Abstract

Financial fraud, especially in credit card transactions, is a growing concern. To tackle this, data mining techniques are used to automatically analyze large and complex financial datasets. Detecting credit card fraud is tricky because the patterns of normal and fraudulent behavior keep changing, and the data about fraud is much less common compared to legitimate transactions Several techniques were tried on a dataset from European cardholders, including Decision Tree, Random Forest, SVC, XGBoost, K-Nearest Neighbors, and Logistic Regress The dataset had information from 284,786 credit card transactions. To address the challenges, six advanced data mining approaches (Logistic Regression, K-Nearest Neighbors, Support Vector Classifiers, Decision Tree, Random Forests, and XGBoost) are evaluated. A comparative analysis is conducted to identify the best-performing model.