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Predicting Motor Insurance Claims Using Telematics Data—XGBoost versus Logistic Regression

Department of Econometrics, Riskcenter-IREA, Universitat de Barcelona, 08034 Barcelona, Spain
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Risks 2019, 7(2), 70; https://doi.org/10.3390/risks7020070
Received: 9 May 2019 / Revised: 7 June 2019 / Accepted: 12 June 2019 / Published: 20 June 2019
(This article belongs to the Special Issue Machine Learning in Insurance)
XGBoost is recognized as an algorithm with exceptional predictive capacity. Models for a binary response indicating the existence of accident claims versus no claims can be used to identify the determinants of traffic accidents. This study compared the relative performances of logistic regression and XGBoost approaches for predicting the existence of accident claims using telematics data. The dataset contained information from an insurance company about the individuals’ driving patterns—including total annual distance driven and percentage of total distance driven in urban areas. Our findings showed that logistic regression is a suitable model given its interpretability and good predictive capacity. XGBoost requires numerous model-tuning procedures to match the predictive performance of the logistic regression model and greater effort as regards to interpretation. View Full-Text
Keywords: dichotomous response; predictive model; tree boosting; GLM; machine learning dichotomous response; predictive model; tree boosting; GLM; machine learning
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Pesantez-Narvaez, J.; Guillen, M.; Alcañiz, M. Predicting Motor Insurance Claims Using Telematics Data—XGBoost versus Logistic Regression. Risks 2019, 7, 70.

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