French Motor Third-Part Liability datasets

In the two datasets freMTPLfreq, freMTPLsev, risk features are collected for 413,169 motor third-part liability policies (observed mostly on one year). In addition, we have claim numbers by policy as well as the corresponding claim amounts. freMTPLfreq contains the risk features and the claim number while freMTPLsev contains the claim amount and the corresponding policy ID. Some claim amounts of freMTPLsev are fixed claim amounts based on the French IRSA-IDA claim convention, see e.g.~https://www.index-assurance.fr/pratique/sinistre/convention-irsa.

In the two datasets freMTPL2freq, freMTPL2sev, risk features are collected for 677,991 motor third-part liability policies (observed mostly on one year). In addition, we have claim numbers by policy as well as the corresponding claim amounts. freMTPL2freq contains the risk features and the claim number while freMTPL2sev contains the claim amount and the corresponding policy ID. Some claim amounts of freMTPL2sev are fixed claim amounts based on the French IRSA-IDA claim convention, see e.g.~https://www.index-assurance.fr/pratique/sinistre/convention-irsa.

Usage

data(freMTPLfreq)
data(freMTPLsev)

data(freMTPL2freq)
data(freMTPL2sev)

Format

freMTPLfreq contains 10 columns:

PolicyID: The policy ID (used to link with the claims dataset).
ClaimNb: Number of claims during the exposure period.
Exposure: The period of exposure for a policy, in years.
Power: The power of the car (ordered categorical).
CarAge: The vehicle age, in years.
DriverAge: The driver age, in years (in France, people can drive a car at 18).
Brand: The car brand divided in the following groups: A- Renaut Nissan and Citroen, B- Volkswagen, Audi, Skoda and Seat, C- Opel, General Motors and Ford, D- Fiat, E- Mercedes Chrysler and BMW, F- Japanese (except Nissan) and Korean, G- other.
Gas: The car gas, Diesel or regular.
Region: The policy region in France (based on the 1970-2015 classification).
Density: The density of inhabitants (number of inhabitants per km2) in the city the driver of the car lives in.

freMTPLsev contains 2 columns:

PolicyID: The occurence date (used to link with the contract dataset).
ClaimAmount: The cost of the claim, seen as at a recent date.

freMTPL2freq contains 11 columns:

IDpol: The policy ID (used to link with the claims dataset).
ClaimNb: Number of claims during the exposure period.
Exposure: The period of exposure for a policy, in years.
VehPower: The power of the car (ordered values).
VehAge: The vehicle age, in years.
DrivAge: The driver age, in years (in France, people can drive a car at 18).
BonusMalus: Bonus/malus, between 50 and 350: <100 means bonus, >100 means malus in France.
VehBrand: The car brand (unknown categories).
VehGas: The car gas, Diesel or regular.
Area: The density value of the city community where the car driver lives in: from "A" for rural area to "F" for urban centre.
Density: The density of inhabitants (number of inhabitants per square-kilometer) of the city where the car driver lives in.
Region: The policy region in France (based on the 1970-2015 classification).

freMTPL2sev contains 2 columns:

IDpol: The occurence date (used to link with the contract dataset).
ClaimAmount: The cost of the claim, seen as at a recent date.

Source

Unknown private insurer.

References

M. Denuit, A. Charpentier, and J. Trufin (2021), Autocalibration and tweedie-dominance for insurance pricing with machine learning, Insurance: Mathematics and Economics, 101, 485–497, doi:10.1016/j.insmatheco.2021.09.001 .

T. Miljkovic and D. Fernández (2018). On Two Mixture-Based Clustering Approaches Used in Modeling an Insurance Portfolio, Risks, 6(2), 1–18, doi:10.3390/risks6020057 .

A. Noll, R. Salzmann, and M. V. Wuthrich (2020), Case study: French motor third-party liability claims, Innovation Practice eJournal, doi:10.2139/ssrn.3164764 .

N. Pocuca, P. Jevtic, P. D. McNicholas, and T. Miljkovic (2020), Modeling frequency and severity of claims with the zero-inflated generalized cluster-weighted models, Insurance: Mathematics and Economics, 94, 79–93, doi:10.1016/j.insmatheco.2020.06.004 .

Examples


# (1) load of data
#
data(freMTPLfreq)
dim(freMTPLfreq)
#> [1] 413169     10

data(freMTPLsev)
dim(freMTPLsev)
#> [1] 16181     2


# (2) check
#should be equal
sum(freMTPLsev$PolicyID %in% freMTPLfreq$PolicyID)
#> [1] 16181
sum(freMTPLfreq$ClaimNb)
#> [1] 16181



# (1) load of data
#
data(freMTPL2freq)
dim(freMTPL2freq)
#> [1] 677991     12

data(freMTPL2sev)
dim(freMTPL2sev)
#> [1] 26444     2