A Comprehensive Review of Hybrid Deep Learning Models for Real-Time UPI Fraud Detection and Digital Payment Security

Abstract

The emergence of the Unified Payments Interface (UPI) has transformed the digital payment ecosystem by enabling seamless, instant, and interoperable financial transactions across India. Its widespread acceptance has accelerated the shift toward cashless payments and increased the availability of digital financial services for millions of users. However, the rapid growth in transaction volume and user adoption has also created new opportunities for cybercriminals to exploit vulnerabilities within the digital payment infrastructure.Financial fraud associated with UPI platforms has become increasingly sophisticated, involving techniques such as phishing campaigns, fraudulent QR codes, identity impersonation, account hijacking, social engineering attacks, and the misuse of mule accounts. These evolving threats generate complex transaction patterns that are often difficult to detect using conventional rule-based security mechanisms. As fraud strategies continue to change, static detection systems struggle to provide accurate and timely identification of suspicious activities.Recent progress in Artificial Intelligence, Machine Learning, and Deep Learning technologies has significantly enhanced the capability of fraud detection systems. One of these developments has caught a lot of interest from researchers is hybrid deep learning methods that are able to integrate the benefits of several computational models. Several techniques can be combined in a unified approach for enhancing fraud detection, such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, Autoencoders, Graph Neural Networks (GNNs), Explainable Artificial Intelligence (XAI), and Federated Learning. These architectures enable real-time analysis, adaptive learning, privacy protection, and efficient management of large-scale transactional data. This review is based on the recent research and studies regarding the detection of fraudulent activities in UPI-based payment environment using hybrid deep learning techniques. It offers detailed evaluations of the current models, their strengths and weaknesses, explores current issues and challenges, and suggests areas of future work. These results suggest that hybrid deep learning architectures are promising to improve the security of digital payment systems through better detection accuracy, fewer false alarms, safeguarding sensitive user data, and greater transparency of automated decision-making. Thus, these wise frameworks are a valuable basis for developing safe, reliable, and trustworthy digital financial ecosystems.

Introduction

The text discusses the growing importance of securing India's Unified Payments Interface (UPI) ecosystem against increasingly sophisticated digital payment fraud using Artificial Intelligence (AI) and Deep Learning (DL) techniques.

UPI has transformed digital payments in India by enabling fast, low-cost, and convenient transactions. However, its widespread adoption has also attracted cybercriminals who employ fraud methods such as phishing, QR code scams, account takeovers, SIM swap attacks, mule accounts, and fake UPI applications. Traditional fraud detection systems based on fixed rules and predefined thresholds struggle to detect evolving and complex fraud patterns.

To address these challenges, researchers have increasingly adopted machine learning and deep learning approaches. While traditional machine learning models such as Decision Trees, Random Forests, and SVMs can identify suspicious behavior, they often require extensive feature engineering and have limitations in analyzing sequential transaction patterns. Deep learning models such as CNNs, LSTMs, Autoencoders, and Graph Neural Networks (GNNs) provide better fraud detection by automatically learning complex behavioral patterns from large-scale transaction data.

The review highlights several advanced hybrid deep learning models:

• CNN-LSTM: Combines feature extraction and sequence analysis for high fraud detection accuracy.
• Autoencoder-LSTM: Detects anomalies and previously unseen fraud patterns.
• GNN-LSTM: Identifies fraud networks, mule accounts, and money laundering activities.
• Federated Deep Learning: Enables collaborative fraud detection while preserving user privacy.
• Explainable AI (XAI) Models: Improve transparency using techniques such as SHAP and LIME.

Comparative studies show that hybrid models generally achieve the highest accuracy (up to 98%) and are better suited for real-time fraud detection than standalone approaches.

The paper identifies key research gaps, including:

• Lack of public UPI fraud datasets.
• Class imbalance between legitimate and fraudulent transactions.
• Poor interpretability of deep learning models.
• High computational requirements.
• Data privacy concerns.
• Limited information sharing between institutions.
• Difficulty detecting emerging fraud techniques.

To overcome these limitations, the authors propose a Hybrid Fraud Detection Framework consisting of six layers:

1. Data Collection Layer – gathers transaction, device, behavioral, and network data.
2. Preprocessing Layer – cleans, normalizes, and balances data.
3. Hybrid Deep Learning Layer – integrates CNN, LSTM, Autoencoder, and GNN models.
4. Explainability Layer – provides transparent explanations for fraud predictions.
5. Privacy Preservation Layer – uses Federated Learning and Differential Privacy.
6. Decision Engine – calculates fraud risk scores and generates real-time alerts.

Conclusion

The rapid expansion of Unified Payments Interface (UPI) transactions has significantly increased the demand for intelligent and robust fraud detection mechanisms. Traditional rule-based security systems are becoming inadequate for addressing the growing complexity and constantly evolving nature of financial fraud. As cybercriminals employ increasingly sophisticated attack strategies, there is a critical need for adaptive, data-driven approaches that can identify fraudulent activities accurately and in real time. This review explores the potential of hybrid deep learning frameworks in enhancing the security of digital payment systems. By combining advanced technologies such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, Autoencoders, Graph Neural Networks (GNNs), Explainable Artificial Intelligence (XAI), and Federated Learning, these models offer a comprehensive and effective solution for fraud detection. The integration of multiple deep learning techniques improves detection performance, strengthens anomaly identification capabilities, enables real-time transaction monitoring, and enhances the protection of sensitive financial data. In addition, the incorporation of explainability and privacy-preserving methodologies addresses key concerns related to transparency, regulatory compliance, and user confidence. These features are essential for building trustworthy fraud detection systems that can operate effectively in modern financial environments. The study further highlights the necessity of scalable and adaptive architectures capable of responding to emerging fraud patterns and evolving cybersecurity threats. Overall, privacy-preserving and explainable hybrid deep learning frameworks provide a strong foundation for securing next-generation digital payment ecosystems. Their ability to analyze transactional behavior from temporal, behavioral, and relational perspectives makes them highly effective in detecting fraudulent activities within UPI networks. Continued advancements and research in this domain will be instrumental in improving the security, reliability, and trustworthiness of digital financial services in the future.

