Credit Card Fraud Detection
This is an incremental improvement for financial institutions dealing with fraud detection.
The study tackled credit card fraud detection by evaluating five machine learning models with sampling techniques, finding that a hybrid method achieved the best balance between recall and precision, particularly improving MLP and KNN performance.
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-Layer 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 balance between recall and precision, especially improving MLP and KNN performance.