Book contents
- Frontmatter
- Contents
- Preface
- 1 Regression and the Normal Distribution
- Part I Linear Regression
- Part II Topics in Time Series
- Part III Topics in Nonlinear Regression
- Part IV Actuarial Applications
- 16 Frequency-Severity Models
- 17 Fat-Tailed Regression Models
- 18 Credibility and Bonus-Malus
- 19 Claims Triangles
- 20 Report Writing: Communicating Data Analysis Results
- 21 Designing Effective Graphs
- Brief Answers to Selected Exercises
- Appendix 1 Basic Statistical Inference
- Appendix 2 Matrix Algebra
- Appendix 3 Probability Tables
- Index
18 - Credibility and Bonus-Malus
Published online by Cambridge University Press: 05 June 2012
- Frontmatter
- Contents
- Preface
- 1 Regression and the Normal Distribution
- Part I Linear Regression
- Part II Topics in Time Series
- Part III Topics in Nonlinear Regression
- Part IV Actuarial Applications
- 16 Frequency-Severity Models
- 17 Fat-Tailed Regression Models
- 18 Credibility and Bonus-Malus
- 19 Claims Triangles
- 20 Report Writing: Communicating Data Analysis Results
- 21 Designing Effective Graphs
- Brief Answers to Selected Exercises
- Appendix 1 Basic Statistical Inference
- Appendix 2 Matrix Algebra
- Appendix 3 Probability Tables
- Index
Summary
Chapter Preview. This chapter introduces regression applications of pricing in credibility and bonus-malus experience rating systems. Experience rating systems are formal methods for including claims experience into renewal premiums of short-term contracts, such automobile, health, and workers' compensation. This chapter provides brief introductions to credibility and bonus-malus, emphasizing their relationship with regression methods.
Risk Classification and Experience Rating
Risk classification is a key ingredient of insurance pricing. Insurers sell coverage at prices that are sufficient to cover anticipated claims, administrative expenses, and an expected profit to compensate for the cost of capital necessary to support the sale of the coverage. In many countries and lines of business, the insurance market is mature and highly competitive. This strong competition induces insurers to classify risks they underwrite to receive fair premiums for the risk undertaken. This classification is based on known characteristics of the insured, the person, or firm seeking the insurance coverage.
For example, suppose that you are working for a company that insures small businesses for time lost because of employees injured on the job. Consider pricing this insurance product for two businesses that are identical with respect to number of employees, location, age and sex distribution, and so forth, except that one company is a management consulting firm and the other is a construction firm.
- Type
- Chapter
- Information
- Regression Modeling with Actuarial and Financial Applications , pp. 452 - 466Publisher: Cambridge University PressPrint publication year: 2009