TITLE: Synthetic Dataset Generation of Driver Telematics 

ABSTRACT: This article describes techniques employed in the production of a synthetic
dataset of driver telematics emulated from a similar real insurance dataset.
The synthetic dataset generated has 100,000 policies that included observations
about driver's claims experience together with associated classical risk
variables and telematics-related variables. This work is aimed to produce a
resource that can be used to advance models to assess risks for usage-based
insurance. It follows a three-stage process using machine learning algorithms.
The first stage is simulating values for the number of claims as multiple
binary classifications applying feedforward neural networks. The second stage
is simulating values for aggregated amount of claims as regression using
feedforward neural networks, with number of claims included in the set of
feature variables. In the final stage, a synthetic portfolio of the space of
feature variables is generated applying an extended $\texttt{SMOTE}$ algorithm.
The resulting dataset is evaluated by comparing the synthetic and real datasets
when Poisson and gamma regression models are fitted to the respective data.
Other visualization and data summarization produce remarkable similar
statistics between the two datasets. We hope that researchers interested in
obtaining telematics datasets to calibrate models or learning algorithms will
find our work valuable.
This article describes techniques employed in the production of a synthetic
dataset of driver telematics emulated from a similar real insurance dataset.
The synthetic dataset generated has 100,000 policies that included observations
about driver's claims experience together with associated classical risk
variables and telematics-related variables. This work is aimed to produce a
resource that can be used to advance models to assess risks for usage-based
insurance. It follows a three-stage process using machine learning algorithms.
The first stage is simulating values for the number of claims as multiple
binary classifications applying feedforward neural networks. The second stage
is simulating values for aggregated amount of claims as regression using
feedforward neural networks, with number of claims included in the set of
feature variables. In the final stage, a synthetic portfolio of the space of
feature variables is generated applying an extended $\texttt{SMOTE}$ algorithm.
The resulting dataset is evaluated by comparing the synthetic and real datasets
when Poisson and gamma regression models are fitted to the respective data.
Other visualization and data summarization produce remarkable similar
statistics between the two datasets. We hope that researchers interested in
obtaining telematics datasets to calibrate models or learning algorithms will
find our work valuable.
Synthetic Dataset Generation of Driver Telematics
http://arxiv.org/abs/2102.00252v1