Multi-Objective Optimization of Random Forest for Credit Card Fraud Detection

Abstract

This thesis focuses on financial fraud detection, a major challenge for modern banking systems. The goal is to build an efficient machine learning model that can identify fraudulent transactions in imbalanced datasets. Real-world payment data was preprocessed and analyzed using supervised models such as Decision Trees and Random Forests. Multi-objective optimization with the NSGA-II algorithm was applied to enhance the balance between precision and recall. Results show that the optimized Random Forest model achieves 78% recall on the validation set while keeping false positives low. This work contributes to improving automated fraud detection systems and lays the groundwork for future hybrid or real-time approaches.