Credit Card Fraud Detection

Iva Popova Hamza A. A. Gardi ETIT - KIT, Germany IIIT at ETIT - KIT, Germany Abstract Credit card fraud remains a significant challenge due to class imbalance and fraudsters mimicking legitimate behavior. This study evaluates five machine learning models - Logistic Regression, Random Forest, XGBoost, K - Nearest Neighbors (KNN), and Multi - Lay er Perceptron (MLP) on a real - world dataset using undersampling, SMOTE, and a hybrid approach. Our models are evaluated on the original imbalanced test set to better reflect real - world performance. Results show that the hybrid method achieves the best bala nce between recall and precision, especially improving MLP and KNN performance. I ntroduction Financial fraud is a significant issue that has been continuously increasing over the past few years due to the ever - growing volume of online transactions conduc ted with credit cards. Credit card fraud (CCF) refers to a type of fraud in which an individual other than the cardholder unlawfully conducts transactions using a card that is stolen, lost, or otherwise misused [ 1 ]. CCF has resulted in billions of dollars in losses for banks and other online payment platforms. According to the Federal Trade Commission (FTC), there were 449,076 reports of CCF in 2024, representing a 7.8% increase from the previous year [ 2 ]. Given this trend, new methods must be employed to c apture patterns and dependencies in the data.