AI-Driven Enhancements in Indian Payment Systems: A Futuristic Perspective on Financial Applications and Modifications
Agile production; AI; IoT; Smart manufacturing
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.
Swarna, T.; Kulkarni, Shraddha; Kumar, Mayank; Choephel, Tenzin; Mohan Raju, B.H.L.; Gosavi, Shruti
1st International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025;pp.778-783
Institute of Electrical and Electronics Engineers Inc.
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1109/CE2CT64011.2025.10939302 https://www.scopus.com/pages/publications/105002700846?origin=resultslist
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