European lapse dataset from the direct channel — eudirectlapse • CASdatasets

The eudirectlapse dataset is based on one-year vehicle insurance renewal quotes for an unknown year and an unknown insurer. There are 23,060 policies.

Usage

data(eudirectlapse)

Format

eudirectlapse is a data frame of 19 columns and 23,060 rows:

lapse: A binary variable indicating the lapse of the customer.
polholder_age: The age of the policyholder.
polholder_BMCevol: The evolution of bonus/malus coefficient (BMC) of the policyholder: 3 categorical values ("down" when bonus increases, "stable" when coefficient does not change, "up" when malus increases.
polholder_diffdriver: The difference status between the policyholder and the driver.
polholder_gender: The gender of the policyholder.
polholder_job: The job of the policyholder: either "medical" or "normal".
policy_age: The age of the policy.
policy_caruse: The car usage.
policy_nbcontract: The number of policies given policyholder for this insurer.
prem_final: The final renewal premium value proposed to policyholder.
prem_freqperyear: The premium frequency per year.
prem_last: The premium paid by the policyholder for the last insurance coverage.
prem_market: A proxy of the market premium.
prem_pure: The technical premium value.
vehicl_age: The vehicle age.
vehicl_agepurchase: The vehicle age at purchase.
vehicl_garage: The garage type (categorical values).
vehicl_powerkw: The horsepower of the car (categorical values).
vehicl_region: The living region of policyholder (unknown category).

Source

Unknown non-life insurers from European Union.

Examples


# (1) load of data
#
data(eudirectlapse)
head(eudirectlapse)
#>   lapse polholder_age polholder_BMCevol polholder_diffdriver polholder_gender
#> 1     0            38            stable         only partner             Male
#> 2     1            35            stable                 same             Male
#> 3     1            29            stable                 same             Male
#> 4     0            33              down                 same           Female
#> 5     0            50            stable                 same             Male
#> 6     0            37            stable         only partner             Male
#>   polholder_job policy_age             policy_caruse policy_nbcontract
#> 1        normal          1 private or freelance work                 1
#> 2        normal          1 private or freelance work                 1
#> 3        normal          0 private or freelance work                 1
#> 4       medical          2 private or freelance work                 1
#> 5        normal          8                   unknown                 1
#> 6        normal          1 private or freelance work                 1
#>   prem_final prem_freqperyear prem_last prem_market prem_pure vehicl_age
#> 1     232.46       4 per year    232.47      221.56    243.59          9
#> 2     208.53       4 per year    208.54      247.56    208.54         15
#> 3     277.34       1 per year    277.35      293.32    277.35         14
#> 4     239.51       4 per year    244.40      310.91    219.95         17
#> 5     554.54       4 per year    554.55      365.46    519.50         16
#> 6     266.46       1 per year    266.46      341.88    266.46         13
#>   vehicl_agepurchase      vehicl_garage vehicl_powerkw vehicl_region
#> 1                  8     private garage         225 kW          Reg7
#> 2                  7     private garage         100 kW          Reg4
#> 3                  6 underground garage         100 kW          Reg7
#> 4                 10             street          75 kW          Reg5
#> 5                  8             street          75 kW         Reg14
#> 6                 10 underground garage         100 kW          Reg4