References

[1] N. B. Chakka and S. S. Saheb, “Mobile Payment Fraud Detection in UPI Through Machine Learning Techniques: A Systematic Review,” International Journal of Research and Analytical Reviews, vol. 12, no. 2, pp. 115–128, 2025. [2] J. Kumar and N. Rani, “Optimized Machine Learning and Deep Learning Approaches for Effective Detection of Fraud in UPI Transactions,” International Journal of Scientific Advances and Technology, vol. 5, no. 3, pp. 45–58, 2025. [3] T. Sekhar and G. V. Kumar, “UPI Fraud Detection Using Hybrid Machine Learning Models with Explainable Risk Scoring and Real-Time Monitoring,” International Journal of Advanced Research in Computer and Communication Engineering, vol. 15, no. 4, pp. 89–98, 2026. [4] S. Sahu, A. Mishra, and R. Sharma, “Detection of Mule Accounts and Fraudsters in UPI Transactions Using Artificial Intelligence Techniques,” IRE Journals, vol. 9, no. 1, pp. 112–121, 2026. [5] L. Sen, “UPI Fraud Detection and Prevention: Emerging Trends and Challenges,” SSRN Electronic Journal, pp. 1–15, 2026. [6] Y. Qin, J. Yang, and Z. Wang, “Real-Time Fraud Detection in Enterprise Payment Ecosystems Using Deep Learning,” Journal of Computational Engineering and Information Management, vol. 8, no. 2, pp. 45–60, 2026. [7] M. Z. H. George, M. K. Alam, and M. T. Hasan, “Machine Learning for Fraud Detection in Digital Banking: A Systematic Literature Review,” arXiv preprint arXiv:2510.05167, 2025. [8] V. Jurgovsky, M. Granitzer, S. Ziegler, S. Calabretto, P. Portier, L. He-Guelton, and O. Caelen, “Sequence Classification for Credit Card Fraud Detection,” Expert Systems with Applications, vol. 100, pp. 234–245, 2018. [9] A. Roy, J. Sun, R. Mahoney, L. Alonzi, S. Adams, and P. Beling, “Deep Learning Detecting Fraud in Credit Card Transactions,” in Proc. Systems and Information Engineering Design Symposium (SIEDS), 2018, pp. 129–134. [10] B. Baesens, V. Van Vlasselaer, and W. Verbeke, Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques. Hoboken, NJ, USA: Wiley, 2015. [11] D. Pumsirirat and L. Yan, “Credit Card Fraud Detection Using Deep Learning Based on Autoencoder and Restricted Boltzmann Machine,” International Journal of Advanced Computer Science and Applications, vol. 9, no. 1, pp. 18–25, 2018. [12] S. Fiore, F. De Santis, A. Perla, P. Zanetti, and F. Palmieri, “Using Generative Adversarial Networks for Improving Classification Effectiveness in Credit Card Fraud Detection,” Information Sciences, vol. 479, pp. 448–455, 2019. [13] T. N. Kipf and M. Welling, “Semi-Supervised Classification with Graph Convolutional Networks,” in International Conference on Learning Representations (ICLR), 2017. [14] P. Velickovic et al., “Graph Attention Networks,” in International Conference on Learning Representations (ICLR), 2018. [15] A. Vaswani et al., “Attention Is All You Need,” in Advances in Neural Information Processing Systems, vol. 30, pp. 5998–6008, 2017. [16] A. Dosovitskiy et al., “An Image is Worth 16×16 Words: Transformers for Image Recognition at Scale,” in International Conference on Learning Representations (ICLR), 2021. [17] H. B. McMahan et al., “Communication-Efficient Learning of Deep Networks from Decentralized Data,” in Proc. 20th International Conference on Artificial Intelligence and Statistics (AISTATS), 2017, pp. 1273–1282. [18] C. Dwork, “Differential Privacy,” in International Colloquium on Automata, Languages and Programming, 2006, pp. 1–12. [19] S. Lundberg and S. Lee, “A Unified Approach to Interpreting Model Predictions,” in Advances in Neural Information Processing Systems, vol. 30, pp. 4765–4774, 2017. [20] M. T. Ribeiro, S. Singh, and C. Guestrin, “Why Should I Trust You? Explaining the Predictions of Any Classifier,” in Proc. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 1135–1144. [21] R. Chalapathy and S. Chawla, “Deep Learning for Anomaly Detection: A Survey,” arXiv preprint arXiv:1901.03407, 2019. [22] N. Carlini et al., “The Role of Federated Learning in Privacy-Preserving Financial Analytics,” IEEE Security & Privacy, vol. 20, no. 4, pp. 48–56, 2022. [23] S. Wang, T. Tuor, T. Salonidis, K. Leung, C. Makaya, T. He, and K. Chan, “Adaptive Federated Learning in Resource-Constrained Edge Computing Systems,” IEEE Journal on Selected Areas in Communications, vol. 37, no. 6, pp. 1205–1221, 2019. [24] J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” in Proc. NAACL-HLT, 2019, pp. 4171–4186. [25] OpenAI, “GPT-4 Technical Report,” arXiv preprint arXiv:2303.08774, 2023.

Copyright

Copyright © 2026 Ashish Malik, Jitender Kumar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.