Machine Learning Approaches for Auto Insurance Big Data

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 that solves this problem. As car insurers aim improve their customer service, companies have started adopting applying ML enhance interpretation comprehension data efficiency, thus improving service through better understanding needs. This study considers how automotive providers incorporate machinery company, explores models can apply big data. We utilize various methods, such as logistic regression, XGBoost, random forest, decision trees, naïve Bayes, K-NN, predict claim occurrence. Furthermore, we evaluate compare models’ performances. results showed RF than other with accuracy, kappa, AUC values 0.8677, 0.7117, 0.840, respectively.