Fraud Shield

The rapid growth of digitalpayment systems has transformed financial transactions by enabling secure and instant money transfers through mobile platforms. Among these systems, Unified Payments Interface has gained widespread adoption due to its convenience and interoperability across banks. However, the increasing volume of digital transactions has also led to a rise in fraudulent activities, including identity theft, phishing attacks, and unauthorized fund transfers.Detecting fraudulent transactions in real time is challenging because of large-scale data generation and evolving fraud patterns. This research proposes a machine learning based fraud detection framework designed to analyse transaction behaviour and identify suspicious activities with high accuracy. The system examines multiple features such as transaction amount, frequency, time patterns, and device information to distinguish between legitimate and fraudulent transactions. Data preprocessing and feature engineering techniques are applied to enhance predictive performance. Experimental evaluation demonstrates that ensemble learning models provide improved detection rates while minimizing false alarms. The proposed solution aims to strengthen digital payment security by providing a scalable and efficient fraud detection mechanism