Improving Automobile Insurance Claims Frequency Prediction With Telematics Car Driving Data

Abstract

Novel navigation applications provide a driving behavior score for each finished trip to promote safe driving, which is mainly based on experts’ domain knowledge. In this paper, with automobile insurance claims data and associated telematics car driving data, we propose a supervised driving risk scoring neural network model. This one-dimensional convolutional neural network takes time series of individual car driving trips as input and returns a risk score in the unit range of (0,1). By incorporating credibility average risk score of each driver, the classical Poisson generalized linear model for automobile insurance claims frequency prediction can be improved significantly. Hence, compared with non-telematics-based insurers, telematics-based insurers can discover more heterogeneity in their portfolio and attract safer drivers with premiums discounts.

Non-life insurance: The state of the art of determining the superior method for pricing automobile insurance premiums using archival technique

“…The following machine learning techniques: logistic regression, decision tree, random forest, xBoost, and feed-forward network were investigated and used to predict claims severity and they were suggested to be superior techniques (Baran & Rola, 2022). A supervised driving risk scoring neural network (NN) model was also proposed and shown to predict accurate premiums with discounts (Meng et al, 2022). The combination of the Back Propagation Neural Network and credibility theory was investigated and suggested that they can conduct accurate claim amount estimation and pricing for vehicle insurance, which can effectively improve the current situation of the automotive insurance companies and encourage the development of the insurance industry (Yu et al, 2021).…”

CRFormer: Complementary Reliability Perspective Transformer for Automotive Components Reliability Prediction Based on Claim Data

“…Owing to the expandability of deep learning models into various fields, it was possible to confirm the effects of deep learning models in reliability prediction [7]. Recently, [14], [15] suggested the 1-D CNN and RNN-based time-series models for automobile reliability prediction. In particular, [14] proved that deep learning time-series models showed outstanding performance in the reliability prediction of automotive components compared to existing statistical, time-series, and machine learning methods.…”

“…Although ARIMA model can capture sequential patterns, it results in unsatisfactory prediction results when predicting long-period failures with short-term inputs [14]. Recently, [14], [15] applied deep learning models to predict automobile reliability. [15] and [14] used 1-D Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), respectively, to represent time-series claim data.…”

“…Recently, [14], [15] applied deep learning models to predict automobile reliability. [15] and [14] used 1-D Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), respectively, to represent time-series claim data. [14] showed that RNN outperformed the previous methods (i.e., parametric, time-series, and machine learning models) for remaining warranty period failure prediction with short-term inputs.…”

Telematics combined actuarial neural networks for cross-sectional and longitudinal claim count data

“…In Meng et al . (2022), the authors propose a supervised driving risk scoring convolutional neural network (CNN) model that uses telematics car driving data to improve automobile insurance claims frequency prediction. Blier-Wong et al .…”