Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Multani, Danish; Radhakrishnan, G.V.; Shankar, Uma; Upreti, Kamal; Gupta, Komal; Tiwari, Akhilesh Title A name given to the resource Fraud Prevention in Banking: Machine Learning-driven Approaches for Detecting Payment Anomalies Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 International Conference in Advances in Power, Signal, and Information Technology, APSIT 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/APSIT63993.2025.11086276" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/APSIT63993.2025.11086276</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105013069516?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105013069516?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Multani D., UST Global Solutions Gurgaon, Haryana, India; Radhakrishnan G.V., Kalinga School of Management, Kalinga Institute of Industrial Technology, Bhubaneswar, India; Shankar U., Qaiwan International University, Faculty of Management and Social Sciences, Kurdistan, Sulaymaniyah, Iraq; Upreti K., Christ University, Delhi NCR Campus, Department of Computer Science, Ghaziabad, India; Gupta K., Accenture, Banglore, India; Tiwari A., Christ University, Delhi NCR Campus, Department of Business and Management, Ghaziabad, India Description An account of the resource The fast-paced development of digital banking has brought with it new convenience but also tremendous challenges in maintaining transaction security. Banks are confronted with mounting threats from malicious activities like identity theft, account takeover, and unauthorized access, which can lead to huge financial losses and loss of customer confidence. This study investigates the formulation of a cybersecurity framework for fraud prevention in banking through machine learning algorithms. A transactional real-world dataset of 200,000 instances from LOL Bank Pvt. Ltd. was used to construct and evaluate predictive models. Preprocessing included categorical encoding, temporal feature engineering, and synthetic minority oversampling (SMOTE) for class imbalance handling. Three machine learning classifiers - Logistic Regression, Random Forest, and XGBoost - have been compared using measures of accuracy, precision, recall, F1-score, and ROC-AUC. Results show that ensemble models significantly outperformed logistic regression by a wide margin, with Random Forest and XGBoost both achieving over 91% accuracy and very good discrimination power. The study emphasizes how well machine learning-based systems detect theft in real time and outlines avenues for future research to enhance detection using adaptive and interpretable AI models. 2025 IEEE. Subject The topic of the resource Bank Fraud Detection; Cybersecurity; Financial Security; Fraud Analytics; Machine Learning Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833152989-5; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online