Abstract
Private insurance is already one of the sectors with the greatest growth potential. For the majority of high-value assets today, including houses, jewelry, cars, and other valuable items, there are insurance solutions available. To maximize profits while handling client claims, insurance firms are leading have adopted cutting-edge operations, procedures, and mathematical models for estimating risks and serving customer best interests, while also maximizing profits. In this work, we aim to develop a machine learning-based automated framework that minimizes human involvement, protects insurance operations, identifies high-risk consumers, uncovers false claims, and lowers financial loss for the insurance industry. This framework consists of fraud detection followed by risk prediction and premium prediction. We trained and tested different machine learning approaches for each of the three insurance processing tasks; the observations are presented in this article.
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Sreegeethi, D., Gorti, S.S., Leela Akshaya, T., Sowmya Kamath, S. (2024). Automated Health Insurance Management Framework with Intelligent Fraud Detection, Premium Prediction, and Risk Prediction. In: Nanda, S.J., Yadav, R.P., Gandomi, A.H., Saraswat, M. (eds) Data Science and Applications. ICDSA 2023. Lecture Notes in Networks and Systems, vol 818. Springer, Singapore. https://doi.org/10.1007/978-981-99-7862-5_21
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