مدلسازی و شناسایی روابط علی بین عوامل اصلی ریسک اعتباری در سیستم بانکی با استفاده از تکنیک تصمیمگیری دیمتل | ||
| سیاست گذاری اقتصادی | ||
| دوره 17، شماره 33، فروردین 1404، صفحه 180-211 اصل مقاله (924.02 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22034/epj.2024.20992.2544 | ||
| نویسندگان | ||
| مهرداد جیحونی پور1؛ سمیه اعظمی* 2؛ سهراب دل انگیزان2 | ||
| 1دانشجوی دکتری اقتصاد دانشگاه رازی، کرمانشاه، ایران | ||
| 2دانشیار گروه اقتصاد دانشگاه رازی، کرمانشاه، ایران | ||
| چکیده | ||
| یکی از پیامدهای فعالیتهای واسطهگری مالی در بانکها، ریسک اعتباری است که قدیمیترین، بزرگترین و در عین حال مهمترین ریسک بانکی محسوب میشود. در این پژوهش به مدلسازی و شناسایی روابط علی بین عوامل اصلی ریسک اعتباری جهت پیشبینی نکول بازپرداخت مشتریان با مراجعه به خبرگان اعتباری و استفاده از روش تصمیمگیری دیمتل پرداخته شده است. ساختاردهی به عوامل پیچیده در قالب گروههای علت و معلولی از مهمترین کارکردهای روش دیمتل در فرایندهای حل مسئله است. بر این اساس تعداد 22 متغیر توصیف-کننده ریسک اعتباری شناسایی و به دو دسته علی و معلول دستهبندی شدهاند. یافتههای پژوهش نشان داده است که حرفه متقاضی دارای بیشترین تاثیرگذاری در مدل است. متغیرهای درآمد سالیانه، مالک بودن محل کار و وضعیت تاهل متقاضی به ترتیب در درجات بعدی تاثیرگذاری قرار دارند. متغیر تعداد وثیقه نیز کمترین تاثیرگذاری را در مدل دارد. مبلغ هر قسط از بیشترین میزان تاثیرپذیری نسبت به سایر متغیرها برخوردار است. همچنین متغیرهای درآمد سالیانه و حرفه متقاضی به ترتیب بیشترین تعامل را با سایر متغیرهای مورد مطالعه دارند. لذا، میباید شاخصهای جمعیتشناختی و اجتماعی- اقتصادی در کنار شاخصهای مالی و اعتباری در مدلهای اعتبارسنجی بیشتر مورد توجه قرار گرفته و سامانههای مختلف و جامع جهت سنجش اعتبار با سرعت بیشتری به یکدیگر لینک شود تا مورد استفاده بانکها و موسسات اعتبارسنجی قرار گیرد. یافتههای پژوهش امکان ارائه یک سیستم پشتیبانی تصمیمگیری موثر برای بانکها به منظور شناسایی و کاهش نسبت تسهیلات-گیرندگان بدحساب و در نتیجه کاهش میزان ریسک اعتباری را فراهم میکند | ||
| کلیدواژهها | ||
| بانک؛ تکنیک دیمتل؛ ریسک اعتباری؛ روابط علی؛ مدلسازی اعتباری | ||
| عنوان مقاله [English] | ||
| Modeling and identification of causal relationships between the main factors of credit risk in the banking system using the Dematel decision making technique | ||
| نویسندگان [English] | ||
| Mehrdad Jeyhoonipour1؛ Somayeh Azami2؛ Sohrab Delangizan2 | ||
| 1Ph.D. student in Economics, University of Razi, Kermanshah, Iran. | ||
| 2Associate Professor of Economics, University of Razi, Kermanshah, Iran. | ||
| چکیده [English] | ||
| Purpose: One of the consequences of financial intermediation activities in banks is credit risk, which is the oldest, largest and, at the same time, the most important banking risk. As the society is growing and developing, the amount of facilities and liquidity circulation in it increases, and the importance of credit health becomes more necessary. Therefore, evaluating and managing credit risk is a vital thing for banks and is also an important solution for implementing banking policies and business strategies. In addition, the existence of an evolving credit risk management framework indicates the financial prosperity of the banking system in general. It is also an important indicator for the stability and financial stability of each bank in particular. Methodology: In this research, the modeling and identification of causal relationships between the main factors of credit risk is done in order to predict the default repayment of customers by referring to credit experts and using the DEMATEL method. Structuring complex factors in the form of cause and effect groups is an important function of DEMATEL method in problem solving processes. Therefore, 22 variables describing credit risk are divided into the two categories of cause and effect. Findings and discussion: The results show that the job status of the applicant has the most influence in the model. The variables of applicant's annual income, workplace ownership and marital status respectively have the next degrees of influence. The