Evaluation of accuracy and performance of machine learning models for prognosis customer churn in insurance companies

A comprehensive comparative study of several machine learning algorithms for predicting customer churn in an insurance company was conducted using data from an open dataset. Both predictive quality metrics and computational efficiency were examined. The topic is relevant due to intense competition in the insurance market and the substantial costs of losing customers; early detection of a customer’s intention to leave enables targeted retention actions. The aim of the study is to assess the accuracy and performance of different machine-learning models capable of predicting churn. The experiments used open data on insurance customers (life-insurance industry) containing features that describe claim events, historical records, and the churn outcome. We also added factor analysis: correlations between features and the target variable were investigated, factor analysis was performed, and feature importance related to churn was evaluated. The results show that most models achieved similarly high predictive quality due to the presence of a dominant churn-risk factor, but differed in performance: logistic regression and gradient boosting trained an order of magnitude faster than support vector machines and random forests while using substantially less memory. These findings confirm that modern ensemble algorithms can provide high-accuracy churn prediction at reasonable resource costs. Their use is advisable for insurers to promptly identify high-risk clients, such as those with large claims, and to take proactive measures to retain them.

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