REAL-TIME FINANCIAL TRANSACTION FRAUD DETECTION USING BEHAVIORAL PATTERN ANOMALIES

Abstract

Financial fraud has become a major challenge for financial institutions due to the rapid growth of
digital payment systems and online banking. Detecting fraudulent transactions in real time is
essential to prevent financial losses and protect user accounts. This project presents a machine
learning-based system for detecting fraudulent financial transactions using behavioral pattern
anomalies. The system analyzes transaction attributes such as time, transaction amount, and
behavioral features that represent user activity patterns. In this approach, machine learning
algorithms including Logistic Regression, Decision Tree, and Random Forest are used to classify
transactions as legitimate or fraudulent.
The behavioral features capture patterns such as transaction frequency, spending deviation,
device usage, and merchant interaction. These patterns help the system identify unusual activities
that may indicate fraud. The model is trained using historical transaction data and then deployed
through a web-based interface developed using Streamlit, allowing users to input transaction
details and obtain instant fraud predictions. The proposed system demonstrates effective fraud
detection with high accuracy and provides a simple, interactive platform for real-time analysis of
financial transactions. This project highlights the importance of machine learning techniques in
improving the efficiency and reliability of fraud detection systems in modern financial
environments.