1. © Deloitte Consulting, 2005 Predictive Modeling – Panacea or Placebo? Cheng-Sheng Peter Wu, FCAS, ASA, MAAA CAS 2005 Spring Meeting Scottsdale, AZ May 16-19, 2005

2. © Deloitte Consulting, 2005 2 Agenda What is Predictive Modeling A Case Study of Successful Predictive Modeling - Credit Scoring Revolution From Credit Scoring to Predictive Modeling What Does Predictive Modeling Mean for Actuaries?

3. © Deloitte Consulting, 2005 3 What is Predictive Modeling What is predictive modeling? –Predictive modeling is an application of mathematical and statistical techniques and algorithms to produce a mathematical model that can effectively predict and segment future events

4. © Deloitte Consulting, 2005 4 Why is PM a Hot Topic? Is it just a new actuarial fashion? Is it just the “flavor of the month” that we all like to talk about at conferences but nobody really does? No to both! –It is a new addition to the actuary’s toolkit. –It is here to stay.

5. © Deloitte Consulting, 2005 5 Why is PM a Hot Topic? PM is a natural extension of what actuaries have done all along. –Use data to make predictions and forecasts. It allows us to add statistical rigor and additional info to traditional areas of actuarial practice. –Large scale data mining and multivariate analysis –GLM-based ratemaking –Stochastic Loss reserving

6. © Deloitte Consulting, 2005 6 Why is PM a Hot Topic? PM also allows us to broaden actuarial practice. –Underwriting models –Credit scoring –Retention and cross-sell modeling –Target marketing models –Agency monitoring tools –Other industries

7. © Deloitte Consulting, 2005 7 What is New About “Today’s” Predictive Modeling? Rapid advancement of cheap computing power Moore’s Law Year198019851990199520002004 Storage Cost per Megabyte $190 $ 70 $ 10 $0.90 $0.05 $0.001 Microprocessor Speed, MHz5-8163375200400

8. © Deloitte Consulting, 2005 8 What is New About “Today’s” Predictive Modeling? Availability of wide range of data from internal and external sources. “Data Mining”: “Data mining is a process that utilizes predictive modeling techniques to analyze large quantities of internal and external data, in order to unlock previously unknown and meaningful business relationships”

9. © Deloitte Consulting, 2005 9 What is New About “Today’s” Predictive Modeling? Development of new and powerful modeling and data exploration techniques –Examples: regression, GLM, neural networks, decision trees, clustering analysis, MARS,... –Explore complicated patterns in data such as non- normality, non-linearity, interactions, etc. Statistical analysis is no longer restricted to what you can do with pencil and paper... …or spreadsheets.

10. © Deloitte Consulting, 2005 10 What is New About “Today’s” Predictive Modeling? Multivariate analysis with large amount of data and many variables –Analyze multiple variables “simultaneously” instead of one or two at a time. –Use large amounts of data No need to use summarized data for actuarial analyses. –Create and analyze novel predictive variables.

11. © Deloitte Consulting, 2005 11 A Case Study of Successful PM

12. © Deloitte Consulting, 2005 12 Credit Score Revolution

13. © Deloitte Consulting, 2005 13 Why? Multiple Choice Progressive provided foosball tables and free snacks to their trendy, 20-something workforce Progressive built a compound Gamma- Poisson GLM model to design their class plan Progressive pioneered the use of credit in pricing/underwriting

14. © Deloitte Consulting, 2005 14 Credit Score Revolution Personal line rating history: –Few rating factors before World War II –Explosion of class plan factors after the War –Auto class plans: Territory, driver, vehicle, coverage, loss and violation, others, tiers/company… –Homeowners class plans: Territory, construction class, protection class, coverage, prior loss, others, tiers/company... –Credit scoring introduced in late 80s and early 90s

15. © Deloitte Consulting, 2005 15 Credit Score Revolution About credit score: –First important factor identified over the past 2 decades –Composite multivariate score vs. raw credit information –Introduced in late 80s and early 90s –Viewed at first as a “secret weapon” –Quiet, confidential, controversial, black box, …etc “Early believers and users have gained significant competitive advantage!”

