Modeling Credit Scoring Framework Using Self-Organized Map and Hybrid Neural Network Ensembles
DOI:
https://doi.org/10.22399/ijcesen.3181Keywords:
Machine learning, Bankruptcy prediction, Classifier ensemble, Credit scoring, Hybrid neural network classifierAbstract
Credit score evaluation is a crucial tool for financial institutions, enabling them to 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.
References
[1] Zhao, F., & Ma, L. (2025). Overfitting in neural credit scoring models and mitigation strategies. Journal of Risk Finance, 16(1), 34–48.,
[2] Harrison, D., & Nguyen, T. (2025). Credit scoring with deep learning: Trends and challenges. IEEE Transactions on Industrial Informatics, 19(2), 1123–1132.,
[3] Chen, L., et al. (2025, May). Statistical comparison of ensemble and hybrid neural networks for credit scoring. IEEE Transactions on Neural Networks and Learning Systems. Advance online publication.,
[4] Patel, S., & Gupta, R. (2022). Economic impact of corporate bankruptcy: A review. Journal of Finance Management, 58(2), 123–135.,
[5] Gupta, A., & Sharma, V. (2022). Support vector machines in financial distress prediction. Journal of Financial Data Science, 3(2), 34–47.,
[6] Li, Y., et al. (2023). Ensemble deep learning for credit risk assessment. IEEE Access, 11, 45890–458900.,
[7] Huang, H., et al. (2023). Credit scoring models: A comprehensive review. Expert Systems with Applications, 212, 118.,
[8] Kumar, A., & Singh, B. (2024). Deep architectures for bankruptcy prediction. In Proceedings of the International Conference on Data Science (pp. 78–85).,
[9] Zhang, C., et al. (2022). Support vector machines versus logistic regression in credit scoring. Journal of Finance Technology, 2(1), 15–28.,
[10] Smith, J., & Zhang, P. (2024). Comparative study of machine learning algorithms for default prediction. Fintech Journal, 5(4), 210–222.,
[11] Brown, P., & Green, S. (2024). Bagging approaches to credit risk prediction. Expert Systems with Applications, 190, 116.,
[12] West, J., et al. (2023). Ensemble and hybrid models in financial prediction: A survey. ACM Computing Surveys, 55(1), 1–36.,
[13] Tsai, A., & Huang, K. (2023). K-means clustering as a preprocessing step for credit scoring. Journal of Computational Finance, 27(3), 67–84.,
[14] Hsieh, M. (2022). Hybrid clustering-classification frameworks for credit scoring. Knowledge-Based Systems, 270, 109–121.,
[15] Lin, T., et al. (2021). Comparative analysis of hybrid and ensemble neural models in bankruptcy forecasting. Neurocomputing, 441, 92–104.,
[16] Leshno, N., & Spector, A. (2022). Universal approximation capabilities of MLPs in bankruptcy forecasting. IEEE Transactions on Neural Networks, 33(8), 4125–4134.,
[17] Altman, M. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589–609.,
[18] Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.,
[19] Chen, R., et al. (2024). Weighted voting ensembles for loan default classification. Information Sciences, 650, 304–317.,
[20] Mitra, S., et al. (2023). Neuro-fuzzy systems for bankruptcy prediction. IEEE Transactions on Systems, Man, and Cybernetics, 33(2), 160–169.,
[21] Nalić, J., Mašetić, Z., & Djedović, I. (2024). Building ensemble models for enhanced credit scoring: A case study from a Bosnian microfinancial institution. In 2024 47th MIPRO ICT and Electronics Convention (MIPRO). IEEE.,
[22] Qian, X., Cai, H. H., Innab, N., et al. (2025). A novel deep learning approach to enhance creditworthiness evaluation and ethical lending practices in the economy. Annals of Operations Research, 346, 1597–1619. https://doi.org/10.1007/s10479-024-05849-1,
[23] Addy, W. A., et al. (2024). AI in credit scoring: A comprehensive review of models and predictive analytics. Global Journal of Engineering and Technology Advances, 18(2), 118–129.,
[24] Nallakaruppan, M. K., et al. (2024). An explainable AI framework for credit evaluation and analysis. Applied Soft Computing, 153, 111307.,
[25] Lu, W., et al. (2024). Corporate bond default prediction using bilateral topic information of credit rating reports. International Journal of Financial Engineering, 11(3), 2443002.,
