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Artificial Intelligence-Based Strategies for Improving Customer Retention and Satisfaction in the Insurance Industry
Conference proceeding   Open access

Artificial Intelligence-Based Strategies for Improving Customer Retention and Satisfaction in the Insurance Industry

Mahender Singh
2025 5th International Conference on Electrical, Computer and Energy Technologies (ICECET), pp 1-6
03 Jul 2025
DOI: https://doi.org/10.1109/ICECET63943.2025.11472238
url
https://doi.org/10.1109/ICECET63943.2025.11472238View
Published, Version of Record (VoR) Open

Abstract

Central Processing Unit Churn Prediction Circuits Communication systems Customer Retention Feedback Frequency modulation Insurance Industry Internet Machine Learning (ML) Medical Insurance Premium Dataset Policyholder Satisfaction Predictive Analytics Radio broadcasting Radio frequency Integrated Circuits Internet of Things
A major problem in many sectors, including insurance, is customer retention. Because insurance contracts are often renewed annually, keeping consumers inside the insurance business is much more difficult than in any other sector. The primary goals of this study are to identify the risk variables linked to churn, identify the consumers who are leaving, and predict the period till churn. Accurate insurance premium prediction is crucial for optimizing customer retention and satisfaction in the insurance industry. The use of ML models to improve premium price prediction accuracy is the focus of this research. The study implements and compares Random Forest (RF) and Gradient Boosting Regression (GBR) with baseline models such as Support Vector Regression (SVR) and Extreme Gradient Boosting (XGB). Experimental results demonstrate that GBR achieved superior predictive performance, attaining an R 2 of 0.8652 and an RMSE of 0.3839, outperforming RF and SVR. Additionally, XGB exhibited the lowest RMSE (0.2231), highlighting its effectiveness in minimizing prediction errors. These results highlight the promise of cutting-edge ML methods for enhancing the forecasting of insurance premiums, which in turn might improve risk assessment, pricing tactics, and customer satisfaction. Future research will explore deep learning models and real-time data integration for further enhancement.

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