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This data set contains information on 22036 settled personal injury insurance claims in Australia. These claims arose from accidents occurring from July 1989 through to January 1999. Claims settled with zero payment are not included.

Usage

data(ausautoBI8999)

Format

ausautoBI8999 is a data frame of 8 columns and 1,340 rows:

AccDate, ReportDate, FinDate

The accident date, the reporting date, the finalization date, note that the day is always set to the first day of the month.

AccMth, ReportMth, FinMth

The accident month, the reporting month, the finalization month: 1 = July 1989, ..., 120 = June 1999).

OpTime

The operational time.

InjType1, InjType2, InjType3, InjType4, InjType5

The injury code for the people injured (up to five).

InjNb

Number of injured people.

Legal

A character string for: Has the policyholder a legal representation?

AggClaim

Aggregate settled amount of claims.

Source

Formerly on a website dedicated to P. De Jong and G.Z. Heller (2008).

References

P. De Jong and G.Z. Heller (2008), Generalized linear models for insurance data, Cambridge University Press, doi:10.1017/CBO9780511755408 .

Examples


# (1) load of data
#
data(ausautoBI8999)
dim(ausautoBI8999)
#> [1] 22036    15
head(ausautoBI8999)
#>      AccDate ReportDate    FinDate AccMth ReportMth FinMth OpTime InjType1
#> 1 1993-08-01 1993-09-01 1993-10-01     50        51     52    0.1     <NA>
#> 2 1993-12-01 1994-01-01 1994-02-01     54        55     56    0.1     <NA>
#> 3 1994-01-01 1994-01-01 1994-02-01     55        55     56    0.1     <NA>
#> 4 1994-04-01 1994-04-01 1994-05-01     58        58     59    0.1     <NA>
#> 5 1994-08-01 1994-09-01 1994-09-01     62        63     63    0.1     <NA>
#> 6 1994-12-01 1995-01-01 1995-01-01     66        67     67    0.1     <NA>
#>   InjType2 InjType3 InjType4 InjType5 InjNb Legal AggClaim
#> 1     <NA>     <NA>     <NA>     <NA>     0    No    87.75
#> 2     <NA>     <NA>     <NA>     <NA>     0    No   353.62
#> 3     <NA>     <NA>     <NA>     <NA>     0    No   688.83
#> 4     <NA>     <NA>     <NA>     <NA>     0    No   172.80
#> 5     <NA>     <NA>     <NA>     <NA>     0    No    43.29
#> 6     <NA>     <NA>     <NA>     <NA>     0    No  2915.43