Modeling Credit Scoring Framework Using Self-Organized Map and Hybrid Neural Network Ensembles

assess the creditworthiness of both individuals and businesses. Evaluating the risk of business failure is especially significant for stakeholders like lenders and investors. Credit scoring provides a structured and data-driven method to predict these risks by analyzing financial, operational, and historical information. Applications of credit scoring include risk assessment, financial stability forecasting, trend identification, risk-based pricing, and default prediction. By providing a data-driven evaluation of credit risk, it enables institutions to make informed decisions, reduce potential losses, and improve risk management strategies. This research aims to bridge this gap by analyzing the effectiveness of neural network ensembles and hybrid neural network models using three standard credit scoring benchmark datasets: Australian, German, and Japanese. Experimental results show that while standalone neural networks achieve accuracies of 87.44%, 83.37%, and 85.08% respectively, ensemble models (weighted voting) improve performance to 92.75%, 89.34%, and 89.97%. Hybrid neural networks outperform both in the Australian dataset (93.61%), but show similar performance in the German (89.45%) and Japanese (89.17%) datasets. Although hybrid models demonstrate slightly higher accuracy on one dataset, the overall difference between hybrid and ensemble models is not statistically significant. This study provides a comprehensive comparative analysis to support the development of more accurate bankruptcy prediction systems and credit risk modeling strategies.