Investigating and Evaluating Credit Risk in Banks Using Support Vector Machines with Genetic Algorithms

Document Type : Original Article

Authors
razieh akhondzardaini 1
Hamideh Shekari
khalil kalavani 2
1 PhD candidate healthcare Management, Student Research committee, Faculty of Management and Medical Information Sciences, Kerman University of Medical Sciences, Kerman, Iran
2 modirtPhD candidate healthcare Management, Student Research committee, Faculty of Management and Medical Information Sciences, Kerman University of Medical Sciences, Kerman, Iranbzmed@gmail.com
10.22034/jirss.2025.2009127.1031
Abstract
The prediction of credit risk is of great economic importance for banks and financial institutions, leading to the utilization of various methods in developing predictive models. This study introduces a credit risk prediction model that combines the support vector machine (SVM) with a genetic algorithm (GA) to aid credit decision-making by managers. While SVM is a reliable classification method, its performance can be influenced by factors such as model shape, parameter setting, and feature selection. To address these challenges, a novel approach is proposed that employs GA to optimize feature selection and parameter settings within the SVM framework.The proposed model is compared against alternative models including neural network, logistic regression, random forest, and decision tree. The study utilizes data from Bank of Yazd Province, with a sample size of 1876 customers divided into two groups: those who defaulted on their credit obligations and those who fulfilled them. The results demonstrate that the GA-SVM model serves as a suitable alternative for credit risk prediction, outperforming other models in terms of predictive power. Furthermore, the proposed model offers the benefit of feature selection, enabling financial institutions to identify potential risks and implement preventive measures. The use of GA in conjunction with SVM also facilitates the identification of optimal SVM parameter values, thereby enhancing the overall performance of the model. In conclusion, the proposed GA-SVM model emerges as a valuable tool for credit decision-making and risk management within banks and financial institutions. Further optimization can be achieved by exploring other meta-heuristic optimization algorithms.
Keywords
risk
support vector machine
algorithm
modeling
Subjects
Aziz A, Dar HA. Predicting corporate bankruptcy: where we stand? Corporate Governance: The International Journal of Business in Society. 2006;6:18–33.
Bai Y, Zha D. Commercial Bank Credit Grading Model Using Genetic Optimization Neural Network and Cluster Analysis. Computational Intelligence and Neuroscience. 2022;.
Banegas-Luna AJ, Peña-García J, Iftene A, Guadagni F, Ferroni P, Scarpato N, et al. Towards the interpretability of machine learning predictions for medical applications targeting personalised therapies: A cancer case survey. International Journal of Molecular Sciences. 2021;22:4394.
Bao Y, Deng Z, Wang Y, Kim H, Armengol VD, Acevedo F, et al. Using machine learning and natural language processing to review and classify the medical literature on cancer susceptibility genes. JCO Clinical Cancer Informatics. 2019;1:1–9.
Bozorg-Haddad O, Aboutalebi M, Ashofteh PS, Loáiciga HA. Real-time reservoir operation using data mining techniques. Environmental Monitoring and Assessment. 2018;190:1–22.
Chen L, Zhang H. Margin error bounds for support vector machines on reproducing kernel Banach spaces. Neural Computation. 2017;29:3078–3093.
Chui KT, Lytras MD, Liu RW. A generic design of driver drowsiness and stress recognition using MOGA optimized deep MKL-SVM. Sensors. 2020;20:1474.
Dam DL. Evaluation of credit risk management policies and practices in a Vietnamese joint-stock commercial bank’s transaction office. PhD thesis; 2010.
Emary E, Zawbaa HM, Grosan C. Experienced gray wolf optimization through reinforcement learning and neural networks. IEEE Transactions on Neural Networks and Learning Systems. 2017;29:681–694.
Fan Y, Zhang Y, Hua Y, Wang Y, Zhu L, Zhao J, et al. Investigation of machine intelligence in compound cell activity classification. Molecular Pharmaceutics. 2019;16:4472–4484.
Fernández-Lozano C, Canto C, Gestal M, Andrade-Garda JM, Rabuñal JR, Dorado J, et al. Hybrid model based on genetic algorithms and SVM applied to variable selection within fruit juice classification. The Scientific World Journal. 2013;2013.
