integrating the broad range applications of predictive modeling in a competitive market environment...

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

- 2 -- 2 -Copyright © 2008 Deloitte Development LLC. All rights reserved. Confidential and Proprietary - Do not Copy to Distribute.

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

Three Major Types of Predictive Modeling in P&C Industry


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


– 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

Data Issues and Challenges for Pricing


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.


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


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 pre-

qualified 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


Predictive Modeling for MarketingA

Chart 1Conversion Rate by Price Differentiation

from Major Competitors

0

0.1

0.2

0.3

0.4

0.5

0.6

Price Differentiation from Competitors

Co

nvers

ion

Rate Conversion Rate


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


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.


Pricing Optimization Approach: Renewal Business

Pricing ModelTier 1 Model

Underwriting ModelTier 2 ModelCompetitor Information

Demand Model - Retention Model

(Retention Indicator= Premium Change at Renewal

+ Premium Comparison w/Competitors+ Other Policy Variables)

Tier 1 and Tier 2 Models

(Best Loss Cost Estimation by

Policy Segment)

Projected Overall Loss Ratio

Optimization Constraints

Pricing Optimization Tier 3 Model

Optimization Objective

Price Optimization Flowchart – Renew Business


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.


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


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.

integrating the broad range applications of predictive modeling in a competitive market environment...

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