فهرست نشریات علمی دسترسی آزاد ایران

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.