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A Comparison of Generalised Linear Modelling with Machine Learning Approaches for Predicting Loss Cost in Motor Insurance

Author

Listed:
  • Alinta Ann Wilson

    (School of Computing, Birmingham City University, Birmingham B4 7RQ, UK)

  • Antonio Nehme

    (School of Computing, Birmingham City University, Birmingham B4 7RQ, UK
    These authors contributed equally to this work.)

  • Alisha Dhyani

    (National Farmers Union Mutual Insurance Society, Tiddington, Stratford-upon-Avon CV37 7BJ, UK
    These authors contributed equally to this work.)

  • Khaled Mahbub

    (School of Computing, Birmingham City University, Birmingham B4 7RQ, UK)

Abstract

This study explores the insurance pricing domain in the motor insurance industry, focusing on the creation of “technical models” which are essentially obtained after combining the frequency model (the expected number of claims per unit of exposure) and the severity model (the expected amount per claim). Technical models are designed to predict the loss costs (the product of frequency and severity, i.e., the expected claim amount per unit of exposure) and this is a main factor that is taken into account for pricing insurance policies. Other factors for pricing include the company expenses, investments, reinsurance, underwriting, and other regulatory restrictions. Different machine learning methodologies, including the Generalised Linear Model (GLM), Gradient Boosting Machine (GBM), Artificial Neural Networks (ANN), and a unique hybrid model that combines GLM and ANN, were explored for creating the technical models. This study was conducted on the French Motor Third Party Liability datasets, “freMTPL2freq” and “freMTPL2sev” included in the R package CASdatasets. After building the aforementioned models, they were evaluated and it was observed that the hybrid model which combines GLM and ANN outperformed all other models. ANN also demonstrated better predictions closely aligning with the performance of the hybrid model. The better performance of neural network models points to the need for actuarial science and the insurance industry to look beyond traditional modelling methodologies like GLM.

Suggested Citation

  • Alinta Ann Wilson & Antonio Nehme & Alisha Dhyani & Khaled Mahbub, 2024. "A Comparison of Generalised Linear Modelling with Machine Learning Approaches for Predicting Loss Cost in Motor Insurance," Risks, MDPI, vol. 12(4), pages 1-29, March.
    • Handle: RePEc:gam:jrisks:v:12:y:2024:i:4:p:62-:d:1368081
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    References listed on IDEAS

    as
    1. Shengkun Xie & Kun Shi, 2023. "Generalised Additive Modelling of Auto Insurance Data with Territory Design: A Rate Regulation Perspective," Mathematics, MDPI, vol. 11(2), pages 1-24, January.
    2. Katrien Antonio & Emiliano Valdez, 2012. "Statistical concepts of a priori and a posteriori risk classification in insurance," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 96(2), pages 187-224, June.
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