ENHANCING UPI TRANSACTION SECURITY: A DEEP LEARNING APPROACH FOR FRAUD DETECTION

International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)

Abstract

UPI fraud has become a major concern in digital payments, with cybercriminals using advanced techniques to exploit security loopholes. Existing fraud detection systems often fail to accurately predict fraudulent transactions due to their evolving nature. Traditional models like Convolutional Neural Networks (CNN) struggle with large datasets, requiring significant computational power and time, making them inefficient for real-time fraud detection. To address these limitations, a deep learning-based ensemble model is proposed, combining Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU). ANN detects complex transaction patterns, LSTM identifies sequential dependencies in financial data, and GRU optimizes efficiency by reducing parameters while maintaining accuracy. This integration enhances fraud detection by improving precision and minimizing overfitting. The ensemble model effectively balances computational efficiency and predictive accuracy. Unlike CNN, which faces challenges with large-scale transactions, this approach processes vast amounts of data in real time. Moreover, by leveraging deep learning, the model continuously adapts to emerging fraud patterns, increasing its detection capability over time. This proactive fraud detection system strengthens security in digital payments, reducing financial losses for individuals and organizations while enhancing trust in online transactions.

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Keywords

UPI Fraud, Fraud Detection, Deep Learning, Ensemble Model, Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Real-Time Detection, Transaction Security, Cybersecurity.