Machine learning approaches for auto insurance big data - Mohamed Hanafy; Ruixing Ming (Risks, 2021)
The growing trend in the number and severity of auto insurance claims creates a needfor new methods to efficiently handle these claims. Machine learning (ML) is one of the methodsthat solves this problem. As car insurers aim to improve their customer service, these companieshave started adopting and applying ML to enhance the interpretation and comprehension of theirdata for efficiency, thus improving their customer service through a better understanding of theirneeds. This study considers how automotive insurance providers incorporate machinery learning intheir company, and explores how ML models can apply to insurance big data. We utilize various MLmethods, such as logistic regression, XGBoost, random forest, decision trees, näive 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 valuesof 0.8677, 0.7117, and 0.840, respectively.
Keywords: 330; a confusion matrix; big data; classification analysis; insurance; machine learning