2006 CAS RATEMAKING SEMINAR CONSIDERATIONS FOR SMALL BUSINESSOWNERS POLICIES (COM-3) Beth...

2006 CAS RATEMAKING SEMINAR

CONSIDERATIONS FOR SMALL BUSINESSOWNERS POLICIES

(COM-3)Beth Fitzgerald, FCAS, MAAA

Agenda

•Definition of Risks

•Market Needs

•Use of Statistical Modeling

•Scoring Model Development

•Amount of Insurance Relativity Factors

Underwriting Small Commercial Risks

Eligible for Businessowners• Size– Area– Gross sales

• Type of risk– Office, apartments, retail, service– Contractors, restaurants, motels, self-storage

facilities– Light manufacturing

•Rating– Class-rated– Low average premium

Growth in Small Businesses

18,000,000

19,000,000

20,000,000

21,000,000

22,000,000

23,000,000

24,000,000

25,000,000

1992 1997 2000 2003

Establishmentswith less than 10Employees

Source: Office of Advocacy, U.S. Small Business Administration

Market Needs

•Efficient use of technology to allow for faster, more consistent underwriting decisions

•Add intelligence to the policywriting process

•Low-cost solution due to low premium size

What Makes Statistical Modeling Possible?

•Advanced computer capabilities

•Advanced statistical data mining tools

Uses of Statistical Modeling

•Scoring of small commerical risks– Improve loss predictability of risks– Increase accuracy of pricing decisions– Cost effective, consistent underwriting

• Improve manual rating of risks

Development of Scoring Models

•Analyze historical policy and loss data

•Link policy and loss data with external data:– Business financial data– Weather – Demographics

•Use statistical data mining software and techniques

Modeling Process

BusinessKnowledge

Data Linking

Data Cleansing

Analyze Variables

Determine Predictive Variables

Evaluation

Data Gathering

Modeling

Statistical Modeling Techniques

Balance good fit with explanatory power

•Generalized Linear Models

•Classification Trees

•Regression Trees

•Multivariate Adaptive Regression Splines

•Neural Networks

Benefits of Scoring Model

•Fast, cost-effective tool to help you determine which risks to insure

•More accurate pricing decisions

•Reduce underwriting expense through automated scoring process efficiencies

•Expand your markets

Risks of Not Scoring

•Lost market share

•Greater risk of adverse selection

Use of Statistical Modeling in Manual Rating

• Improve rating relativities of current rating factors

•Add new rating factor to manual using a multi-variate statistical model

Amount of Insurance Relativities

• Amount of Insurance identified as important variable in BOP Scoring analysis

• Partially handled by insurers

• Decision to include as variable in manual and not in scoring model

Property BuildingsOne Dimensional

0

0.5

1

1.5

2

2.5

Amount of Insurance in 000's

Exp

eri

en

ce R

ati

o

Current Rating for BOPProperty

•Base loss costs by state/territory for buildings & personal property

•Multi-state Relativities– Rate number– Sprinkler– Protection– Construction

Current Rating for BOPLiability

•Base loss costs by state/territory for occupants & lessors– Occupants vary by AOI, Payroll or Sales

exposure base

•Multi-state rating relativities– Class group

Multivariate Analysis for Amount of Insurance Relativities

•Variables used for Property– Rate number– Sprinkler– Protection– Construction

•Variables used for Liability– Class group

BOP Implementation of AOI Relativities

• Incorporation into manual – Definition of base amount of insurance– Building - vary by state/region

•Timeline– 12 month lead time – Interaction with other possible changes– Filing late 2006