Automobile Bodily Injury Claims

Description

Data from the Insurance Research Council (IRC), a division of the American Institute for Chartered Property Casualty Underwriters and the Insurance Institute of America. The data, collected in 2002, contains information on demographic information about the claimant, attorney involvement and the economic loss (LOSS, in thousands), among other variables. We consider here a sample of n = 1; 340 losses from a single state. The full 2002 study contains over 70,000 closed claims based on data from thirty-two insurers. The IRC conducted similar studies in 1977, 1987, 1992 and 1997.

Usage

data(AutoBi)

Format

A data frame with 1340 observations on the following 8 variables.

CASENUM

Case number to identify the claim, a numeric vector

ATTORNEY

Whether the claimant is represented by an attorney (=1 if yes and =2 if no), a numeric vector

CLMSEX

Claimant's gender (=1 if male and =2 if female), a numeric vector

MARITAL

claimant's marital status (=1 if married, =2 if single, =3 if widowed, and =4 if divorced/separated), a numeric vector

CLMINSUR

Whether or not the driver of the claimant's vehicle was uninsured (=1 if yes, =2 if no, and =3 if not applicable), a numeric vector

SEATBELT

Whether or not the claimant was wearing a seatbelt/child restraint (=1 if yes, =2 if no, and =3 if not applicable), a numeric vector

CLMAGE

Claimant's age, a numeric vector

LOSS

The claimant's total economic loss (in thousands), 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

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

Automobile Insurance Claims

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

data(AutoClaims)

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

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

Automobile UK Collision Claims

Description

This data is due to Mildenhall (1999). Mildenhall (1999) considered 8,942 collision losses from private passenger United Kingdom (UK) automobile insurance policies. The data were derived from Nelder and McCullagh (1989, Section 8.4.1) but originated from Baxter et al. (1980). We consider here a sample of n = 32 of Mildenhall data for eight driver types (age groups) and four vehicle classes (vehicle use). The average severity is in pounds sterling adjusted for inflation.

Usage

data(AutoCollision)

Format

A data frame with 32 observations on the following 4 variables.

Age

Age of driver, a factor with levels A B C D E F G H

Vehicle_Use

Purpose of the vehicle use: DriveShort means drive to work but less than 10 miles, DriveLong means drive to work but more than 10 miles, a factor with levels Business DriveLong DriveShort Pleasure

Severity

Average amount of claims (in pounds sterling), a numeric vector

Claim_Count

Number of claims, 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.

Mildenhall S.J. (1999), A systematic relationship between minimum bias and generalized linear models, in: Proceedings of the Casualty Actuarial Society, 86, p. 393-487.

Examples

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

Claims Longitudinal

Description

This is a simulated data set, based on the car insurance data set used throughout the text. There are 40000 policies over 3 years, giving 120000 records.

Usage

data(ClaimsLong)

Format

A data frame with 120000 observations on the following 6 variables.

policyID

number of policy, a numeric vector

agecat

driver's age category: 1 (youngest), 2, 3, 4, 5, 6, a numeric vector

valuecat

vehicle value, in categories 1,...,6. (Category 1 has been recoded as 9.), a numeric vector

period

1, 2, 3, a numeric vector

numclaims

number of claims, a numeric vector

claim

a numeric vector

Details

The dataset "Longitudinal Claims"

Source

http://www.businessandeconomics.mq.edu.au/our_departments/Applied_Finance_and_Actuarial_Studies/

research/books/GLMsforInsuranceData/data_sets

References

De Jong P., Heller G.Z. (2008), Generalized linear models for insurance data, Cambridge University Press

Examples

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

Auto Industry

Description

The data represent industry aggregates for private passenger auto liability\/medical coverages from year 2004, in millions of dollars. They are based on insurance company annual statements, specifically, Schedule P, Part 3B. The elements of the triangle represent cumulative net payments, including defense and cost containment expenses.

Usage

data(IndustryAuto)

Format

A data frame with 55 observations on the following 3 variables.

Incurral.Year

The year in which a claim has been incurred, a numeric vector

Development.Year

The number of years from incurral to the time when the payment is made, a numeric vector

Claim

Cumulative net payments, including defense and cost containment expenses, 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.

Wacek M.G. (2007), A Test of Clinical Judgment vs. Statistical Prediction in Loss Reserving for Commercial Auto Liability, in: Casualty Actuarial Society Forum, p. 371-404.

Examples

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

Singapore Automobile Claims

Description

The data is from the General Insurance Association of Singapore, an organization consisting of general (property and casualty) insurers in Singapore (see the organization's website: www.gia.org.sg). From this database, several characteristics are available to explain automobile accident frequency. These characteristics include vehicle variables, such as type and age, as well as person level variables, such as age, gender and prior driving experience.

Usage

data(SingaporeAuto)

Format

A data frame with 7483 observations on the following 15 variables.

