Faculty Publications

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Author: search.filters.author.Gorti, S.S.Subject: search.filters.subject.Machine learning

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    Automated Health Insurance Management Framework with Intelligent Fraud Detection, Premium Prediction, and Risk Prediction
    (Springer Science and Business Media Deutschland GmbH, 2024) Devaguptam, D.; Gorti, S.S.; Leela Akshaya, T.; Sowmya Kamath, S.
    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. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
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    Automated Health Insurance Processing Framework with Intelligent Fraud Detection, Risk Classification and Premium Prediction
    (Springer, 2024) Devaguptam, S.; Gorti, S.S.; Akshaya, T.L.; Kamath S․, S.
    Private insurance represents one of the sectors poised for significant growth. There are insurance solutions available for most high-value assets such as homes, jewelry, vehicles, and other valuable items. To optimize profitability while managing client claims, insurance companies have embraced advanced operations, procedures, and mathematical models to assess risks and prioritize customer satisfaction, all while maximizing profits. This article introduces a machine learning-driven automated framework designed to reduce human intervention, safeguard insurance operations, identify high-risk clients, detect fraudulent claims, and mitigate financial losses within the insurance sector. Initially, the framework focuses on fraud detection to determine the legitimacy of claims. Genuine claims leverage the patient’s medical history to calculate associated risk factors and premiums. Various machine learning-based classification models and ensemble techniques were employed and evaluated for each of the three insurance processing tasks. Performance assessments using relevant metrics are presented and thoroughly discussed. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.