Modern Research in Decision Making

Bank Client Credit Scoring along with Facility Parameters Optimization using the Simulation-Optimization Model

Document Type : Original Article

Authors

Amir Khorrami 1
mahmoud Dehghan Nayeri 2
Ali Rajabzade 3

1 PhD student in Industrial Management, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran

2 Associate Professor, Department of Industrial Management, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran

3 Professor, Department of Industrial Management, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran.

Abstract
This article presents a novel simulation-optimization framework for credit scoring and optimal bank facility parameter determination. The method comprises three stages:
1. Data Preparation: Collecting financial statements and facility data from 1,000 Iranian corporate clients (2017-2021), with 11 critical features selected from 30 variables using the Minimum Redundancy Maximum Relevance (MRMR) algorithm.
2. Credit Scoring: Five classification models—Logistic Regression (LR), Support Vector Machine (SVM), Artificial Neural Networks (ANN), Extreme Gradient Boosting (XGB), and Random Forest (RF)—are evaluated via accuracy, F1-score, and AUC. Random Forest (RF) outperforms others (accuracy: 89.2%, AUC: 0.93).
3. Simulation-Optimization: A Memetic Algorithm (MA) optimizes three variables—facility amount, interest rate, and repayment period—across four scenarios. The MA integrates a pre-trained RF model to estimate default probabilities dynamically.
Key outcomes:
• Adjusting parameters (34% lower facility amounts, 25% reduced interest rates, 40% longer repayment terms) cuts default rates from 38% to 20%.
• The approach enhances bank profitability by 19% through risk-adjusted loan pricing.
This methodology bridges AI-driven credit assessment with metaheuristic optimization, offering a scalable solution for credit risk mitigation in emerging markets. By enabling real-time adaptation to customer risk profiles, it empowers banks to balance profitability and risk exposure effectively.

