مدلسازی و شناسایی روابط علی بین عوامل اصلی ریسک اعتباری در سیستم بانکی با استفاده از تکنیک تصمیم‌گیری دیمتل

مقالات آماده انتشار

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری اقتصاد دانشگاه رازی، کرمانشاه، ایران

2 دانشیار گروه اقتصاد دانشگاه رازی، کرمانشاه، ایران

10.22034/epj.2024.20992.2544

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

موضوعات

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 Jeyhoonipour 1
  • Somayeh Azami 2
  • Sohrab Delangizan 2
1 Ph.D. student in Economics, University of Razi, Kermanshah, Iran.
2 Associate 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

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مقالات آماده انتشار، پذیرفته شده
انتشار آنلاین از تاریخ 07 مهر 1403

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