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 Swarna, T.; Kulkarni, Shraddha; Kumar, Mayank; Choephel, Tenzin; Mohan Raju, B.H.L.; Gosavi, Shruti Title A name given to the resource AI-Driven Enhancements in Indian Payment Systems: A Futuristic Perspective on Financial Applications and Modifications 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 1st International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025;pp.778-783 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/CE2CT64011.2025.10939302" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/CE2CT64011.2025.10939302</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105002700846?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105002700846?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 Swarna T., Gitam University, Department of Finance, Telangana, Hyderabad, India; Kulkarni S., IIMS, Department of Human Resource Management(HRM), Maharashtra, Pune, India; Kumar M., School of Business and Management, Christ University, Bengaluru, India; Choephel T., Rajiv Gandhi University, Department of Management, Arunachal Pradesh, India; Mohan Raju B.H.L., Mallareddy college of engineering, Department of MBA, Telangana, Hyderabad, India; Gosavi S., Tilak Maharashtra Vidyapeeth, Pune, India Description An account of the resource This study explores the application of machine learning (ML) to improve Indian payment systems, with a focus on AI-driven developments for tasks including fraud detection, transaction validation, and consumer behaviour research. We evaluate the effectiveness of several machine learning (ML) systems, including K-Nearest Neighbours (KNN), Random Forest (RF), Decision Trees (DT), Support Vector Machine (SVM), and Logistic Regression (LR), using a variety of criteria. The results, which include precision, F1 score, accuracy, recall, and AUC, show how well Random Forest and Logistic Regression work to detect fraudulent transactions. Important elements including transaction amount, payment method, and user behaviour patterns are also revealed by the feature importance evaluation. Significant differences exist between models in terms of training times and hyperparameter optimisation outcomes. All things considered, this study highlights how ML models can spur innovation in Indian payment systems, enhancing security, effectiveness, and consumer satisfaction while offering a thorough assessment structure for potential future implementation in the fintech industry. 2025 IEEE. Subject The topic of the resource Agile production; AI; IoT; Smart manufacturing Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833151857-8; 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