دانشگاه سمنان

 آمار

تعداد نشریات21
تعداد شماره‌ها631
تعداد مقالات9,294
تعداد مشاهده مقاله67,842,587
تعداد دریافت فایل اصل مقاله15,213,433
Masomian, Bahman, Mirhosseini, Seyed Mohsen. (1404). A model for validating bank customers using multilayer perceptron neural network and imperialist competitive algorithm (ICA). نشریه هنرهای کاربردی, (), -. doi: 10.22075/ijnaa.2023.31084.4561
Bahman Masomian; Seyed Mohsen Mirhosseini. "A model for validating bank customers using multilayer perceptron neural network and imperialist competitive algorithm (ICA)". نشریه هنرهای کاربردی, , , 1404, -. doi: 10.22075/ijnaa.2023.31084.4561
Masomian, Bahman, Mirhosseini, Seyed Mohsen. (1404). 'A model for validating bank customers using multilayer perceptron neural network and imperialist competitive algorithm (ICA)', نشریه هنرهای کاربردی, (), pp. -. doi: 10.22075/ijnaa.2023.31084.4561
Masomian, Bahman, Mirhosseini, Seyed Mohsen. A model for validating bank customers using multilayer perceptron neural network and imperialist competitive algorithm (ICA). نشریه هنرهای کاربردی, 1404; (): -. doi: 10.22075/ijnaa.2023.31084.4561

A model for validating bank customers using multilayer perceptron neural network and imperialist competitive algorithm (ICA)

International Journal of Nonlinear Analysis and Applications
مقالات آماده انتشار، اصلاح شده برای چاپ، انتشار آنلاین از تاریخ 26 خرداد 1404    اصل مقاله (722.12 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

[1] M. Ala’raj and M. Abbod, Classifiers consensus system approach for credit scoring, Knowledge-Based Syst. 104 (2016), 89–105.

[2] E. Atashpaz-Gargari and C. Lucas, Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition, IEEE Cong. Evolut. Comput., 2007, pp. 4661–4667.

[3] J. Bessis, Risk Management in Banking, John Wiley & Sons, 2011.

[4] M. Faheem, AI-Driven risk assessment models: Revolutionizing credit scoring and default prediction, Iconic Res. Engin. J. 5 (2021), 177–186.

[5] T. Lee, C. Chiu, C. Lu, and I. Chen, Credit scoring using the hybrid neural discriminant technique, Expert Syst. Appl. 23 (2002), 245–254.

[6] C. Luo, D. Wu, and D. Wu, A deep learning approach for credit scoring using credit default swaps, Engin. Appl. Artific. Intell. 65 (2017), 465–470.

[7] R. Merton, On the pricing of corporate debt: The risk structure of interest rates, J. Finance 29 (1974), 449–470.

[8] G. Morton, Managing Bank Risk: An Introduction to Broad Base Credit Engineering, Academic Press, Amsterdam, 2003.

[9] V. Neagoe, A. Ciotec, and G. Cucu, Deep convolutional neural networks versus multilayer perceptron for financial prediction, Int. Conf. Commun., 2018, pp. 201–206.

[10] M. Peihani, Basel committee on banking supervision: A primer on governance, history, and legitimacy—Part i, Brill Rese. Perspect. Int. Bank. Secur. Law 1 (2016), no. 1, 1–87.

[11] M. Soui, I. Gasmi, S. Smiti, and K. Ghedira, Rule-based credit risk assessment model using multi-objective evolu[1]tionary algorithms, Expert Syst. Appl. 126 (2019), 144–157.

[12] K. Tran, T. Duong, and Q. Ho, Credit scoring model: A combination of genetic programming and deep learning, Future Technol. Conf., 2016, pp. 145–149.

[13] H. Van Greuning and S. Bratanovic, Analyzing Banking Risk: A Framework for Assessing Corporate Governance and Risk Management, World Bank Publications, 2020.

[14] M. Vazan, Machine Learning and Data Science: Foundations, Concepts, Algorithms, and Tools, Miaad Andisheh Pub., 2021. [In Persian]

[15] B. Zhu, W. Yang, H. Wang, and Y. Yuan, A hybrid deep learning model for consumer credit scoring, Int. Conf. Artific. Intell. Big Data, 2018, pp. 205–208.

آمار
تعداد مشاهده مقاله: 7


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب