# Synthetic Dataset Generation of Driver Telematics

arXiv: https://arxiv.org/abs/2102.00252 · HTML: https://mlsanity.com/paper/2102.00252/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 56 references that are in the map.
- First posted on arXiv 30 Jan 2021.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [Gradient-based Hyperparameter Optimization through Reversible Learning](https://mlsanity.com/paper/1502.03492/) (2015)
- [Forward and Reverse Gradient-Based Hyperparameter Optimization](https://mlsanity.com/paper/1703.01785/) (2017)
- [Why are Adaptive Methods Good for Attention Models?](https://mlsanity.com/paper/1912.03194/) (2019)
- [Convolutional Recurrent Neural Networks for Small-Footprint Keyword Spotting](https://mlsanity.com/paper/1703.05390/) (2017)
- [Ask the GRU: Multi-Task Learning for Deep Text Recommendations](https://mlsanity.com/paper/1609.02116/) (2016)
- [Chemical-protein relation extraction with ensembles of SVM, CNN, and RNN models](https://mlsanity.com/paper/1802.01255/) (2018)
- [Hyperparameters Optimization in Deep Convolutional Neural Network / Bayesian Approach with Gaussian Process Prior](https://mlsanity.com/paper/1712.07233/) (2017)

## Built on by (most cited first, 8 of 8)

- [Data Augmentation for Imbalanced Regression](https://mlsanity.com/paper/2302.09288/) (2023)
- [Enhanced Gradient Boosting for Zero-Inflated Insurance Claims and Comparative Analysis of CatBoost, XGBoost, and LightGBM](https://mlsanity.com/paper/2307.07771/) (2023)
- [Boarding for ISS: Imbalanced Self-Supervised: Discovery of a Scaled Autoencoder for Mixed Tabular Datasets](https://mlsanity.com/paper/2403.15790/) (2024)
- [An engine to simulate insurance fraud network data](https://mlsanity.com/paper/2308.11659/) (2023)
- [Zero-Inflated Tweedie Boosted Trees with CatBoost for Insurance Loss Analytics](https://mlsanity.com/paper/2406.16206/) (2024)
- [Nightly Automobile Claims Prediction from Telematics-Derived Features: A Multilevel Approach](https://mlsanity.com/paper/2205.04616/) (2022)
- [Telematics Combined Actuarial Neural Networks for Cross-Sectional and Longitudinal Claim Count Data](https://mlsanity.com/paper/2308.01729/) (2023)
- [Synthetic data for ratemaking: imputation-based methods vs adversarial networks and autoencoders](https://mlsanity.com/paper/2509.02171/) (2025)

## Abstract (arXiv)

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 textttSMOTE 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.