number of collaterals has the least influence in the model. The monthly repayment burden has the highest level of influence compared to other variables. Also, the variables of annual income and job status have the most interactions with the other studied variables. Conclusions and policy implications: Demographic and socio-economic indicators, along with financial and credit indicators, should be given more attention in credit bureau models, and different and comprehensive systems to measure credit should be linked to each other more quickly if they are to be used by banks and credit bureau institutions. Therefore, our findings allow providing an effective decision support system for banks in order to detect and reduce the rate of bad borrowers, thus reducing credit risks. | ||
| کلیدواژهها [English] | ||
| Bank, DEMATEL technique, Credit risk, Causal relationships, Credit Modeling | ||
| مراجع | ||
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Abdou, H., Pointon, J., & Masry, EI. A. (2008). Neural Nets versus Conventional Techniques in Credit Scoring in Egyptian Banking. Expert Systems with Applications, 35, 1275-1292. Abid, L., Zaghdene, S., Masmoudi, A., & Ghorbel, S. Z. (2017). Bayesian Network Nod-Eling: A Case Study of Credit Scoring Analysis of Consumer Loans Default Pay-ment. Asian Economic and Financial Review, 7(9), 846. Anastasiou, D., Louri, H., & Tsionas, M. (2019b). Non-Performing Loans in the Uuro-Area: Are Core-Periphery Banking Markets Fragmented?. International Journal of Finance and Economics, 24(1), 97-112. Anginer, D., Demirguc-Kunt, A., & Zhu, M. (2014). How Does Competition Affect Bank Systemic Risk?. Journal of Financial Intermediation, 23, 1-26. Asadi, Z., Yavari, K., & Heydari, H. (2020). The Study of the Effects of Liquidity and Credit Risk on Bank Stability in Iran Using the Z-Score Index. Biquarterly Journal of Economic Research, 12(23), 1-31 (In Persian). Aysan, A. F, & Disli, M. (2019). Small Business Lending and Credit Risk: Granger-Causality Evidence. Journal of Economic Modelling, 83(c), 245-255. Baesens, B., & Smedts, K. (2023). Boosting Credit Risk Models. Journal of the British Accounting Review. Published by Elsevier Ltd. Bakhtiar, M., Moayedfar, R., Vaez Barzani, M., & Mojab, R. (2022). Investigating the Three Dimensions of Credit Risk of Banks in Iran with an Emphasis on the Geographical Location of the Enterprise. Quarterly Journal of the Economic Research (Sustainable Growth and Development), 23(1), 221-247 (In Persian). Barseghyan, L. (2010). Non-Performing Loans, Prospective Bailouts, and Japan's Slowdown. Journal of Monetary Economics, 57(7), 873-890. Beck, R., Jakubik, P., & Piloiu, A. (2015). Key Determinants of Non-Performing Loans: New Evidence from a Global Sample. Open Economies Review, 26(3), 525-550. Bellotti, T. (2010). A Simulation Study of Basel II Expected Loss Distributions for a Portfolio of Credit Cards. Journal of Financial Services Marketing. Castro, V. (2013). Macroeconomic Determinants of the Credit Risk in the Banking System: The Case of the GIPSI. Journal of Economic Modelling, 31, 672–683. Espinoza, R., & Prasad, A. (2010). Nonperforming Loans in the GCC Banking System and Their Macroeconomic Effects. Working Paper, 224, International Monetary Fund. Ghosh, A. (2015). Banking-Industry Specific and Regional Economic Determinants of Non-Performing Loans: Evidence from US States. Journal of Financial Stability, 20, 93-104. Godarzifarahani, Y., & Arabi, S, H. (2021). The Collateral Constraint and Its Impacts on the Banking Performance and Macroeconomic Variables. Biquarterly Journal of Economic Research, 13(25), 375-405 (In Persian). Gonzalez-Hermosillo, B. (1999). Determinants of Ex-Ante Banking System Distress: A Macro-Micro Empirical Exploration of Some Recent Episodes. IMF Working Papers from International Monetary Fund, 1999(033). Hu, Y., & Su, J. (2022). Research on Credit Risk Evaluation of Commercial Banks Based