Credit Card Fraud Detection Using Machine Learning

Abstract - With the rise in online transactions and digital banking, credit card fraud has emerged as a critical threat to financial institutions and customers. Traditional rule-based systems fail to adapt to evolving fraud patterns, resulting in poor detection rates and financial losses.This project presents a credit card fraud detection system using machine learning algorithms—K-Nearest Neighbors (KNN), Logistic Regression, Support Vector Machine (SVM), and Decision Tree. We utilized a publicly available dataset of real credit card transactions, applied preprocessing and balancing techniques to address class imbalance, and trained each model on the transformed data.Model performance was evaluated using accuracy, precision, recall, and F1-score. The Decision Tree and SVM classifiers demonstrated high recall values, suitable for minimizing false negatives in fraud detection. This research contributes to financial fraud prevention by implementing efficient ML techniques to detect fraudulent behavior in real-time. Keywords: Credit card fraud, machine learning, KNN, SVM, Logistic Regression, Decision Tree, anomaly detection, imbalanced data