Publicação
A refreshed vision of Non-Life Insurance Pricing - A Generalized Linear Model and Machine Learning Approach
| Resumo: | Insurance companies are faced with a constantly changing world, in a daily basis. There are a number of known risks that are quantifiable, alongside with many more that remain unknown or difficult to measure. It is only natural that the pricing of such risks must evolve side by side with the state-of-the-art technology that is available. In the late years there has been a rise in the number of studies that conclude on the better fitting of models based on machine learning technology, when it comes to estimate the prices charged by insurance companies in order to hedge the risks they endure. This project work aims to provide a refreshment of the pricing methods applied by a given insurance company operating in Portugal, by developing GLM-based frequency and severity modelling on a subset of the motor portfolio of the company, backed up by a machine learning model: Gradient Boosting. In doing so, there is an expectation of improvement of the accuracy of the model, providing better fitting estimates that could translate into a fairer tariff for both the insurance company and its clients. In fact, it was concluded that the Gradient Boosting approach outputted the lowest total deviance associated with the frequency model. In terms of severity, it was the GLM that produced to the lowest value. With the development of this project, there is now an open path in my company for the inclusion of machine learning methods on the development of insurance tariffs, being here proven that with little required input, this approach can in fact lead to very good results and thereby add value to the classic methodology. |
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| Autores principais: | Clemente, Carina de Miranda |
| Assunto: | Non-life Insurance GLM GBM Tariff Machine Learning Frequency Severity |
| Ano: | 2023 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | Insurance companies are faced with a constantly changing world, in a daily basis. There are a number of known risks that are quantifiable, alongside with many more that remain unknown or difficult to measure. It is only natural that the pricing of such risks must evolve side by side with the state-of-the-art technology that is available. In the late years there has been a rise in the number of studies that conclude on the better fitting of models based on machine learning technology, when it comes to estimate the prices charged by insurance companies in order to hedge the risks they endure. This project work aims to provide a refreshment of the pricing methods applied by a given insurance company operating in Portugal, by developing GLM-based frequency and severity modelling on a subset of the motor portfolio of the company, backed up by a machine learning model: Gradient Boosting. In doing so, there is an expectation of improvement of the accuracy of the model, providing better fitting estimates that could translate into a fairer tariff for both the insurance company and its clients. In fact, it was concluded that the Gradient Boosting approach outputted the lowest total deviance associated with the frequency model. In terms of severity, it was the GLM that produced to the lowest value. With the development of this project, there is now an open path in my company for the inclusion of machine learning methods on the development of insurance tariffs, being here proven that with little required input, this approach can in fact lead to very good results and thereby add value to the classic methodology. |
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Atividades financiadas
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