AI-Powered Fraud Detection in Financial Transactions Using Full Stack Web Development

Abstract

Financial fraud has become increasingly sophisticated, leading to significant losses for banks and financial institutions worldwide. Traditional rule-based detection systems struggle to detect complex fraud patterns in real-time transactions. This project proposes an AI-powered fraud detection system leveraging machine learning algorithms integrated with a full stack web application. The system collects transaction data, preprocesses it, and applies predictive models such as Random Forest, XGBoost, and Neural Networks to classify transactions as legitimate or fraudulent. The web application provides an interactive dashboard for real-time monitoring, reporting, and visualization of fraud alerts. The proposed approach enhances detection accuracy, reduces false positives, and enables instant response to suspicious activities, making financial transactions more secure.