Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Doddamani, S.S.Has files: No

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    FinTech Revolution in Bharat
    (Springer Science and Business Media Deutschland GmbH, 2024) Doddamani, S.S.; Bhowmik, B.
    Due to rapid advancements in technology, the financial sector has experienced significant changes in the last few decades. In particular, financial technology (FinTech) has revolutionized the financial services industry, reshaping customer experiences and transforming conventional banking practices. FinTech had a substantial positive impact on the growth of several economies worldwide, and Bharat (India) is at the forefront of this drive. This study explores the advancements of FinTech in Bharat and the role of the public and private sectors in realizing its full potential. The cutting edge technologies like UPI and India Stack and their economic impacts are discussed. The study also focuses on how government initiatives and FinTech disruptors are instrumental in expanding financial inclusion in the country. Furthermore, it delves into the challenges FinTechs face today and provides insights into the evolving solutions to address the key issues. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
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    Money Laundering Detection in Imbalanced E-wallet Transactions with Threshold Optimization
    (Institute of Electrical and Electronics Engineers Inc., 2024) Doddamani, S.S.; Girish, K.K.; Bhowmik, B.
    The swift embrace of technology within the financial industry has driven the extensive utilization of electronic payment systems, providing smooth money transfers and substituting outdated paper-based procedures. Consequently, mobile payment systems centered on electronic wallets (e-wallets) have signifi-cantly transformed contemporary finance, introducing improved security measures and transaction functionalities. However, the prevalence of unlawful activities, including money laundering and associated fraud via e-wallets, presents a substantial threat to the integrity of the financial sector. This research paper delves into the pivotal role of machine learning models in identifying money laundering activities within e-wallet transactions. The study focuses on addressing the imbalance inherent in the PaySim dataset through the oversampling technique. Employing three distinct models - Logistic Regression (LR), Gradient Boosting, and XGBoost - the research systematically evaluates their effectiveness. Notably, XGBoost emerged as the standout performer, showcasing exceptional accuracy at 99.88%, precision at 0.9984, and sensitivity at 0.999. Furthermore, a threshold moving technique is employed to enhance the model's efficiency, optimizing its performance in detecting potential instances of money laundering within e-wallet transactions. © 2024 IEEE.
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    Leveraging Hybrid Modeling for Enhanced Runtime Prediction in Big Data Jobs
    (Institute of Electrical and Electronics Engineers Inc., 2024) Singh, R.; Zadokar, V.N.; Kumar, S.; Doddamani, S.S.; Bhowmik, B.
    In an era of rapid data expansion, big data has significantly transformed various industries, redefining the processes of data processing, analysis, and utilization. The widespread adoption of digital technologies has driven this surge in big data, leading to an unprecedented accumulation of information from sources such as social media, sensors, and transactions. As big data evolves, it presents significant challenges and unique opportunities, necessitating innovative solutions to leverage its potential fully. One critical challenge in big data environments is accurately predicting job runtimes, essential for optimizing resource utilization and enhancing overall system performance. Current approaches, including analytical models and machine learning algorithms, often need help to manage the complexities of unstructured data and maintain interpretability effectively. This paper proposes a novel hybrid modeling approach that integrates the strengths of both techniques to improve job runtime predictions. The hybrid architecture combines an analytical model, which captures the intricate characteristics of jobs and execution environments, with a machine learning model trained to detect patterns and relationships in historical data. As demonstrated on real-world big datasets, the hybrid model achieves greater accuracy by merging these capabilities. Utilizing the flexible capabilities of PySpark and incorporating advanced feature engineering techniques, the model dynamically adapts to various dataset sizes and complexities, ensuring robust performance across different scenarios. © 2024 IEEE.