Combining Linear and Non-Linear Modeling Techniques: EMB America. Getting the Best of Two Worlds

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1 Combining Linear and Non-Linear Modeling Techniques: Getting the Best of Two Worlds

2 Outline Who is EMB? Insurance industry predictive modeling applications EMBLEM our GLM tool How we have used CART with EMBLEM Case studies Other areas of expected synergies 2

3 EMB Worldwide Global network of p&c insurance consultants servicing clients throughout the world 3

4 Consulting Services Offered Predictive Modeling Regulatory Support & Law Analysis Ratemaking & Profitability Analysis Expert Witness Testimony Underwriting & Credit Scoring Software Development & Software Support EMB Enterprise Risk Management, Pro Forma, Business Planning Reserve Analysis & Opinion Letters Retention & Conversion Modeling Reinsurance Program Analysis New Program Development Competitive Analysis 4

5 State-of-the-Art Software EMB s suite of software products cover all aspects of personal and commercial lines of insurance EMBLEM GLM software for risk, marketing, and claims analysis ExtrEMB Dynamic parameterization for risk modeling Rate Assessor Pricing implementation software ResQ Professional Complete loss reserving tool Classifier Categorization software for high-dimension variables (e.g., territory) PrisEMB Reinsurance and large account pricing Igloo Professional Financial simulation engine for risk modeling RePro Management information analysis software for excess of loss insurance and reinsurance 5

6 EMBLEM We use EMBLEM, a GLM tool, for our predictive modeling needs Why? 6

7 Predictive Modeling in the Insurance Industry Primary application: - Estimating the cost of the product they sell (insurance) Two steps: 1. Reserving = estimating the cost of outstanding insurance claims 2. Pricing = estimating the cost of future insurance coverage Secondary applications: - Retention Modeling = probability that a policyholder will renew - Conversion Modeling = probability that a prospective policyholder will purchase a policy - Price Optimization - Claim fraud detection - Marketing 7

8 Estimating the Cost of Insurance Goal is to develop a unique rate for every risk - Don t think in terms of good/bad risks - State Farm/Allstate vs GEICO/Progressive - Quickly exhausts the data credibility / variability / stability Risks are described by the predictor variables, not the target - Need to have a mapping of the predictor variable levels to a target value not the other way around Other way around makes it difficult to derive impact of individual predictor variables Important because actual data often does not describe all possible combinations of potential customers 8

9 Estimating the Cost of Insurance Highly regulated marketplace - Restrictions Predictors can and cannot use Credit score Rules on values for the predictors Ages 65+ relativities cannot be >110% of ages Maximum rate change between adjacent territories Rules on predictor order and magnitude of importance CA Sequential Analysis (driving record > annual mileage > years held license) - Regulatory Approval Rates need to be supported Black box methodologies will not be accepted 9

10 Estimating the Cost of Insurance Response variable is continuous/discrete function Density: Severity Frequency: Frequency 1, ,000 90,000 1,200 80,000 1,000 Severity 70,000 Frequency Density Frequency 60,000 50,000 40, ,000 20, , ,000 4,000 6,000 8,000 10,000 12,000 14,000 Range Range - Gamma consistent with severity modeling, or even Inverse Gaussian - Poisson consistent with frequency modeling No single trial/outcome - Trial is measured in terms of time - Actual policy length varies tremendously because of changes marital status new car moved 10

11 Solution? EMBLEM In 1996, EMB designed EMBLEM to provide access to GLM for statisticians and non-statisticians pricing personal and commercial insurance EMBLEM revolutionized the use of GLM s, enabling analysis that was previously either impossible or too time-consuming to be worth attempting EMBLEM is now used by over 100 insurance companies globally: - 18 of the top 20 personal auto writers in the UK - 50 companies in the US including 8 of the top 10 personal auto writers Fastest GLM tool with the capability to model millions of observations in seconds with a host of diagnostic tools: - Graphical, practical, statistical, automated. - Stand-alone software package that can be integrated with a variety of external software including SAS. - Microsoft Visual Basic for Applications provides ultimate flexibility. 11

12 EMBLEM GLM characteristics work to our advantage - Exponential family does an excellent job of describing the underlying components of insurance losses - Output of the model is in the form of Beta parameters which can easily be converted to rate relativities - EMBLEM is not automated User has complete control over the model structure Complete diagnostic tools to assist the modeler with decisions 12

