Integrating the Broad Range Applications
of Predictive Modeling in a Competitive
Market Environment
Jun Yan
Mo Mosud
Cheng-sheng Peter Wu
2008 CAS Spring Meeting
Three Major Types of Predictive Modeling in P&C Industry
Predictive modeling for pricing: most popular in actuarial
Predictive modeling for underwriting: common in commercial lines
Predictive modeling for marketing and sales: classic application for
predictive modeling
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Predictive Modeling for Pricing
 Built on coverage/exposure level
 Rating structure design
 Determining loss cost relativities by rating factors
 Typical approach: frequency/severity vs. pure premium
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Data Issues and Challenges for Pricing
– Missing information
– Miscoding
– Losses below deductible not recorded
– Losses above liability limit truncated
– Data for modeling severity could be very thin
– Inconsistency in exposure base from one coverage to another, from one class to another
– For commercial lines, information kept at bureau class code reporting level, not at
exposure level
– CAT loss adjustment
– Sparse data available for special lines and coverages
– Adjustment for complex rating factors:
• Territory for personal lines
• Vehicle Symbol for personal auto
• Class code for commercial lines
– Regulatory constraints
• Use of credit information
• Restrictions for variable selection, could be different by state
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Predictive Modeling for Underwriting
 Evaluation of risk quality related to rating plan. Differentiate profitability by policy
segments.
 Assisting underwriters or product managers in underwriting:
– Acceptance or rejection
– Renewal or cancellation
– Tier or company placement
– Credit or Debit
– Coverage limitations
– Payment plan selection
– Manual touch or automatic underwriting
 Underwriting model design:
– Policy level
– Loss ratio as the target variable, frequency/severity approach is not commonly used
– A wider selection of predictive variables
• Rating vs. non-rating variables
• Internal vs. external variables.
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Data Issue and Challenges for Underwriting
 Most of the data issues for pricing equally applicable to underwriting
 Availability for non-rating variables, for example, billing data.
 Actuarial adjustments for target loss ratio variable necessary:
– Premium on-leveling
– Loss development and trend
 “Policy level” variables rolled up from the coverage and exposure level
 Policy level underwriting models vs. account level underwriting models
 Implementation consideration
– Technology related
– Regulation related
– Business concerns
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Predictive Modeling for Marketing and Sales

Classic application of data mining and predictive modeling

For insurance, there are four types of models:
– New business qualification or targeting model: for example, mail solicitation for prequalified customers
– New business conversion model: conversion rate from quote to binding
– Renew business retention model: probability of an existing policy staying from current
term to next term
– Renew business conversion model: probability of conversion of a renewal policy to the
next term at underwriting cycle.
 Binary target for modeling: “success or failure”
 A piece of “critical information” for marketing and sales models, “Price Elasticity”:
– Premium comparison with major competitors, premium change at renewal
– Other variables may affect price elasticity, including brand name, account indicator, policy
age, etc
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Predictive Modeling for Marketing
A
Chart 1
Conversion Rate by Price Differentiation
from M ajor Competitors
0.6
Conversion Rate
0.5
Conversion Rate
0.4
0.3
0.2
0.1
60
%
50
%
40
%
30
%
20
%
10
%
0%
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0%
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0%
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0%
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0%
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0%
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0%
-7
0%
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0%
0
Price Differentiation from Com petitors
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Data Issues and Challenges for Marketing
 For new business applications:
– Quote files are not well stored, and the information on quote files are sparse:
• Name and address of an insured
• Basic and key rating information
• Agent information
• Competitiveness information including prior carrier’s name and price
– For new business marketing models, need to rely on external databases: data
quality and avaiability
 For renewal business applications, lack of information for cancelled
policies and competitors’ pricing data
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Premium Optimization by Integrating Three Applications
 Premium
optimization for P&C insurance is an approach to achieve an
optimal outcome for an insurance company by balancing profitability and
growth objective
 Premium
optimization is built on top of the 3 major types of predictive
modeling:
Underwriting Gain = Premium – Loss – Expenses
Expected Loss = Overall expected loss cost * rating plan factors * LR Relativity
Customer Marketing, Conversion, and Retention:
• New business marketing: campaign, solicitation and targeting
• New business conversion: price elasticity
• Renewal business retention: price elasticity
 Predictive
modeling will be subjective to internal and external constraints.
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Pricing Optimization Approach: Renewal Business
Price Optimization Flowchart – Renew Business
Competitor Information
Pricing Model
Tier 1 Model
Underwriting Model
Tier 2 Model
Projected Overall
Loss Ratio
Demand Model - Retention Model
Tier 1 and Tier 2
Models
(Best Loss Cost
Estimation by
Policy Segment)
(Retention Indicator
= Premium Change at Renewal
+ Premium Comparison w/Competitors
+ Other Policy Variables)
Optimization
Constraints
Optimization
Objective
Pricing
Optimization
Tier 3 Model
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Integrating 3 Types Predictive Models
 General Approach for Integration:
• Develop an “adequate” rating plan using the standard GLM approach:
The GLM rating plan would assume that the rate is adequate with regards to the rating
variables and the structure of the rating plan
• Develop a new business conversion model or renew business retention model
by studying the sensitivity of how insurance buyers react to price difference,
such as the price elasticity
• Adjust the GLM rating plan so that the parameters can be re-optimized based
on the conversion or retention model outcomes
Potentially many iteration and time consuming.
• Build underwriting models on top of the pricing and marketing models.
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Advantages for the Integration
 Underwriting and marketing models are flexible in dealing with the
dynamic external environment
 The subjective judgment by underwriters can be largely eliminated
 Optimize between premium growth and profitability
 “Fine tune” the pricing strategy:
– For example, adjust the rates for the most price sensitive segments, instead of
taking uniform, comprehensive rate adjustments
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Data Issues and Challenges for the Integration
 The data level is different between the 3 types of models
 Marketing applications are “forward-looking” based, while the pricing and
underwriting applications are based on “historical” information
Change in distribution for book mix
Change in distribution for premium size
Change in distribution channels or affinity programs
 Data is more sparsely available for the marketing application than for the
underwriting or pricing applications.
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