Automobile Insurance Claims

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Description

Claims experience from a large midwestern (US) property and casualty insurer for private passenger automobile insurance. The dependent variable is the amount paid on a closed claim, in (US) dollars (claims that were not closed by year end are handled separately). Insurers categorize policyholders according to a risk classification system. This insurer's risk classification system is based on automobile operator characteristics and vehicle characteristics, and these factors are summarized by the risk class categorical variable CLASS.

Usage

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Format

A data frame with 6773 observations on the following 5 variables.

STATE

Codes 01 to 17 used, with each code randomly assigned to an actual individual state, a factor with levels STATE 01 STATE 02 STATE 03 STATE 04 STATE 06 STATE 07 STATE 10 STATE 11 STATE 12 STATE 13 STATE 14 STATE 15 STATE 17

CLASS

Rating class of operator, based on age, gender, marital status, use of vehicle, a factor with levels C1 C11 C1A C1B C1C C2 C6 C7 C71 C72 C7A C7B C7C F1 F11 F6 F7 F71

GENDER

a factor with levels F M

AGE

Age of operator, a numeric vector

PAID

Amount paid to settle and close a claim, a numeric vector

Details

http://instruction.bus.wisc.edu/jfrees/jfreesbooks/Regression%20Modeling/BookWebDec2010/

DataDescriptions.pdf

Source

http://instruction.bus.wisc.edu/jfrees/jfreesbooks/Regression%20Modeling/BookWebDec2010/data.html

References

Frees E.W. (2010), Regression Modeling with Actuarial and Financial Applications, Cambridge University Press.

Examples

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data(AutoClaims)
## maybe str(AutoClaims) ; plot(AutoClaims) ...