16. © Deloitte Consulting, 2005 16 Credit Score Revolution Current environment of credit score: –Now everyone is using it: Marketing and direct solicitation New business and renewal business pricing and underwriting –Regulatory constraints: Many states have conducted studies on the true correlation with loss ratio and potential discrimination issues - WA study, TX study, MO study Many states have/are considering restricting the use of credit scores or certain type of information, MI. More states want the “black box” filed and opened

17. © Deloitte Consulting, 2005 17 Credit Score Revolutions What is “credit score”? –A composite score that usually contains 10 to 40 pieces of credit information Payment pattern information, account history, bankruptcies/liens, collections, inquiries, bad debt/defaults… Formula scoring or rule-based scoring Industry scores and proprietary scores

18. © Deloitte Consulting, 2005 18 Credit Score Revolutions Why “credit score” is so successful? –“Large scale” “multivariate” scoring using “external data source” –Loss ratio lift is significant, a powerful class plan factor or rate tiering factor –“Brilliant” marketing approach for credit score: Benefits/ROI are measurable and lift curve can be translated into bottom-line benefit Blind test and independent validation can be done to verify the benefit

19. © Deloitte Consulting, 2005 19 Loss Ratio Lift Curve 82 66 58 62 70 74 78 90 120 50 Credit Score Decile Loss Ratio

20. © Deloitte Consulting, 2005 20 Credit Score Revolution

21. © Deloitte Consulting, 2005 21 From Credit Scores to Predictive Modeling A credit score is just “one example” of an insurance predictive model The same methods used to build credit scores are used in data mining to build insurance predictive models: –Fully utilize all sources of internal and external data sources –Fully utilize all available data Not just credit –Other lines of business?

22. © Deloitte Consulting, 2005 What Does PM Mean for Actuaries?

23. © Deloitte Consulting, 2005 23 What Does PM Mean for Actuaries? New ways of analyzing data –New data sources –New technologies –New analytical tools –True Multivariate analysis No longer one or two variables at a time No longer one or two variables at a time –Analysis of risk-, policy-, or HH-level data, rather than aggregated data.

24. © Deloitte Consulting, 2005 24 What Does PM Mean for Actuaries? New emphasis on the “business” side of the analytical work and out-of-box thinking Who thought of credit a decade ago? How to stay competitive if everyone is using credit and GLM? What is the “next” big thing out there? Are you using the same “lift curve” and ROI concept in your analytical work? How do you tie in your model/analytical work to business benefit? Can you demonstrate the business benefits of your analytical work through a blind test? …etc

25. © Deloitte Consulting, 2005 25 What Does PM Mean for Actuaries? New challenges to “actuarial” methodologies and principles –Actuarial Ratemaking Principle #1: “A rate is an estimate of the expected value of future costs” –Actuarial Ratemaking Principle #4: ” A rate is reasonable, not excessive, not inadequate, and not unfairly discriminatory –But is that really the way profit-seeking companies price their products? Are rates ultimately based on costs or on what the market will bear?

26. © Deloitte Consulting, 2005 26 What Does PM Mean for Actuaries? New challenges to actuarial principles and methodology: –“Unfairly discriminatory”: If we develop a powerful new segmentation model, is it discriminatory to certain risks? If we don’t introduce it, is it discriminatory to other risks? How do we know if we don’t do the analysis? Actuaries’ “Static/Equalibrium” Principles vs. Business’ “Ever Changing/Dynamic” Principles

27. © Deloitte Consulting, 2005 27 Placebo or Panacea? So which is it? Not a placebo –PM is here to stay –A permanent addition to the actuary’s toolkit –Has the power to both deepen and expand actuarial practice. Not a panacea –PM complements, doesn’t replace fundamental actuarial principles –PM does nothing without sound business strategy and implementation.