Authors - Samrudhi Pustole, Hemal Rajput, Linisha Thakor, Dhanashri Gawai, R. B. Murumkar Abstract - Fraud detection is a critical challenge in financial transactions, requiring advanced machine learning models to distinguish between genuine and fraudulent activities. This project focuses on LSTMbased fraud detection, leveraging historical transaction data to identify suspicious patterns. The model processes multiple attributes, including transaction amount, category, user job type, geolocation, and time-based parameters, to assess fraud risk. In addition to the LSTM model, we conducted single-attribute fraud analysis using various models, evaluating their individual impact on fraud detection. This helped determine the most influential features in predicting fraudulent transactions. The system is integrated into a web-based application built with React and Flask, allowing users to input transaction details and receive a fraud score in real-time. The backend ensures efficient data preprocessing using feature scaling and categorical encoding, aligning new transactions with the trained model’s feature space. Through extensive testing with high-risk and low-risk transaction scenarios, the system demonstrates its ability to detect fraudulent transactions with high accuracy, making it a valuable tool for financial security. . . .
