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

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