Predictive Modeling for Disability Pricing May 13, 2009

1. Predictive ModelingforDisability Pricing May 13, 2009 Claim Analytics Inc. Barry Senensky FSA FCIA MAAA Jonathan Polon FSA www.claimanalytics.com

2. Agenda • About Claim Analytics • Introduction to Predictive Modeling • Disability Pricing • Discussion

3. About Us • Founded in 2001 by two actuaries • Objective: Apply predictive modeling technology to insurance questions • Current Disability Products • Claim scoring • Claim reserving • Pricing • Benchmarking

4. About Us (Cont’d) • Clients in Canada and U.S. • Blue Cross Life • ING Employee Benefits • Lincoln Financial • Mutual of Omaha • Principal Financial • Sun Life Canada • Sun Life US • UNUM • Several new product initiatives

5. Introduction to Predictive Modeling

7. Computer Performance

8. What is a Predictive Model A Predictive Model is a model which is created or chosen to try to best predict the probability of an outcome Have been around for 40+ years Harnesses power of modern computers to find hidden patterns in data Used extensively in industry Many possible uses in insurance:

9. About Predictive Models May be parametric… apply numerical methods to optimize parameters E.g., gradient descent, competitive learning Or non-parametric often have a decision tree form typically optimized using exhaustive search

10. Predictive Modeling Tools Some common techniques Generalized linear models Neural networks Genetic algorithms Random forests Stochastic gradient boosted trees Support vector machines

11. Why aren’t Actuaries building modern predictive models? • Life Insurance Industry is conservative and slow to change • Not a traditional actuarial tool • The times are changing! • Especially P&C Actuaries • Its only a matter of time! • It just makes too much sense! • Innumerable applications to help solve insurance problems

12. Disability Pricing

13. Current Industry Approach • Traditional actuarial methods focus on one, maybe two risk factors at a time • Solve for one factor, then move to the next • Unable to account for correlations and interactions between rating variables • Example: region and industry may be highly correlated and may interact • Lots of uncertainty in rates

14. Uncertainty in Current Rates Quotes for Claim Analytics employee LTD benefits • No consistency between insurers • Does anyone have confidence in their rates?

15. Predictive Modeling Approach • Ideally suited to multivariate analysis • Adjust for correlations between variables • Facilitate analysis of interaction effects • Uncover and quantify complexrelationshipsbetween risk factors and claim experience • Maintain wholeness of data • Improvedaccuracyvs traditional methods • Greater confidence in rates

16. Recent Project Highlights • Identified a key rating variable that was not priced for in current rates • Identified and quantified two-way interaction effects between rating variables • Better quantification of all effects • Significant improvements compared to existing rates

17. Data Requirements • Exposure data: • Census: age, gender, salary • Plan features: EP, ben%, benefit period, etc • Group info: SIC, region, size, etc • Claim data: • Policy #: link to plan features and group info • Claimant info: age, gender, salary, benefit • Cost estimate: PV benefits paid plus reserve

18. The Objective • Apply predictive modeling to: • Predict claim incidencerates • Predict claim severity, conditional upon claim being made • for each memberof census data

19. Rate Structure • Flexible, client-defined • Can be the same as current • Most common structure is base rates and multiplicative loadings • Iterative process: • Test multiple structures • Test several rating variables

20. Base Rate Approach • Base rates are typically a function of: age, gender, EP and max benefit period • In low dimensions, with sufficient data, traditional graduation approach works well • Or, predictive modeling can be used if data is sparse or heterogeneous

21. Multiplicative Loadings • Generalized linear models (GLM) can be used to optimize loading factors • Accurate, yet efficient  can test several combinations of rating factors • Significant and insignificant rating factors are identified • Interactions between rating factors can be quantified

22. Generalized Linear Models • Predictive modeling technique • Industry standard for P&C insurance • Generalization of classical linear regression • Computationally efficient • Performs very well for multiplicative models

23. GLM At A Glance • Similar to linear regression except that effects are additive on a transformed scale • Transformation occurs through a link function, g(x) • Log-link function results in multiplicative effects: g(x) = ln(x) g-1(x) = ex • μi = g-1(Β1xi1 + …+ Βpxip) = exp(Β1xi1) * … * exp(Βpxip)

24. The Final Rate Manual • Predictive modeling is a decision supporttool • Pure factors can be manually adjusted: • Marketplace pressures • Strategic considerations • Cost of adjustments can be quantified

25. A Marketplace Advantage • Predictive modeling facilitates better decisions • Identify rating variables that the market misprices • Where market overprices, can choose to be aggressive or to keep excess profits • Where market underprices, can choose to avoid unprofitable business or at least know the cost of writing • Better business mix • Greater confidence in rates supports decisions as to when and how much to discount

26. Predictive Modeling Benefits • Improved accuracyandconfidence • Accurately account for correlations and interactions between rating variables • Facilitates analysis of new rating variables • General rate structure can remain the same • Maintains flexibility in final rates

27. Discussion