June 2003 — Given the current state of the insurance landscape, carriers competing for customers, dealing with rising claim rates and high loss ratios, the need has never been greater for insurers to establish and maintain a complete view of their current and prospective policyholders.
A key part of this complete view is the specific claims risk that each customer poses to the company. Evaluating claims propensity gives insurance providers an objective way to adjust premium rates based on relevant, customer-specific information. Accurately predicting claims propensity throughout an organization can dramatically improve loss ratios and enables providers to charge lower premiums to low-risk customers.
To improve loss ratios through more effective pricing and marketing, insurance providers need a solution that gathers relevant claims data from every corner of the enterprise as well as third-party data sources to provide both claims propensity predictions and claim size predictions.
Table of Contents
- 1.1 Trends in Insurance and Rate Making
- 1.2 Introduction to Rate Making
- 1.3 Rate Making: Data Concepts, Terminology
- Data Mining for Rate Making
- 2.1 Introduction to Data Mining
- 2.2 Introduction to Enterprise Miner™
- 2.3 Rate Making Using Data Mining
- 2.4 Rate Making Using Traditional Statistics
- Finding a new Rate Structure
- Refining an existing Rate Structure
- An Introduction to Statistical Rate Making
- Code for generalized Linear Models estimating Accident Frequency
Enterprise Miner™ – SAS Institute’s Solution for Data Mining
Document in pdf format, about 560k
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