Detection of Fraudulent Activities in Unified Payments Interface using Machine Learning - LSTM Networks

The rise in fraudulent activities has seen a sharp increase alongside the widespread use of digital payment systems such as the Unified Payments Interface (UPI). As a result, the need for reliable fraud detection solutions has become essential. This study introduces a novel approach to detecting UPI fraud by utilizing LSTM networks, which are specialized recurrent neural networks designed for analyzing sequential input. A novel architecture utilizing LSTM is devised to analyze UPI transactions and detect complex patterns that signify potential errors. Through extensive evaluation and testing on real-world datasets, the proposed method has been found to effectively detect fraudulent transactions while minimizing errors. The findings suggest that LSTM networks hold significant promise in enhancing the security of online payment systems and also provide a solid foundation for further investigations into the use of deep learning to improve the identification of fraudulent financial transactions and address the issue of financial fraud.