New Machine Learning Study Findings Recently Were Reported by a Researcher at Birmingham City University (A Comparison of Generalised Linear Modelling with Machine Learning Approaches for Predicting Loss Cost in Motor Insurance): Machine Learning

2024 APR 17 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- A new study on artificial intelligence is now available. According to news originating from Birmingham, United Kingdom, by NewsRx correspondents, research stated, “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).”

The news editors obtained a quote from the research from Birmingham City University: “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.”

According to the news editors, the research concluded: “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.”

For more information on this research see: A Comparison of Generalised Linear Modelling with Machine Learning Approaches for Predicting Loss Cost in Motor Insurance. Risks, 2024,12(4). (Risks - http://www.mdpi.com/journal/risks). The publisher for Risks is MDPI AG.

A free version of this journal article is available at https://doi.org/10.3390/risks12040062.

Our news journalists report that additional information may be obtained by contacting Alinta Ann Wilson, School of Computing, Birmingham City University, Birmingham B4 7RQ, United Kingdom. Additional authors for this research include Antonio Nehme, Alisha Dhyani, Khaled Mahbub.

ORCID is an identifier for authors and includes bibliographic information. The following is ORCID information for the authors of this research: Antonio Nehme (http://orcid.org/0000-0002-2101-0713), Khaled Mahbub (http://orcid.org/0000-0002-2112-2061).

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