Keywords

credit risk
credit scoring
classification
Memetic Algorithm
simulation-optimization model
[1]    Soui, M., Gasmi, I., Smiti, S., & Ghédira, K. (2019). Rule-based credit risk assessment model using multi-objective evolutionary algorithms. Expert systems with applications, 126, 144-157.
[2]    Minaei, B., Afshar, A., & Houshdar Mahjoub, R. (2014). Customer credit clustering for Present appropriate facilities. Management Research in Iran, 7(4), 1-24.
[3]    Safari, S., Ebrahimi, M., & Sheik, M. J. (2011). Managing the Credit Risk of the Bank's Clients in Commercial Banks DEA Approach (Credit Rating). Management Research in Iran, 14(4), 137-164.
[4]    Ahmed, M. I., & Rajaleximi, P. R. (2019). An empirical study on credit scoring and credit scorecard for financial institutions. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 8(7), 2278-1323.
[5]    Bluhm, C., Overbeck, L., & Wagner, C. (2016). Introduction to credit risk modeling. Crc Press.
[6]    Ghasemnia Arabi, N., & Safaei Ghadikolaei, A. (2016). A New Approach of applying multi criteria decision making models for classifying the credit customers of bank. Modern Research in Decision Making, 1(3), 43-64.
[7]    Sohrabi, B., Raeesi Vanani, I., & Zareh Mirkabad, F. (2016). Designing a Recommender System for Optimizing and Managing Bank Facilities through the Utilization of Clustering and Classification Algorithms. Modern Research in Decision Making, 1(2), 53-76.
[8]    Abdou, H. A., & Pointon, J. (2011). Credit scoring, statistical techniques and evaluation criteria: a review of the literature. Intelligent systems in accounting, finance and management, 18(2-3), 59-88.
[9]    Koutanaei, F. N., Sajedi, H., & Khanbabaei, M. (2015). A hybrid data mining model of feature selection algorithms and ensemble learning classifiers for credit scoring. Journal of Retailing and Consumer Services, 27, 11-23.
[10]    Dastile, X., Celik, T., & Potsane, M. (2020). Statistical and machine learning models in credit scoring: A systematic literature survey. Applied Soft Computing, 91, 106263.
[11]    Markov, A., Seleznyova, Z., & Lapshin, V. (2022). Credit scoring methods: Latest trends and points to consider. The Journal of Finance and Data Science.
[12]    Dželihodžić, A., Đonko, D., & Kevrić, J. (2018). Improved credit scoring model based on bagging neural network. International Journal of Information Technology & Decision Making, 17(06), 1725-1741.
[13]    Nehrebecka, N. (2018). Predicting the default risk of companies. Comparison of credit scoring models: LOGIT vs Support Vector Machines. Econometrics. Ekonometria. Advances in Applied Data Analytics, 22(2), 54-73.
[14]    Dželihodžić, A., Đonko, D., & Kevrić, J. (2018). Improved credit scoring model based on bagging neural network. International Journal of Information Technology & Decision Making, 17(06), 1725-1741.
[15]    Moradi, S., & Mokhatab Rafiei, F. (2019). A dynamic credit risk assessment model with data mining techniques: evidence from Iranian banks. Financial Innovation, 5(1), 1-27.
[16]    Munkhdalai, L., Munkhdalai, T., Namsrai, O. E., Lee, J. Y., & Ryu, K. H. (2019). An empirical comparison of machine-learning methods on bank client credit assessments. Sustainability, 11(3), 699.
[17]    Nalić, J., & Martinovic, G. (2020). Building a credit scoring model based on data mining approaches. International Journal of Software Engineering and Knowledge Engineering, 30(02), 147-169.
[18]    Boughaci, D., & Alkhawaldeh, A. A. (2020). Appropriate machine learning techniques for credit scoring and bankruptcy prediction in banking and finance: A comparative study. Risk and Decision Analysis, 8(1-2), 15-24.
[19]    Pławiak, P., Abdar, M., Pławiak, J., Makarenkov, V., & Acharya, U. R. (2020). DGHNL: A new deep genetic hierarchical network of learners for prediction of credit scoring. Information Sciences, 516, 401-418.
[20]    Ampountolas, A., Nyarko Nde, T., & Constantinescu, C. (2021). A machine learning approach for micro-credit scoring. Risks, 9(3), 50.
[21]    Biecek, P., Chlebus, M., Gajda, J., Gosiewska, A., Kozak, A., Ogonowski, D., ... & Wojewnik, P. (2021). Enabling machine learning algorithms for credit scoring--explainable artificial intelligence (XAI) methods for clear understanding complex predictive models. arXiv preprint arXiv:2104.06735.
[22]    Laborda, J., & Ryoo, S. (2021). Feature selection in a credit scoring model. Mathematics, 9(7), 746.
[23]    Tripathi, D., Edla, D. R., Bablani, A., Shukla, A. K., & Reddy, B. R. (2021). Experimental analysis of machine learning methods for credit score classification. Progress in Artificial Intelligence, 10, 217-243.
[24]    Hussin Adam Khatir, A. A., & Bee, M. (2022). Machine learning models and data-balancing techniques for credit scoring: What is the best combination?. Risks, 10(9), 169.