13 Current Status in Insurance Marketplace In terms of estimating the cost of insurance: - UK has embraced predictive modeling Experienced with its techniques Knowledgeable with the factors that tend to be predictive - US is learning about predictive modeling Saturation with big players in personal lines marketplace Companies not using predictive modeling techniques are being adversely selected against Now expanding dimensionality of databases Still fairly new concept in commercial lines marketplace Big players are using techniques but historical rating structures are hindering the rapid expansion 13

14 Current Status in Insurance Marketplace Result? - UK is expanding into secondary applications Retention modeling Conversion modeling Price optimization Claim fraud detection - Because Predictive Modeling has been around for some time in the UK, the datasets are getting larger in terms of the number of predictors to evaluate - Experienced US companies are beginning to evaluate the secondary applications - Marketing is used in a manner similar to other industries 14

15 Cart.lnk CART How does CART fit into this? - As we transition into the secondary applications we move from modeling a continuous function to a binary function Tree-based techniques can add value to the analysis Retention and Conversion modeling - Accept/Reject target variable - Desirable smooth surface - Price optimization integrates these with premium models Marketing and Fraud detection - Classic tree applications 15

16 Cart.lnk CART EMBLEM Using CART and EMBLEM - Goal is to play off of the strengths of each tool CART Strengths - Automatic separation of relevant from irrelevant predictors - Easily rank-orders variable importance - Automatic interaction detection (requires additional work) - Captures multiple structures within a dataset rather than a single dominant structure - Can handle missing values and is impervious to outliers 16

17 Cart.lnk CART EMBLEM EMBLEM Strengths - User has control over the model structure - Ease of communication/conceptualization effects of each explanatory variable is transparent - Provides predicted response values for new data points 17

18 Cart.lnk Factor selection CART Interaction detection Model validation EMBLEM Model structure Incorporating time/seasonality trend effects Implementation of results 18

19 Speakers Note Both CART and EMBLEM are excellent tools both of which produce consistent results in similar situations - This is not an exercise of seeing which is better The purpose of this discussion is to show how efficiencies can be gained in the modeling process - As datasets get larger in terms of the number of predictors time becomes a crucial element 19

20 Case Study #1 US Dataset Retention modeling assignment - 97,227 observations each observation represents one trial/outcome split 50/50 between training/test datasets - 11 predictors grand total number of levels:

21 Case Study #1 Modeling Process - Started with Forward Entry Regression Automated process Used Chi-Squared statistic for testing significance Took about 30 minutes to run - Significant factors (8) Rating Area Vehicle Category Age NCD Driver Restriction Vehicle Age Change Over Last Year s Premium Market Competitiveness 21

22 Forward Entry Regression Build a Model with no factors and add based on prespecified criteria regarding improvement in model fit: Model Variables Deviance Degrees of Freedom Chi Squared Compare to Base Base Mean 12, ,596 1 Mean + Gender 12, , % 2 Mean + Policyholder Age 12, , % 3 Mean + Rating Area 12, , % 4 Mean + Vehicle Age 9, , %.. 17 Mean + MTA Indicator 12, , % 18 Mean + Time 12, , % Add the factor that performed the best on the Chi Square test. (Policyholder Age) Iterate process with the new base model until no further factors indicated removal 22

23 Case Study #1 Compared results with CART/TreeNet - Significant factors were essentially the same - Model predictiveness was the same (ROC = 0.7) Interactions - no significant interactions were found by EMBLEM or CART Test Dataset - ROC =

24 Case Study #2 UK Dataset Retention modeling assignment - 198,386 observations each observation represented one trial/outcome split 50/50 between training/test datasets predictors grand total number of levels: approx 3,752 24

25 Case Study #2 Forward Entry Regression - Found 57 predictors to be significant - Took a weekend to run Comparison to CART/TreeNet - Found 24 significant predictors - Top 15 based on variable importance were also found by EMBLEM - Correlations with the rest of the predictors Through the modeling process we reduced the number of predictors to 26 25

26 Case Study #2 Interactions - We relied on indications from CART/TreeNet - 6 interactions were identified and included in the model EMBLEM Results - Training ROC = Test ROC =.85 26

27 Other Expected Synergies Variable importance Segmentation Super-Profiling 27

28 Segmentation CART excels at identifying different segments in data CART may also help determine where to segment data Segmentation is a useful alternative to fitting many interactions Example: in a automobile insurance renewal problem, a CART analysis showed several occurrences of a split between those policyholders with just one years duration and those with a greater duration. This suggests segmenting the data into two parts: Policies renewing with one year duration Policies renewing with more than one year 28

29 Super-Profiling After a GLM model is constructed use CART to model the residuals to see if any pattern exists - If a pattern is discovered, go back to the model structure and incorporate the findings - test to see if model structure was inadvertently over-simplified 29

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