Abstract
The growing trend in the number and severity of auto insurance claims creates a need for new methods to efficiently handle these claims. Machine learning (ML) is one of the methods that solves this problem. As car insurers aim to improve their customer service, these companies have started adopting and applying ML to enhance the interpretation and comprehension of their data for efficiency, thus improving their customer service through a better understanding of their needs. This study considers how automotive insurance providers incorporate machinery learning in their company, and explores how ML models can apply to insurance big data. We utilize various ML methods, such as logistic regression, XGBoost, random forest, decision trees, naïve Bayes, and K-NN, to predict claim occurrence. Furthermore, we evaluate and compare these models’ performances. The results showed that RF is better than other methods with the accuracy, kappa, and AUC values of 0.8677, 0.7117, and 0.840, respectively.
Highlights
The insurance industry’s current challenges are its transformation into a new level of digital applications and the use of machine learning (ML) techniques
We investigate more powerful ML techniques to make an accurate prediction for claims occurrence by analyzing the big dataset given by Porto Seguro, a large automotive company based in Brazil3, and we apply the ML methods in the dataset, such as logistic regression, XGBoost, random forest, decision trees, naïve Bayes, and K-NN
There is a need for an effective approach and a more reliable ML model to assess the danger that the driver poses to the insurance provider and the probability of filing a claim in the coming year, a model that can read and interpret vast databases containing thousands of consumer details provided by the Porto Seguro insurance company
Summary
The insurance industry’s current challenges are its transformation into a new level of digital applications and the use of machine learning (ML) techniques. Insurance claims occur when the policyholder (the customer) creates a formal request to an insurer for coverage or compensation for an accident. The insurance company must validate this request and decide whether to issue payment to the policyholder. There is a need for an effective approach and a more reliable ML model to assess the danger that the driver poses to the insurance provider and the probability of filing a claim in the coming year, a model that can read and interpret vast databases containing thousands of consumer details provided by the Porto Seguro insurance company. Porto Seguro is one of the biggest car and homeowner insurance firms in Brazil.
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