UPI fraud detection in mobile payment using a boost based framework Available to Purchase

As the prevalence of e-payment systems, particularly Unified Payment Interface (UPI), continues to increase, the need for robust fraud detection systems becomes even more crucial. This study proposes a machine learning approach to tackle UPI fraud challenges by distinguishing between legitimate and deceptive transactions, thus bolstering the security of digital financial activities. The methodology involves gathering labeled datasets containing transaction details like amount, time, location, and user behavior. Engineered features aim to capture discernible patterns indicating potential fraud, crucial given the prevalence of digital transactions. Focus lies on an efficient fraud detection system utilizing XGBoost and LSTM algorithms. LSTMs excel in learning sequential data, while XGBoost handles complex data relationships, ideal for analyzing user behavior in real-time. Data preprocessing ensures quality, handling missing values, converting categories, and normalizing features. Feature engineering extracts indicators like transaction frequency and temporal patterns. The Hybrid LSTM and XGBoost model is fine-tuned with selected hyperparameters, extensively trained on labeled data to predict fraudulent user probabilities. Performance evaluation incorporates precision, recall, F1-score, and ROC curve analysis. Cross-validation and ensemble methods improve stability and accuracy. Feature importance analysis guides model refinement, focusing on influential factors in fraud detection. Real-time monitoring adapts to evolving fraud patterns through continuous learning with new data. Collaborating with industry tools like two-factor authentication enhances security. Compliance with regulations and transparent communication about fraud measures are crucial. By integrating XGBoost and LSTM with continuous learning and real-time monitoring, this research aims to develop a dependable fraud detection system, fostering user trust in digital transactions.