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

Author:

Wilson Alinta Ann1,Nehme Antonio1ORCID,Dhyani Alisha2,Mahbub Khaled1ORCID

Affiliation:

1. School of Computing, Birmingham City University, Birmingham B4 7RQ, UK

2. National Farmers Union Mutual Insurance Society, Tiddington, Stratford-upon-Avon CV37 7BJ, 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.

Publisher

MDPI AG

Reference59 articles.

1. Utilizing grid search cross-validation with adaptive boosting for augmenting performance of machine learning models;Adnan;PeerJ. Computer Science,2022

2. A practitioner’s guide to generalized linear models;Anderson;Casualty Actuarial Society Discussion Paper Program,2004

3. Statistical Concepts of a Priori and a Posteriori Risk Classification in Insurance;Antonio;AStA Advances in Statistical Analysis,2012

4. Ardabili, Sina, Mosavi, Amir, and Várkonyi-Kóczy, Annamária R. (2019). International Conference on Global Research and Education, Springer International Publishing.

5. Using Artificial Neural Network Modelling in Forecasting Revenue: Case Study in National Insurance Company/Iraq;Bahia;International Journal of Intelligence Science,2013

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