) Ranked Criteria for Selecting Physical Audits Type of Risk Premium Size Regulatory Requirements New Business Prior Audits Claims History Location of Risk Auditor Experience Auditor Workload Cost of Auditing

20. Key data features to Construct or Collect • Industry Type • Governing Class • Price Per Exposure • Change in Rate for Gov. Class • Claim Frequency vs. Norm for Industry / Class • Loss ratio vs. Norm for Industry / Class • Claimant class code not listed on payroll • Same claimant – multiple claims • Multiple claimants – multiple claims • Revenue and payrolls not aligned (productivity) • “False W2s” • Only severe claims reported • Comparison: Average weekly wage reported vs in audit

21. Deriving Data = Power Depending on the target variable, there are many factors that may be relevant for modeling. • Totals: Total Exposures • Trends: Rate of Medical Bill Increases • Ratios: Claims/Premium, Target/Median • Friction: Level of inconvenience, ratio of rental to damage • Sequences: Lawyer-Doctor, Auto-Life Policy • Circumstances: Minimal Impact Severe Trauma • Temporal: Loss shortly after adding collision • Spatial: Distance to Service, proximity of stakeholders • Logged: Progress Notes, Diaries, • Who did it, When, “Why”

22. Deriving Data = Power (Cont’d) Depending on the target variable, there are many factors that may be relevant for modeling. • Behavioral: Deviation from past usage, spike buying • Experience Profiles: Vendor, Doctor, Premium Audit • Channel: How applied, How reported, Service Chain • Legal Jurisdiction: Venue Disposition, Rules • Demographics: Working, Weekly wage, lost income • Firmographics: Industry Class Code Vs Injuries Claimed • Inflation: Wage, Medical, Goods, Auto, COLA • Gov’t Statistics: Crime Rate, Employment, Traffic • Other Stats: Rents, Occupancy, Zoning, Mgd Care

23. Premium Audit Model Development • Potential Model Components • Raw and derived variables from below data sources • Final Model will include all predictive factors • Preliminary models developed with promising results Premium Audit Industry Model Data Examples Industry, Third Party and ISO Data Crime & Demographics Wage & Employment Measures Injury and Illness Rates Insured Financial Condition Business Rating Information Nearby Businesses Macro Economic Indicators Premium, Loss and Audit Variables from carrier historical data Policy Attributes Historic Audit Experience Insured Attributes Risk Location Claims

24. Optimizing Audit Timing Audit Results Randomly Distributed • Cash Flow Comparison for Mandatory Audits* Simulated Cash Flow • Based on hypothetical $500MM book • Policies ordered by expiration date and # days to complete audit • Simulates when cash hits door • AP and RP audits randomly distributed over time *Mandatory defined by statute or > $10,000 premium

25. Optimizing Audit Timing AP Audits Completed First • Cash Flow Comparison for Mandatory Audits* Optimized Cash Flow • Based on hypothetical $500MM book • Policies ordered by expiration date and model score • AP audits completed first • Removes randomness • Improves NPV *Mandatory defined by statute or > $10,000 premium

26. Optimizing Audit Timing Improved NPV • Cash Flow Comparison for Mandatory Audits* Alternative Cash Flows • Based on hypothetical $500MM book • Both methods arrive at the same Gross AP • Divergence of cash flow lines results in a positive NPV impact • Represents ideal scenario • Ordering should be modified to accommodate audit requests from agent, insured or business *Mandatory defined by statute or > $10,000 premium

27. Optimizing Discretionary* Audits • Match Audit Method with Potential to Generate AP • Maximizes AP dollars while minimizing costs • High AP Potential warrants greater Audit expense for a supervised result • Use telephone or voluntary audits for policies with Low AP potential • Use voluntary (or waive if possible) audits for policies with RP Potential Optimization rules can be changed to fulfill requests by insured, agent or to accommodate existing business constraints /rules *Discretionary defined by statute or < $10,000 premium

28. Optimizing Discretionary* Audits Optimized • Example based on hypothetical $500MM book Before Optimal strategy is to match Audit method with AP Potential (fill the yellow boxes) • Assume the following capacity requirements exist: • Number of Physicals can only vary by ± 10% • Limit of 1,000 Telephone Audits *Discretionary defined by statute or < $10,000 premium

29. Optimizing Discretionary* Audits • To meet operational requirements • Move top 20% of “Low AP” policies to Physical Audit • Limit Telephone to a Maximum of 1,000 • Voluntary on remainder Operational Requirements *Discretionary defined by statute or < $10,000 premium

30. Optimizing Discretionary* Audits % Change in Audit Premium and Costs 60.0% 50.0% 40.0% % Change in $ 30.0% Change in Change in AP: 55.1% Cost: 4.0% 20.0% 10.0% 0.0% Change in AP Change in Cost • Increases Additional Premium • Empirical analysis shows that supervised audits result in more AP compared to unsupervised audits for similarly scored policies • Costs remain essentially equal • Total number of Physicals did not change much • Simply selected better quality audits • Premium Assumptions: • Physical: $1060 • Telephone: $120 • Voluntary: -$180 • Cost Assumptions: • Physical: $250 • Telephone: $100 • Voluntary: $50 *Discretionary defined by statute or < $10,000 premium

31. Potential Financial Impacts • Realized financial benefits will vary by carrier • Depends largely on sophistication of current decision making strategies • Initial analysis suggests an increase of 3% - 7% in Premium Audit Dollars can be achieved • Example: • Premium Audit function produces $100 MM per year • 5% lift on PA activities yields additional $5MM year over year • Over 5 years this is an additional $25MM. These results can be achieved simply by doing what you do everyday…. But doing it more effectively!!!

32. Future Direction

33. Expected Timeline • Expected timeline dependent upon receipt of necessary data • Currently the first version of the Industry Model is expected to be released in Q1 2009 • Industry Model available as a standardized service • Custom Models available on a consultation basis

34. Expected Architecture • Anticipated architecture will be batch scoring of policies • ISO will warehouse all data necessary to score policies • Includes historical experience as well as third party data • Carriers will submit policies for scoring on a periodic basis • Monthly / Quarterly • Can be based on policy effective dates or expiration (cancel) dates • Scored policies will be returned to carrier via electronic transfer • Scores can be augmented with pre-determined strategies • Example: • Carrier wants to physically audit all policies with construction classes • If the policy scores “high” OR the policy has a construction class, the strategy will be to physically audit Policy System Policy / Audit Data Audit System ISO Premium Audit Server Scored File

35. Future Directions • Expand into GL and Commercial Auto • Leverage Model and Infrastructure as a Premium Fraud solution • Enhance database to track individual insured’s across carriers • Identify those that have a questionable history

36. What You Should Take With You… • No matter how many controls you have in place, $$$$ is being left on the table • Technology and expertise is available • Insurance industry rapidly adopting predictive analytics technology • Nothing to lose…and everything to gain

37. Questions?

38. Thank you. For more information please contact: Sharon L. Carney Assistant Vice President Premium Audit Advisory Service (201) 469-3139 SCarney@iso.com