Credit Card Fraud Detection using Machine Learning

Abstract

Credit card fraud has become a growing concern with the rapid expansion of digital payments and online transactions. Detecting fraudulent activities in real-time is critical to minimize financial losses and ensure customer trust. This project aims to develop a machine learning-based system for detecting credit card fraud by analyzing transaction patterns and identifying anomalies that may indicate fraudulent behavior.
The system leverages historical transaction data, including features such as transaction amount, time, location, and merchant category, to train supervised learning models like Logistic Regression, Decision Trees, and Random Forests. Advanced techniques such as SMOTE (Synthetic Minority Over-sampling Technique) are used to handle data imbalance, as fraudulent transactions typically represent a small fraction of the total data. The model's performance is evaluated using metrics like accuracy, precision, recall, and F1-score to ensure effective detection with minimal false positives.
This approach enables proactive fraud prevention and supports financial institutions in enhancing their security systems. The project demonstrates how data-driven techniques can be applied to address real-world problems in the financial domain.