This document discusses how EMB America and Salford Systems can leverage their combined strengths in predictive modeling for the insurance industry. EMB specializes in insurance predictive modeling applications using their EMBLEM tool, while Salford Systems provides tree-based modeling techniques. The document outlines case studies where they have used both EMBLEM and CART (Classification and Regression Trees) models to identify important factors, interactions, and segments in large insurance datasets. Combining the approaches helped reduce modeling time and improve predictive performance for applications like customer retention modeling.

1. EMB America + Salford Systems
Getting the best of Two Worlds

2.  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

3.  Global network of p&c insurance consultants
servicing clients throughout the world
 (insert globe)

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

5.  EMB’s suite of software products cover all
aspects of personal and commercial lines of
insurance
◦ EMBLEM
◦ Rate Assessor
◦ Classifier
◦ Igloo Professional
◦ ExtrEMB
◦ ResQ Professional
◦ PrisEMB
◦ RePro

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

7.  Primary application:
◦ Estimating the cost of the product they sell (insurance) two steps:
 Reserving= estimating the cost of outstanding insurance claims
 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

8.  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

9.  Highly regulated marketplace
◦ Restrictions
 Predictors can and cannot use
 Credit scores
 Rules on values for the predictors
 Ages 65+ relativities cannot be >110% of ages 40-60
 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

10.  Response variable is continuous/discrete function
 (insert graph)
◦ Gamma consistent with severity modeling, or even Inverse
Gaussian
 (insert graph)
◦ 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

11.  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

12.  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

13.  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

14.  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

15.  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

16.  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

17.  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

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

19.  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

20.  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:147

21.  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

22.  Build a model with no factors and add based
on prespecified criteria regarding
improvement in model fit:
 (insert table)
 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

23.  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=0.7

24.  Retention modeling assignment
◦ 198,386 observations
 Each observation represented one trial/outcome
 Split 50/50 between training/test datasets
◦ 135 predictors
 Grand total number of levels: approx 3,752

25.  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

26.  Interactions
◦ We relied on indications from CART/ TreeNet
◦ 6 interactions were identified and included in the
model
 EMBLEM Results
◦ Training ROC= .862
◦ Test ROC= .85

27.  Variable importance
 Segmentation
 Super-Profiling

28.  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

29.  After a GLM model is constructed use CART
to model the residuals to see if any patterns
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