|تعداد مشاهده مقاله||22,864,391|
|تعداد دریافت فایل اصل مقاله||21,108,539|
Designing a proper model and software program to evaluate and predict credit risk of small and medium‑sized enterprises in commercial banks
|International Journal of Finance & Managerial Accounting|
|دوره 8، شماره 31، آذر 2023، صفحه 1-12 اصل مقاله (755.5 K)|
|نوع مقاله: Original Article|
|شناسه دیجیتال (DOI): 10.30495/ijfma.2023.21713|
|kokab sharifi1؛ Fereydon Rahnamay Roodposhti 2؛ Amir Mohammadzadeh3؛ Hashem nikoumaram 4؛ Naser Hamidi5|
|1Ph.D. Student, Department of Financial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran|
|2Professor of financial management, Department of Financial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran|
|3Assistant Professor of financial management, ,Department of Financial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran|
|4Professor of financial management, Department of Business Management, Science and Research Branch, Islamic Azad University, Tehran, Iran|
|5Associate Professor of Industrial management, Department of Industrial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran|
|Globalization and, consequently, intensifying the competition between banks and financial institutions in domestic and foreign financial markets increase significantly the importance and requirement of strengthening and modifying systems in financial enterprises. Banks are no exception. Credit risk assessment is one of the most significant components of the granted facilities process. Small and medium enterprises are the majority of customers of commercial banks; hence, it is possible that designing a credit risk system considerably helps banks to manage credit risk.|
This study aims to introduce a new approach to predict and assess the credit risk of small and medium-sized enterprises. To this end, we identified the indices effective on the credit risk of medium and small-sized enterprises and determined significant indices by selecting the feature. We selected 98 cases of medium and small-sized legal clients in the industrial sector for research data from one of the commercial banks during the years 2018-2020. We then implemented the logit regression models, artificial neural network, and hybrid model (fuzzy expert system, logit, and artificial neural network) in order to predict and assess customers' credit risk and also calculated the accuracy of the models. Ultimately, we have designed the program applying visual studio software. We calculated the customer using logit regression models, artificial neural network, and hybrid model according to the designed program by inserting each customer's information and we also determined credit rating and type of collateral of each customer based on customer risk.
|Credit risk؛ Logit regression؛ Artificial neural network؛ Fuzzy expert system؛ Small and medium-sized enterprises|
Abdou, H., El-Masry, A., & Pointon, J. (2007). On the applicability of credit scoring models in Egyptian banks. Banks and Bank Systems, 2(1), 4.
2) Abollhasani,A., Hassani Moghaddam.R, (2008), Study of types of risks and its management methods in the interest-free banking system of Iran. Islamic Economics Research Quarterly, Year 8, No. 90.
10 / Designing a proper model and software program to evaluate and predict credit risk of …
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3) Angelini, E., di Tollo, G., and Roli, A. (2008). A neural network approach for credit risk evaluation. The Quarterly Review of Economics and Finance, 48(4), 733-755.
4) Bridges,S., Disney.R.(2001). Modeling consumer credit and default. The Research Agenda.
5) Crook, J. N., Edelman, D. B., & Thomas, L. C. (2007). Recent developments in consumer credit risk assessment. European Journal of Operational Research, 183(3), 1447-1465.
6) Ebrahimi, M., Daryaber, A. (2012). Credit Risk Management in the Banking System - Data Envelopment Analysis Approach and Logistic and Neural Network Regression, Investment Knowledge Quarterly, First Year, Second Issue.
7) Fallah Shams, M.F., (2008), Credit Risk Measurement Models in Banks and Credit Institutions, New Journal of Economics, No. 109, pp. 22-28.
8) Hamadani, A. Z., Shalbafzadeh, A., Rezvan, T., & Moghadam, A. (2013). An integrated genetic-based model of naive Bayes networks for credit scoring. International Journal of Artificial Intelligence & Applications, 4(1), 85.
9) Huang, C. L., Chen, M. C., & Wang, C. J. (2007). Credit scoring with a data mining approach based on support vector machines. Expert systems with applications, 33(4), 847-856.
10) Kang Li, Jyrki Niskanen, Mikko Kolehmainen, Mervi Niskanen(2016). Financial Innovation: Credit Default Hybrid Model for SME Lending, Expert Systems with Applications
11) Greuning,V.H., Bratanovic,S. (2003). Analysing and managing banking risk: A framework for assessing corporate and financial risk,The World Bank, Washington D.C.
12) Khashman, A. (2011). Credit risk evaluation using neural networks: Emotional versus conventional models. Applied Soft Computing, 11(8), 5477-5484.
13) Lin, S. L. (2009). A new two-stage hybrid approach of credit risk in banking industry. Expert Systems with Applications, 36(4), 8333-8341.
14) Mohammadian Haji Kurd, A., Asgharzadeh Zafarani, M., Imam Doust, M. (2016). Investigation of Credit Risk of Legal Clients Using Supporting Machine Vector Model and Hybrid Model of Genetic Algorithm Case Study of Tejarat
Bank, Journal of Financial Engineering and Securities Management, No. 27.
15) Mohammadi, T., Johari, H. (1398). Designing and compiling a credit risk model in the banking system of the country using multilevel models, Quarterly Journal of Financial Knowledge, Securities Analysis, Twelfth Year, No. 41.
16) Mirghfouri, S. H., Amin Ashouri, Z. (1394). Credit Risk Assessment of Bank Customers, Two Quarterly Journal of Business Management Research, Year 7, No. 13, pp. 166-147.
17) Myers, J. H., and Forgy, E. W. (1963). The development of numerical credit evaluation systems. Journal of the American Statistical Association, 58(303), 799-806.
18) Oreski, S., Oreski, D., & Oreski, G. (2012). Hybrid system with genetic algorithm and artificial neural networks and its application to retail credit risk assessment. Expertsystems with applications, 39(16), 12605-12617
19) Orgler, Y. E. (1971). Evaluation of bank consumer loans with credit scoring models: Tel-Aviv University, Department of Envirnonmental Sciences.
20) Parker.J., Parsaiyan.A. (1378). Risk management, dimensions of risk management, its definition and application in financial organizations. Financial research. Nos 13&14.
21) Rastegar,M.A.,Eidipoush,M.,(1399).Credit risk modeling of bank customers using survival analysis model based on spline method. Journal of Investment Knowledge. Ninth year. No. Thirty-four.
22) Siddiqi, N. (2005). Credit Risk Scorecards: Developing And Implementing Intelligent Credit Scoring.
23) Soydaner, D., & Kocadağlı, O. (2015). Artificial Neural Networks with Gradient Learning Algorithm for Credit Scoring. Istanbul University Journal of the School of Business,44(2), 003-012.
24) Taqvi Fard, M. T., Sadat Hosseini, F., Khan Babaei, M. (2014). Hybrid Credit Ranking Model Using Genetic Algorithms and Fuzzy Expert Systems (Case Study: Ghavamin Financial and Credit Institution), Information Technology Management, Volume 1, Number 1, pp. 31-46.
25) Tehrani, R., Fallah Shams, M. (2005). Designing and Explaining the Credit Risk Model in the Banking System, Journal of Social Sciences and
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