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

Jessica Pesantez-Narvaez, Montserrat Guillen and Manuela Alcañiz
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Jessica Pesantez-Narvaez: Department of Econometrics, Riskcenter-IREA, Universitat de Barcelona, 08034 Barcelona, Spain
Montserrat Guillen: Department of Econometrics, Riskcenter-IREA, Universitat de Barcelona, 08034 Barcelona, Spain
Manuela Alcañiz: Department of Econometrics, Riskcenter-IREA, Universitat de Barcelona, 08034 Barcelona, Spain

Risks, 2019, vol. 7, issue 2, 1-16

Abstract: 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.

Keywords: dichotomous response; predictive model; tree boosting; GLM; machine learning (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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