T. Berdamurat

COMPARATIVE ANALYSIS OF ENSEMBLE MACHINE LEARNING MODELS FOR CREDIT SCORING AND FRAUD DETECTION IN FINANCIAL DECISION SUPPORT SYSTEMS

This paper investigates the performance of ensemble machine learning algorithms - Random Forest, Gradient Boosting, and XGBoost - for solving the dual-classification problem of credit scoring and fraud detection within financial decision support systems. The study utilizes an event-driven architecture for real-time dat...