on Artificial Neural Network Model. The 8th International Conference on Information Technology and Quantitative Management. Jimenez, G., & Saurina, J. (2006). Credit Cycles, Credit Risk, and Prudential Regulation. International Journal of Central Banking, 2(2), 65-98. Karadima, M., & Louri, H. (2021). Determinants of Non-performing Loans in Greece: The Intricate Role of Fiscal Expansion. Hellenic Observatory Papers on Greece and Southeast Europe. Greese Paper, No. 160. Karimi Moghari, Z., Asadi Gorji, H., Gilak Hakimabadi, M., & Asadi, N., (2015). Factors Affecting the Credit Risk of Commercial Bank Customers (Case Study: Tejarat Bank Neka City- Mazandaran Province). Two Quarterly Journals of Monetary and Financial Economics, 22(10), 205-234 (in Persian). Kashi, K. (2015). DEMATEL Method in Practice: Finding the Causal Relations among Key Competencies. The 9th International Days of Statistics and Economics, Prague, September 10-12, 2015, 723-732. Kauko, K. (2012). External Deficits and Non-performing Loans in the Recent Financial Crisis. Economics Letters, 115, 196-199. Keshavarz Hadad, GH., & Ayati Gazar, H. (2007). Comparison between Regression and Classification Trees Logit Model and in the Process of Credit Scoring for Individual Customers of a Bank, Quarterly Journal of The Economic Research (Sustainable Growth and Development), 7(4), 71-97. (In Persian). Kick, T., & Prieto, E. (2015). Bank Risk Taking and Competition: Evidence from Regional Banking Markets. Journal of Review of Finance, 19(3), 1185-1222. Klein, N. (2013). Non-Performing Loans in CESEE: Determinants and Impact on Macroeconomic Performance. IMF Working Paper, 01, 27. Koju, L., Koju, R., & Wang, S. (2018). Macroeconomic and Bank-Specific Determinants of Non-Performing Loans: Evidence from Nepalese Banking System. Journal of Central Banking Theory and Practice, 3, 111-138. Louzis, D., Vouldis., A., & Metaxas, V. (2012). Macroeconomic and Bankspecific Determinants of Npls in Greece: A Comparative Study of Mortgage, Business and Consumer Loan Portfolios. Journal of Banking and Finance, 36, 1012-1027. Makri, V., Tsagkanos, A., & Bellas, A. (2014). Determinants of Non-Performing Loans: The Case of Eurozone. Journal of Panoeconomicus, 61(2), 193-206. Mancisidor, R, A., Kampffmeyer, M., Aas, K., & Jenssen, R. (2022). Generating Customer’s Credit Behavior with Deep Generative Models. Journal of Knowledge-Based Systems, 245. Mehrara, M., Mosaee, M., Tasavori, M., & Hasanzadeh, A. (2011). Credit Ranking of Parsian Bank Legal Customers. Quarterly Journal of the Economic Modeling, 3(3), 121-150. (In Persian). Mohd Nor, A., Ismail, S., & Abd Rahman, N. (2021). Determinants of Nonperforming Loans in Asia: Is Southeast Asia Different?. International Journal of Business and Society, 22(1), 431-442. Nkusu, M. M. (2011). Nonperforming Loans and Macrofinancial Vulnerabilities in Advanced Economies. International Monetary Fund. Podpiera J., & Weill, L. (2008). Bad Luck or Bad Management? Emerging Banking Market Experience. Journal of Financial Stability, 4, 135-148. Rinaldi, L., & Sanchis-Arellano, A. (2006). Household Debt Sustainability: What Explains Household Non-Performing Loans? An Empirical Analysis. Working Paper No. 570, European Central Bank. Tari, F., Ebrahimi, S, A., Mosavi, S, J., & Kalantari, M. (2017). Comparison of Neural Network, Genetic Algorithm and Logit Models in Evaluating Customers' Credit Risk. Quarterly Journal of Monetary and Banking Research, 10(34), 657-680 (In Persian). Vithessonthi, C. (2016). Deflation, Bank Credit Growth, and Non-Performing Loans: Evidence from Japan. International Review of Financial Analysis, 45, 295-305. Zeng, Z. (2012). An Overview of Six Economic Zones in Nigeria: Challenges and Opportunities. World Bank Policy Note.
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آمار تعداد مشاهده مقاله: 524 تعداد دریافت فایل اصل مقاله: 328 |
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