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Modelação de sinistros de custo elevado no ramo multirriscos habitação
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Resumo(s)
The insurance market is highly competitive, requiring insurers to continuously evolve their risk rating methodologies to ensure a balance between profitability and customer appeal. Traditional pricing approaches, often based on general averages, can lead to distortions in cost allocation, ultimately affecting the competitiveness of companies’. In this context, predictive models, such as logistic regression, as appliedin this study, play a strategic role in enhancing the accuracy of risk estimation, enabling fairer differentiation between contracts and a stronger competitive position in the market. This study aims to develop two predictive models based on the application of logistic regression, with the goal of estimating the probability of the occurrence of large claimsinnon-life insurance policies (Household). Two distinct models were developed, each focused on a specific coverage: water damage, with emphasis on the building itself, and electrical risks, with emphasis on the contents. Currently, high-cost claims are distributed uniformly across all contracts, regardless of the actual likelihood of occurrence, resulting in a less equitable cost distribution. The proposed model seeks to identify customer segments with a higher propensity for generating large claims, thereby enabling a more equitable and risk-adjusted pricing strategy for each contract. To achieve this, a response variable was defined to represent the proportion of high-cost claims, weighted by the total number of claims, ensuring a fairer and more efficient approach. The data used in this study were provided by an insurance company and processed to ensure confidentiality. The dataset comprises information from five complete financial years, spanning from 2019 to 2023, for the aforementioned coverages. The findings of this study aim to contribute to the refinement of technical pricing criteria, promoting a fairer cost allocation and a more accurate risk assessment
Descrição
Trabalho de Projeto de Mestrado, Matemática Aplicada à Economia e Gestão, 2025, Universidade de Lisboa, Faculdade de Ciências
Palavras-chave
Logistic Regression Large Claims Household Technical Pricing