IDENTIFYING SUSPICIOUS BANK TRANSACTIONS

This project presents a comprehensive Bank Transaction Fraud Detection System using Flask, a Python web framework.This system combines rule-based risk assessment with a user-friendly web interface to detect suspicious transactions in real-time.The solution is designed to help financial institutions identify potentially fraudulent activities by analyzing various transaction features and calculating a risk score based on predefined heuristics.The system architecture consists of three main components: a Flask backend API, a rule-based fraud detection engine, and an interactive web interface.The backend API handles incoming transaction data, processes it through the detection engine, and returns risk assessments.The fraud detection engine calculates risk scores based on multiple factors including transaction amount, time of day, location, account balance ratio, and transaction type.These factors are weighted and combined to produce a probability score between 0 and 1, which is then categorized into low, medium, or high-risk levels.The system also provides detailed recommendations for each transaction, suggesting actions like blocking, flagging for review, or approving based on the risk level.The frontend interface is built with HTML, CSS, and JavaScript, featuring a responsive design with animated elements for better user experience.It includes a transaction analysis form where users can input transaction details, and a dashboard displaying system statistics and recent detections.The interface dynamically updates to show risk assessments, probability visualizations, and identified risk factors.The system maintains a transaction history limited to the 100 most recent transactions for performance optimization.