Machine Learning Approaches for Auto Insurance Big Data

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.

Improving Imbalanced Data Classification in Auto Insurance by the Data Level Approaches

“…Evaluation methods are essential in comparing and selecting the best model because they are assessing the efficiency of classifiers [1].…”

“…Nonetheless, the performance of the prediction models for the small class will be substantially reduced. The following studies show a lack of using the resampling methods to solve the unbalanced data problem except for the study of [1] that only used the oversampling method.…”

“…In the study of machine learning approaches for auto insurance big data [1], they built eight classifiers to predict the occurrence of the claims using big insurance data, including XGBoost, J48, RF, C5.0, CART, K-NN, logistic regression, and naïve Bayes algorithms, and they handled the heavy imbalanced data using the over-sampler method. The RF model performed the best among the eight models.…”

“…A variety of variables influence automobile insurance pricing [1]. And these factors would affect the cost of a client's insurance policy.…”

A Machine Learning Approach for Micro-Credit Scoring

“…Similarly, numerous studies on the applicability of machine learning techniques have been implemented in other areas of finance due to their ability to recognize a set of financial data trends; see, e.g., Carbo-Valverde et al (2020); Hanafy and Ming (2021). Related studies indicated that a combination of machine learning methods could offer high credit scores accuracy; see, e.g., Petropoulos et al (2019); Zhao et al (2015).…”