UPI FRAUD DETECTION USING ML

Digital Fraud has become a threat across all the sectors. Its pivotal for any organization now to have a dedicated focus to detect and prevent fraud and increase their focus on Security. Machine learning algorithms have shown promise in analysing large volumes of transaction data to identify patterns and anomalies indicative of fraudulent transactions. Making use of a heterogeneous dataset that includes both authentic and fraudulent transactions, we utilize feature engineering and data preparation techniques to identify significant trends. Using past data, the chosen machine learning model is trained and its ability to distinguish between real and fraudulent transactions are evaluated. The model takes into account important features such transaction amount, timestamp, payer and payee data, location, and device information. For a thorough analysis, time-based features are given more weight. The model is included into the UPI system for real-time processing after the selected algorithm has been adjusted to maximize performance. In order to ensure prompt intervention, alert systems are implemented in tandem with the fraud detection model.