Detection of Mule Accounts and Fraudsters in UPI Transactions Using AI and Machine Learning Techniques

References

[1] J. West and M. Bhattacharya, "Intelligent financial fraud detection: A comprehensive review," Computers & Security, vol. 57, pp. 47-66, 2016.

[2] A. Dal Pozzolo, O. Caelen, R. A. Johnson, and G. Bontempi, "Calibrating probability with undersampling for unbalanced classification," in Proc. IEEE Symposium on Computational Intelligence and Data Mining (CIDM), 2015.

[3] Y. Liu, X. Ao, Z. Qin, J. Chi, J. Feng, H. Yang, and Q. He, "Pick and choose: A GNN- based imbalanced learning approach for fraud detection," in Proc. ACM Web Conference (WWW), 2021.

[4] M. Weber, G. Domeniconi, J. Chen, D. K. I. Weidele, C. Bellei, T. Robinson, and C. E. Leiserson, "Anti-money laundering in Bitcoin: Experimenting with graph convolutional networks for financial forensics," in Proc. KDD Workshop on Anomaly Detection in Finance, 2019.

[5] B. Wiese and C. Omlin, "Credit card transactions, fraud detection, and machine learning: Modelling time with LSTM recurrent neural networks," in Innovations in Neural Information Paradigms and Applications, Springer, 2009.

[6] S. Jha, M. Guillen, and J. C. Westland, "Employing transaction aggregation strategy to detect credit card fraud," Expert Systems with Applications, vol. 39, no. 16, pp. 12650- 12657, 2012.

[7] I. Sadgali, N. Sael, and F. Benabbou, "Performance of machine learning techniques in the detection of financial frauds," Procedia Computer Science, vol. 148, pp. 45-54, 2019.

[8] T. N. Kipf and M. Welling, "Semi-supervised classification with graph convolutional networks," in Proc. International Conference on Learning Representations (ICLR), 2017.

[9] P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio, "Graph attention networks," in Proc. International Conference on Learning Representations (ICLR), 2018.

[10] S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural Computation, vol. 9, no. 8, pp. 1735-1780, 1997.

[11] T. Chen and C. Guestrin, "XGBoost: A scalable tree boosting system," in Proc. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016.

[12] S. M. Lundberg and S.-I. Lee, "A unified approach to interpreting model predictions," in Advances in Neural Information Processing Systems (NeurIPS), 2017.

[13] 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.

[14] NPCI, "UPI Product Statistics," National Payments Corporation of India, 2024. [Online]. Available: https://www.npci.org.in/statistics

[15] Reserve Bank of India, "Annual Report on Payment and Settlement Systems," RBI, 2024. [Online]. Available: https://www.rbi.org.in

[16] Financial Action Task Force (FATF), "Money Mules: FATF Guidance," FATF, Paris, 2021.

[17] S. Zanin, D. Papo, P. A. Sousa, E. Menasalvas, A. Nicchi, E. Kubik, and S. Boccaletti, "Combining complex networks and data mining: Why and how," Physics Reports, vol. 635, pp. 1-44, 2016.

[18] J. Zhou, G. Cui, S. Hu, Z. Zhang, C. Yang, Z. Liu, L. Wang, C. Li, and M. Sun, "Graph neural networks: A review of methods and applications," AI Open, vol. 1, pp. 57-81, 2020.

[19] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, "Attention is all you need," in Advances in Neural Information Processing Systems (NeurIPS), 2017.

[20] Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521, no. 7553, pp. 436- 444, 2015.

@article{1716166,
      author = {Siba Sahu, Priyanka Chaudhury, Siba Prasad Senapati, Subhakanta Pradhan, Sunil Kumar Nahak},
      title = {Detection of Mule Accounts and Fraudsters in UPI Transactions Using AI and Machine Learning Techniques},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {10},
      pages = {904-913},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1716166.pdf},
      abstract = {The rapid expansion of the Unified Payments Interface (UPI) ecosystem in India has been accompanied by a significant rise in digital financial fraud, particularly through the use of mule accounts - bank accounts used by fraudsters to launder illicitly obtained funds. Conventional rule-based fraud detection systems are increasingly inadequate against the sophistication and volume of modern UPI fraud. This paper proposes a comprehensive multi-model machine learning framework that integrates Gradient Boosted Decision Trees (GBDT), Graph Neural Networks (GNN), and Long Short-Term Memory (LSTM) networks to detect mule accounts and fraudulent actors in UPI transaction data. The proposed system analyses transaction behaviour, network topology of fund flows, and temporal transaction patterns to accurately identify suspicious accounts. Experimental results demonstrate a detection accuracy of 94.3%, precision of 92.7%, recall of 91.8%, and an F1-score of 0.923, outperforming existing single-model approaches. The framework offers a scalable, interpretable, and real-time deployable solution for banks, payment service providers, and financial regulators},
      keywords = {Mule Account Detection, UPI Fraud, Unified Payments Interface, Graph Neural Networks, LSTM, Gradient Boosted Trees, Financial Fraud Detection, Digital Payments, Anomaly Detection, Anti-Money Laundering, Transaction Monitoring, Machine Learning},
      month = {April},
      doi = {https://doi.org/10.64388/IREV9I10-1716166}
  }