Jiang S, Chin KS, Tsui KL. A universal deep learning approach for modeling the flow of patients under different severities. Computer Methods and Programs in Biomedicine. 2018;154:191–203.
Jozsa F, Baker R, Kelly P, Ahmed M, Douek M. The Use of Machine Learning to Reduce Overtreatment of the Axilla in Breast Cancer: Retrospective Cohort Study. JMIR Perioperative Medicine. 2022;5:e34600.
Karaa A, Krichene A. Credit-risk assessment using support vectors machine and multilayer neural network models: a comparative study case of a tunisian bank. Accounting and Management Information Systems. 2012;11:587.
Karthikeyan R, Alli P. Feature selection and parameters optimization of support vector machines based on hybrid glowworm swarm optimization for classification of diabetic retinopathy. Journal of Medical Systems. 2018;42:1–11.
Kumar R, Sharma A, Siddiqui MH, Tiwari RK. Prediction of human intestinal absorption of compounds using artificial intelligence techniques. Current Drug Discovery Technologies. 2017;14:244–254.
Kundu T, Garg H. INNA: An improved neural network algorithm for solving reliability optimization problems. Neural Computing and Applications. 2022;34:20865–20898.
Li X, Wang Y, Ruiz R. A survey on sparse learning models for feature selection. IEEE Transactions on Cybernetics. 2020;52:1642–1660.
Li Y, Zhang T. Deep neural mapping support vector machines. Neural Networks. 2017;93:185–194.
Liao Q, Zhou S, Shi H, Shi W. Parameter estimation of nonlinear systems by dynamic cuckoo search. Neural Computation. 2017;29:1103–1123.
Liu G, Zhou D, Xu H, Mei C. Model optimization of SVM for a fermentation soft sensor. Expert Systems with Applications. 2010;37:2708–2713.
Manurung J, Mawengkang H, Zamzami EM. Optimizing Support Vector Machine Parameters with Genetic Algorithm for Credit Risk Assessment. In: IOP Conference Series: Materials Science and Engineering; 2017. p. 012026.
Mei S, Montanari A, Nguyen PM. A mean field view of the landscape of two-layer neural networks. Proceedings of the National Academy of Sciences. 2018;115:E7665–E7671.
Metawa N, Hassan MK, Elhoseny M. Genetic algorithm based model for optimizing bank lending decisions. Expert Systems with Applications. 2017;80:75–82.
Nguyen H, Choi Y, Bui XN, Nguyen-Thoi T. Predicting blast-induced ground vibration in open-pit mines using vibration sensors and support vector regression-based optimization algorithms. Sensors. 2019;20:132.
Sanz H, Valim C, Vegas E, Oller JM, Reverter F. SVM-RFE: selection and visualization of the most relevant features through non-linear kernels. BMC Bioinformatics. 2018;19:1–18.
Segera D, Mbuthia M, Nyete A. Particle swarm optimized hybrid kernel-based multiclass support vector machine for microarray cancer data analysis. BioMed Research International. 2019;2019.
Shin KS, Lee TS, Kim HJ. An application of support vector machines in bankruptcy prediction model. Expert Systems with Applications. 2005;28:127–135.
Si L, Wang Z, Liu X, Tan C, Liu Z, Xu J. Identification of shearer cutting patterns using vibration signals based on a least squares support vector machine with an improved fruit fly optimization algorithm. Sensors. 2016;16:90.
Silva SR, do Marcela J, Neto B. Improving the ranking quality of medical image retrieval using a genetic feature selection method. Decision Support Systems. 2011;51:810–820.
Tsai CF, Hsiao YC. Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches. Decision Support Systems. 2010;50:258–269.
Tuan NV, Minh DQ, Khoa NX, Lim O. A Study to Predict Ignition Delay of an Engine Using Diesel and Biodiesel Fuel Based on the ANN and SVM Machine Learning Methods. ACS Omega. 2023;8:9995–10005.
Valls Martínez MdC, Cruz Rambaud S, Parra Oller IM. Sustainable and conventional banking in Europe. PLOS ONE. 2020;15:e0229420.
Van Belle V, Van Calster B, Van Huffel S, Suykens JA, Lisboa P. Explaining support vector machines: a color based nomogram. PLOS ONE. 2016;11:e0164568.
Xiao Y, Wang H, Xu W. Parameter selection of Gaussian kernel for one-class SVM. IEEE Transactions on Cybernetics. 2014;45:941–953.
Yemmanuru PK, Yeboah J, Nti IK. Customer Credit Risk: Application and Evaluation of Machine Learning and Deep Learning Models. In: Proceedings of the IEEE International Conference on Computational Intelligence and Communication Technology; 2024.
Journal of the Iranian Statistical Society
Volume 24, Issue 2
December 2025
Pages 125-138

  • Receive Date 13 August 2023
  • Revise Date 02 December 2025
  • Accept Date 26 December 2025