[25]    Lenka, S. R., Bisoy, S. K., Priyadarshini, R., & Sain, M. (2022). Empirical analysis of ensemble learning for imbalanced credit scoring datasets: A systematic review. Wireless Communications and Mobile Computing, 2022.
[26]    Sum, R. M., Ismail, W., Abdullah, Z. H., & Shah, N. F. M. N. (2022). A New Efficient Credit Scoring Model For Personal Loan Using Data Mining Technique For Sustainability Management. Journal of Sustainability Science and Management, 12(2), 135467.
[27]    Zhou, M. (2022). Credit Risk Assessment Modeling Method Based on Fuzzy Integral and SVM. Mobile Information Systems, 2022.
[28]    El Qadi, A., Trocan, M., Diaz-Rodriguez, N., & Frossard, T. (2023). Feature contribution alignment with expert knowledge for artificial intelligence credit scoring. Signal, Image and Video Processing, 17(2), 427-434.
[29]    Abdoli, M., Akbari, M., & Shahrabi, J. (2023). Bagging Supervised Autoencoder Classifier for credit scoring. Expert Systems with Applications, 213, 118991.
[30]    Runchi, Z., Liguo, X., & Qin, W. (2023). An ensemble credit scoring model based on logistic regression with heterogeneous balancing and weighting effects. Expert Systems with Applications, 212, 118732.
[31]    Aljadani, A., Alharthi, B., Farsi, M. A., Balaha, H. M., Badawy, M., & Elhosseini, M. A. (2023). Mathematical Modeling and Analysis of Credit Scoring Using the LIME Explainer: A Comprehensive Approach. Mathematics, 11(19), 4055.
[32]    Chen, Y., Calabrese, R., & Martin-Barragan, B. (2024). Interpretable machine learning for imbalanced credit scoring datasets. European Journal of Operational Research, 312(1), 357-372.
[33]    Mushava, J., & Murray, M. (2024). Flexible loss functions for binary classification in gradient-boosted decision trees: An application to credit scoring. Expert Systems with Applications, 238, 121876.
[34]    Rofik, R., Aulia, R., Musaadah, K., Ardyani, S. S. F., & Hakim, A. A. (2024). The Optimization of Credit Scoring Model Using Stacking Ensemble Learning and Oversampling Techniques. Journal of Information System Exploration and Research, 2(1), 11-20.
[35]    Xiao, J., Zhong, Y., Jia, Y., Wang, Y., Li, R., Jiang, X., & Wang, S. (2024). A novel deep ensemble model for imbalanced credit scoring in internet finance. International Journal of Forecasting, 40(1), 348-372.
[36]    Krishna, S. J. S., Aarif, M., Bhasin, N. K., Kadyan, S., & Bala, B. K. (2024, July). Predictive Analytics in Credit Scoring: Integrating XG Boost and Neural Networks for Enhanced Financial Decision Making. In 2024 International Conference on Data Science and Network Security (ICDSNS) (pp. 1-6). IEEE.
[37]    Talaat, F. M., Aljadani, A., Badawy, M., & Elhosseini, M. (2024). Toward interpretable credit scoring: integrating explainable artificial intelligence with deep learning for credit card default prediction. Neural Computing and Applications, 36(9), 4847-4865.
[38]    Groppelli, A. A., & Nikbakht, E. (2000). Finance, Barron's Educational Series. Inc., ISBN, 764112757.
[39]    Yang, F., Qiao, Y., Huang, C., Wang, S., & Wang, X. (2021). An automatic credit scoring strategy (ACSS) using memetic evolutionary algorithm and neural architecture search. Applied Soft Computing, 113, 107871.
[40]    Acuna, E., & Rodriguez, C. (2004). A meta analysis study of outlier detection methods in classification. Technical paper, Department of Mathematics, University of Puerto Rico at Mayaguez, 1, 25.
[41]    Dawson, R. (2011). How significant is a boxplot outlier?. Journal of Statistics Education, 19(2).
[42]    Deng, X., Li, M., Deng, S., & Wang, L. (2022). Hybrid gene selection approach using XGBoost and multi-objective genetic algorithm for cancer classification. Medical & Biological Engineering & Computing, 60(3), 663-681.
[43]    Xie, S., Zhang, Y., Lv, D., Chen, X., Lu, J., & Liu, J. (2023). A new improved maximal relevance and minimal redundancy method based on feature subset. The Journal of Supercomputing, 79(3), 3157-3180.
[44]    Louzada, F., Ara, A., & Fernandes, G. B. (2016). Classification methods applied to credit scoring: Systematic review and overall comparison. Surveys in Operations Research and Management Science, 21(2), 117-134.
[45]    Sörensen, K., & Sevaux, M. (2006). MA| PM: memetic algorithms with population management. Computers & Operations Research, 33(5), 1214-1225.
[46]    Moscato, P. (1989). On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Caltech concurrent computation program, C3P Report, 826(1989), 37.
[47]    Neri, F., & Cotta, C. (2012). Memetic algorithms and memetic computing optimization: A literature review. Swarm and Evolutionary Computation, 2, 1-14.
[48]    Kumar, R., & Memoria, M. (2020). A review of memetic algorithm and its application in traveling salesman problem. Int. J. Emerg. Technol., 11(2), 1110-1115.
[49]    Holland, J. H. (1973). Genetic algorithms and the optimal allocation of
Modern Research in Decision Making
Volume 10, Issue 1
Spring 2025
Pages 31-65

Home

Guide for Authors

Submit Manuscript

Reviewers

Contact Us