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

The paper presents a study on forecasting customer satisfaction in an insurance company based on machine learning methods. The relevance of the topic is due to the high competition in the insurance market and the need to retain customers by increasing their satisfaction with the service. The purpose of the study is to evaluate the accuracy and performance of models that can predict the level of customer satisfaction with an insurance service based on data on the customer's interaction with the company. Classification algorithms were used as methods. The accuracy and performance of the models was assessed using real data from surveys of insurance company customers. The best were ensemble methods - random forest and gradient boosting, which demonstrated the accuracy of forecasting satisfaction up to 85%, significantly outperforming simpler models. It is shown that gradient boosting allows taking into account nonlinear dependencies of factors, for example, the presence of escalation of the appeal, and thereby more accurately identify "dissatisfied" customers. Currently, such forecasting in insurance companies is either not carried out or relies significantly on random factors. This leads either to too frequent complaints or to low customer satisfaction with their subsequent outflow. The materials of the article are of practical value for insurance organizations: the implementation of the developed models will allow promptly identifying customers with the risk of dissatisfaction and reasonably applying preventive measures, for example, additional service measures or compensation to increase their satisfaction.

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