ARCHIVES
Original Article
Sentinel-UPI: A Graph Neural Network Approach for Real-Time Fraud Detection in High-Volume Unified Payments Interface (UPI) Transactions
Yash Aggarwal1
Abhishek Kumar2
Deepti Kushwaha3
1 2 3 Trinity Institute of Innovations in Professional Studies (GGSIPU) Uttar Pradesh, India.
Published Online: January-February 2026
Pages: 56-64
Cite this article
↗ https://www.doi.org/10.59256/ijrtmr.20260601008
References
1. PwC India, "The Indian Payments Handbook 2023-2028: Driving the Digital Revolution," PwC, Industry Report, 2023. [Online]. Available: pwc.in
2. Press Information Bureau (PIB), "Digital Payment Transactions Surge: RBI and NPCI Data Analysis," Ministry of Finance, Govt. of India, Mar 2024.
3. S. Dhieb et al., "A very secure framework for credit card fraud detection in ubiquitous banking," in 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), 2019.
4. A. Dal Pozzolo et al., "Learned lessons in credit card fraud detection from a practitioner perspective," Expert Systems with Applications, vol. 41, no. 10, pp. 4915-4928, 2014.
5. T. Chen and C. Guestrin, "XGBoost: A Scalable Tree Boosting System," in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016.
6. P. Schlör et al., "Autoencoders for Fraud Detection in Financial Transactions," in IEEE Access, vol. 9, 2021.
7. T. N. Kipf and M. Welling, "Semi-Supervised Classification with Graph Convolutional Networks," in International Conference on Learning Representations (ICLR), 2017.
8. M. Weber et al., "Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks," arXiv preprint arXiv:1908.02591, 2019.
9. P. Veličković et al., "Graph Attention Networks," in International Conference on Learning Representations (ICLR), 2018.
10. T. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, "Focal Loss for Dense Object Detection," in IEEE International Conference on Computer Vision (ICCV), 2017.
11. E. A. Lopez-Rojas, A. Elmir, and S. Axelsson, "PaySim: A financial mobile money simulator for fraud detection," in 28th European Modeling and Simulation Symposium (EMSS), 2016.
12. D. Cheng et al., "Graph Neural Networks for Financial Fraud Detection: A Review," Frontiers of Computer Science, 2024.
13. NPCI, "UPI Procedural Guidelines and Operating Circulars," National Payments Corporation of India, Technical Document, 2023.
14. Z. Wu et al., "A Comprehensive Survey on Graph Neural Networks," IEEE Transactions on Neural Networks and Learning Systems, 2020.
15. J. Hamilton et al., "Graph Representation Learning,"Morgan & Claypool Publishers, 2020.
16. W. Hamilton, R. Ying, and J. Leskovec, "Inductive Representation Learning on Large Graphs," in Advances in Neural Information Processing Systems (NeurIPS), 2017.
17. M. Schlichtkrull et al., "Modeling Relational Data with Graph Convolutional Networks," in European Semantic Web Conference, 2018.
18. Z. Ying et al., "GNN Explainer: Generating Explanations for Graph Neural Networks," in Advances in Neural Information Processing Systems, 2019.
19. N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, "SMOTE: Synthetic Minority Over-sampling Technique,” Journal of Artificial Intelligence Research, vol. 16, pp. 321-357, 2002.
20. Reserve Bank of India (RBI), "Master Direction on Digital Payment Security Controls," RBI Regulatory Framework.
21. Y. Dou, Z. Liu, L. Sun, Y. Deng, H. Peng, and P. S. Yu, "Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters," in Proceedings of the 29th ACM International Conference on Information & Knowledge Management (CIKM), 2020, pp. 315-324.
2. Press Information Bureau (PIB), "Digital Payment Transactions Surge: RBI and NPCI Data Analysis," Ministry of Finance, Govt. of India, Mar 2024.
3. S. Dhieb et al., "A very secure framework for credit card fraud detection in ubiquitous banking," in 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), 2019.
4. A. Dal Pozzolo et al., "Learned lessons in credit card fraud detection from a practitioner perspective," Expert Systems with Applications, vol. 41, no. 10, pp. 4915-4928, 2014.
5. T. Chen and C. Guestrin, "XGBoost: A Scalable Tree Boosting System," in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016.
6. P. Schlör et al., "Autoencoders for Fraud Detection in Financial Transactions," in IEEE Access, vol. 9, 2021.
7. T. N. Kipf and M. Welling, "Semi-Supervised Classification with Graph Convolutional Networks," in International Conference on Learning Representations (ICLR), 2017.
8. M. Weber et al., "Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks," arXiv preprint arXiv:1908.02591, 2019.
9. P. Veličković et al., "Graph Attention Networks," in International Conference on Learning Representations (ICLR), 2018.
10. T. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, "Focal Loss for Dense Object Detection," in IEEE International Conference on Computer Vision (ICCV), 2017.
11. E. A. Lopez-Rojas, A. Elmir, and S. Axelsson, "PaySim: A financial mobile money simulator for fraud detection," in 28th European Modeling and Simulation Symposium (EMSS), 2016.
12. D. Cheng et al., "Graph Neural Networks for Financial Fraud Detection: A Review," Frontiers of Computer Science, 2024.
13. NPCI, "UPI Procedural Guidelines and Operating Circulars," National Payments Corporation of India, Technical Document, 2023.
14. Z. Wu et al., "A Comprehensive Survey on Graph Neural Networks," IEEE Transactions on Neural Networks and Learning Systems, 2020.
15. J. Hamilton et al., "Graph Representation Learning,"Morgan & Claypool Publishers, 2020.
16. W. Hamilton, R. Ying, and J. Leskovec, "Inductive Representation Learning on Large Graphs," in Advances in Neural Information Processing Systems (NeurIPS), 2017.
17. M. Schlichtkrull et al., "Modeling Relational Data with Graph Convolutional Networks," in European Semantic Web Conference, 2018.
18. Z. Ying et al., "GNN Explainer: Generating Explanations for Graph Neural Networks," in Advances in Neural Information Processing Systems, 2019.
19. N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, "SMOTE: Synthetic Minority Over-sampling Technique,” Journal of Artificial Intelligence Research, vol. 16, pp. 321-357, 2002.
20. Reserve Bank of India (RBI), "Master Direction on Digital Payment Security Controls," RBI Regulatory Framework.
21. Y. Dou, Z. Liu, L. Sun, Y. Deng, H. Peng, and P. S. Yu, "Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters," in Proceedings of the 29th ACM International Conference on Information & Knowledge Management (CIKM), 2020, pp. 315-324.
Related Articles
2026
A Strategic Framework for Depth-Dependent Hydroelectric Conversion along the Indian Coastline
2026
Reimagining Development in India: A Critical Analysis of the Viksit Bharat Vision
2026
AI-Enabled Image Description: Bridging the Gap for the Visually Impaired
2026
Perceived Occupational Risks of Emergency Medical Services Personnel
2026
Origin, Growth and recent Development of Integrated Reporting (IR): A theoretical Review
2026