SexInsured

a factor with levels F M U

Female

a numeric vector

VehicleType

a factor with levels A G M P Q S T W Z

PC

a numeric vector

Clm_Count

a numeric vector

Exp_weights

a numeric vector

LNWEIGHT

a numeric vector

NCD

a numeric vector

AgeCat

a numeric vector

AutoAge0

a numeric vector

AutoAge1

a numeric vector

AutoAge2

a numeric vector

AutoAge

a numeric vector

VAgeCat

a numeric vector

VAgecat1

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., Valdez E.A. (2008), Hierarchical insurance claims modeling, ,,Journal of the American Statistical Association", 103(484), p. 1457-1469.

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

Examples

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

Third party insurance

Description

Third party insurance is a compulsory insurance for vehicle owners in Australia. It insures vehicle owners against injury caused to other drivers, passengers or pedestrians, as a result of an accident.

This data set records the number of third party claims in a twelve–month period between 1984-1986 in each of 176 geographical areas (local government areas) in New South Wales, Australia.

Usage

data(Thirdparty)

Format

A data frame with 176 observations on the following variable.

lga.sd.claims.accidents.ki.population.pop_density

a numeric vector

Details

The dataset "Third Party Claims"

Source

http://www.businessandeconomics.mq.edu.au/our_departments/Applied_Finance_and_Actuarial_Studies/

research/books/GLMsforInsuranceData/data_sets

References

De Jong P., Heller G.Z. (2008), Generalized linear models for insurance data, Cambridge University Press

Examples

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

Workers Compensation

Description

Standard example in worker's compensation insurance, examining losses due to permanent, partial disability claims. The data are from Klugman (1992), who considers Bayesian model representations, and are originally from the National Council on Compensation Insurance. We consider n=121 occupation, or risk, classes, over T=7 years. To protect the data source, further information on the occupation classes and years is not available. Source: Frees, E. W., Young, V. and Y. Luo (2001). Case studies using panel data models. North American Actuarial Journal, 4, No. 4, 24-42.

Usage

data(WorkersComp)

Format

A data frame with 847 observations on the following 4 variables.

CL

a numeric vector

YR

a numeric vector

PR

a numeric vector

LOSS

a numeric vector

Details

http://instruction.bus.wisc.edu/jfrees/jfreesbooks/Regression

DataDescriptions.pdf

Source

http://instruction.bus.wisc.edu/jfrees/jfreesbooks/Regression

References

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

Examples

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

data Car

Description

This data set is based on one-year vehicle insurance policies taken out in 2004 or 2005. There are 67856 policies, of which 4624 (6.8

Usage

data(dataCar)

Format

A data frame with 67856 observations on the following 11 variables.

veh_value

vehicle value, in $10,000s

exposure

0-1

clm

occurrence of claim (0 = no, 1 = yes)

numclaims

number of claims

claimcst0

claim amount (0 if no claim)

veh_body

vehicle body, coded as BUS CONVT COUPE HBACK HDTOP MCARA MIBUS PANVN RDSTR SEDAN STNWG TRUCK UTE

veh_age

1 (youngest), 2, 3, 4

gender

a factor with levels F M

area

a factor with levels A B C D E F

agecat

1 (youngest), 2, 3, 4, 5, 6

X_OBSTAT_

a factor with levels 01101 0 0 0

Details

dataset "Car"

Source

http://www.acst.mq.edu.au/GLMsforInsuranceData

References

De Jong P., Heller G.Z. (2008), Generalized linear models for insurance data, Cambridge University Press

Examples

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

Motorcycle Insurance

Description

The data for this case study comes from the former Swedish insurance company Wasa, and concerns partial casco insurance, for motorcycles this time. It contains aggregated data on all insurance policies and claims during 1994-1998; the reason for using this rather old data set is confidentiality; more recent data for ongoing business can not be disclosed.

Usage

data(dataOhlsson)

Format

A data frame with 64548 observations on the following 9 variables.

agarald

The owners age, between 0 and 99, a numeric vector

kon

The owners age, between 0 and 99, a factor with levels K M

zon

Geographic zone numbered from 1 to 7, in a standard classification of all Swedish parishes, a numeric vector

mcklass

MC class, a classification by the so called EV ratio, defined as (Engine power in kW x 100) / (Vehicle weight in kg + 75), rounded to the nearest lower integer. The 75 kg represent the average driver weight. The EV ratios are divided into seven classes, a numeric vector

fordald

Vehicle age, between 0 and 99, a numeric vector

bonuskl

Bonus class, taking values from 1 to 7. A new driver starts with bonus class 1; for each claim-free year the bonus class is increased by 1. After the first claim the bonus is decreased by 2; the driver can not return to class 7 with less than 6 consecutive claim free years, a numeric vector

duration

the number of policy years, a numeric vector

antskad

the number of claims, a numeric vector

skadkost

the claim cost, a numeric vector

Details

The dataset "mccase.txt"

Source

http://people.su.se/~esbj/GLMbook/case.html

References

Ohlsson E., Johansson B. (2010), Non-life insurance pricing with generalized linear models, Springer

Examples

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