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A model for validating bank customers using multilayer perceptron neural network and imperialist competitive algorithm (ICA) | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 10، دوره 17، شماره 4، تیر 2026، صفحه 113-121 اصل مقاله (721.95 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2023.31084.4561 | ||
| نویسندگان | ||
| Bahman Masomian1؛ Seyed Mohsen Mirhosseini* 2 | ||
| 1Department of Computer Engineering, Hi.C, Islamic Azad University, Hidaj, Iran | ||
| 2Department of Computer Engineering, Ka. C, Islamic Azad University, Karaj, Iran | ||
| تاریخ دریافت: 07 تیر 1402، تاریخ پذیرش: 21 مرداد 1402 | ||
| چکیده | ||
| Given the highly competitive nature of the banking industry, financial and credit institutions continually seek to identify the most reliable and profitable customers. They are particularly concerned about loan defaults or delays in repayment, which can negatively impact economic growth. Credit scoring models are among the most effective tools in modern banking for evaluating customer creditworthiness. These models enable banks to assess credit requests with greater accuracy and lower cost. In recent years, machine learning techniques—especially predictive classifiers-have been extensively applied to credit scoring and customer classification. This study introduces a novel hybrid model that combines a Multilayer Perceptron (MLP) neural network with the Imperialist Competitive Algorithm (ICA). In the proposed approach, ICA is employed to optimise the hyperparameters of the MLP network. The model is tested on a dataset comprising 2,571 real customers from Saderat Bank, categorised into default and non-default classes based on 11 identified features. The results demonstrate that the proposed model achieves higher accuracy and lower prediction error in assessing customers’ credit behaviour. | ||
| کلیدواژهها | ||
| Credit Risk؛ Credit Scoring؛ Probability of Default؛ Multilayer Perceptron Neural Network؛ Imperialist Competitive Algorithm | ||
| مراجع | ||
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