Faculty Publications

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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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    Historical Analysis of Financial Fraud and Its Future
    (Springer Science and Business Media Deutschland GmbH, 2024) Girish, K.K.; Bhowmik, B.
    As the world is sailing toward a highly advanced digital financial culture with the advent of financial technologies (FinTech), more and more people are now under the shore of financial inclusion. Subsequently, new opportunities are created in the financial sector, making it possible for people who were previously unbanked or underbanked to access financial services. However, the rise in financial fraud and its potential implications are creating a rift in the financial sector, resulting in substantial economic losses across the globe. This paper provides an in-depth comprehension of financial fraud, encompassing its historical perspectives and ramifications. After that, factors contributing to fraudulent behavior are highlighted. In addition, the paper presents a comprehensive framework for fraud classification and accentuates the impacts of financial fraud. Furthermore, the paper underscores the aspects influencing future occurrences of financial fraud, enabling the formulation of proactive strategies to prevent and mitigate it. © 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.
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    Enhancing Money Laundering Detection in Bank Transactions using GAGAN: A Graph-Adapted Generative Adversarial Network Approach
    (Springer Science and Business Media Deutschland GmbH, 2025) Kadamathikuttiyil Karthikeyan, G.; Bhowmik, B.
    The past decade has witnessed profound transformations in the financial sector, driven by the integration of cutting-edge technologies into its core operations. Consequently, banks are increasingly utilizing technologies such as artificial intelligence (AI), blockchain, and big data analytics to offer personalized services, streamline transactions, and improve risk management, enabling the development of new financial products and services that cater to the diverse and evolving needs of customers. Despite these benefits, the banking landscape has also brought about complex challenges, particularly in the fight against money laundering. Money laundering remains a significant threat to the integrity of financial systems, as criminals exploit digital advancements to conceal illicit activities. The growing complexity of digital transactions and the increasing volume of financial data have made detecting and preventing money laundering more challenging than ever. Existing AI-based solutions, while effective to some extent, often grapple with class imbalance issues. This paper addresses the challenge by introducing a novel model named GAGAN (Graph Attention Generative Adversarial Network) and enhances the detection of money laundering activities in bank transactions. The proposed model further addresses the issue of class imbalance, by incorporating Conditional Generative Adversarial Network (cGAN) and Graph Attention Networks (GAT). The GAT classifier is then employed to accurately classify transactions, leveraging attention mechanisms to focus on the most relevant parts of the graph. Empirical results reveal that the proposed model achieves impressive performance metrics, with an accuracy of 98.62%, precision of 98.10%, recall of 98.92%, F1 score of 98.49%, AUC-ROC of 0.99, and a MCC score of 0.991. These results underscore the efficacy of the model in accurately identifying money laundering transactions, showcasing its potential as a robust tool for financial crime detection. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.