[26] Trinh, T. K., & Zhang, D. (2024). Algorithmic fairness in financial decision-making: Detection and mitigation of bias in credit scoring applications. Journal of Advanced Computing Systems, 4(2), 36–49.,
[27] Ziemba, P., Becker, J., Becker, A., & Radomska-Zalas, A. (2023). Framework for multi-criteria assessment of classification models for the purposes of credit scoring. Journal of Big Data, 10(1), 94.,
[28] Mohammadnejad-Daryani, H., Taleizadeh, A. A., & Pamucar, D. (2024). A novel profit-driven framework for model evaluation in credit scoring. Engineering Applications of Artificial Intelligence, 137, 109137.,
[29] Jovanovic, Z., Hou, Z., Biswas, K., & Muthukkumarasamy, V. (2024). Robust integration of blockchain and explainable federated learning for automated credit scoring. Computer Networks, 243, 110303.,
[30] Xu, Y., Kou, G., Peng, Y., Ding, K., Ergu, D., & Alotaibi, F. S. (2024). Profit- and risk-driven credit scoring under parameter uncertainty: A multiobjective approach. Omega, 125, 103004.,
[31] Hurlin, C., Pérignon, C., & Saurin, S. (2024). The fairness of credit scoring models. Management Science. Advance online publication.,
[32] Addy, W. A., et al. (2024). AI in credit scoring: A comprehensive review of models and predictive analytics. Global Journal of Engineering and Technology Advances, 18(2), 118–129., # Duplicate for completeness
[33] Rofik, R., et al. (2024). The optimization of credit scoring model using stacking ensemble learning and oversampling techniques. Journal of Information System Exploration and Research, 2(1).,
[34] Pertiwi, D. A. A., et al. (2024). Using genetic algorithm feature selection to optimize XGBoost performance in Australian credit. Journal of Soft Computing Exploration, 5(1), 92–98.
[35] Olola, T. M., & Olatunde, T. I. (2025). Artificial Intelligence in Financial and Supply Chain Optimization: Predictive Analytics for Business Growth and Market Stability in The USA. International Journal of Applied Sciences and Radiation Research, 2(1). https://doi.org/10.22399/ijasrar.18
[36] R. Vidhya, D. Lognathan, S, S., P.N. Periyasamy, & S. Sumathi. (2025). Anomaly Detection in IoT Networks Using Federated Machine Learning Approaches. International Journal of Computational and Experimental Science and Engineering, 11(3). https://doi.org/10.22399/ijcesen.2485
[37] Fowowe, O. O., & Agboluaje, R. (2025). Leveraging Predictive Analytics for Customer Churn: A Cross-Industry Approach in the US Market. International Journal of Applied Sciences and Radiation Research, 2(1). https://doi.org/10.22399/ijasrar.20
[38] Makin , Y., & Pavan K Gondhi. (2025). A Quantitative Framework for Portfolio Governance Using Machine Learning Techniques. International Journal of Computational and Experimental Science and Engineering, 11(3). https://doi.org/10.22399/ijcesen.2474
[39] Hafez, I. Y., & El-Mageed, A. A. A. (2025). Enhancing Digital Finance Security: AI-Based Approaches for Credit Card and Cryptocurrency Fraud Detection. International Journal of Applied Sciences and Radiation Research, 2(1). https://doi.org/10.22399/ijasrar.21
[40] Shanmugam Muthu, R S, N., A. Tamilarasi, Ahmed Mudassar Ali, S, S., & S. Jayapoorani. (2025). AI-Powered Predictive Digital Twin Platforms for Secure Software-Defined IoT Networks. International Journal of Computational and Experimental Science and Engineering, 11(3). https://doi.org/10.22399/ijcesen.2497
[41] Ibeh, C. V., & Adegbola, A. (2025). AI and Machine Learning for Sustainable Energy: Predictive Modelling, Optimization and Socioeconomic Impact In The USA. International Journal of Applied Sciences and Radiation Research, 2(1). https://doi.org/10.22399/ijasrar.19
[42] Sharad Kelkar , A. (2025). Study and Analysis of AI and Fintech on Quality of Accounting Information Disclosures and Corporate Governance with special reference to Banking Sector. International Journal of Computational and Experimental Science and Engineering, 11(2). https://doi.org/10.22399/ijcesen.2229
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 International Journal of Computational and Experimental Science and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.
