مدل‌سازی و شناسایی متغیرهای غیرشکننده مؤثر بر ریسک اعتباری در بانک تجارت با تأکید بر فناوری‌های فین‌تک

محورهای موضوعی : مدیریت ریسک

رحمان رحیمی ¹ , فاطمه صراف ² , محبوبه جعفری ³ , بیژن صفوی ⁴

1 - گروه حسابداری، واحد تهران جنوب، دانشگاه آزاد اسلامی، تهران، ایران.
2 - گروه حسابداری، واحد تهران جنوب، دانشگاه آزاد اسلامی، تهران، ایران.
3 - گروه حسابداری، واحد تهران جنوب، دانشگاه آزاد اسلامی، تهران، ایران.
4 - گروه اقتصاد، واحد تهران جنوب، دانشگاه آزاد اسلامی، تهران، ایران.

تاریخ دریافت : 1401/06/20 تاریخ پذیرش : 1401/10/05 تاریخ انتشار : 1401/10/01

کلید واژه: مدیریت ریسک, ریسک اعتباری, بانک تجارت, فین‌تک, مدل‌های بیزین,

چکیده مقاله :

هدف: ارزیابی ریسک اعتباری یکی از موضوعات چالش‌برانگیز در بخش بانکی است؛ مشکل اصلی مدل‌سازی صحیح این نوع ریسک می‌باشد. تنوع متغیرهای اثرگذار بر ریسک اعتباری و نبود یک مدل مشخص از مهم‌ترین علل شکست مدل‌های سنتی می‌باشد. براین‌اساس هدف تحقیق حاضر مدل‌سازی ریسک اعتباری بانک تجارت در رژیم‌های مختلف اقتصادی است.روش‌شناسی پژوهش: این تحقیق کاربردی است. بازه زمانی تحقیق پنج سال (1400-1396) و از بین 105 هزار مشتری بانک تجارت انتخاب شده است. برای این منظور، اطلاعات شاخص‌های 33 عامل مؤثر بر ریسک اعتباری در بانک تجارت وارد مدل‌های BMA، TVP-DMA و TVP-DMS شد. پس از برآورد مدل، 8 متغیر اصلی شناسایی گردید.یافته‌ها: تأثیر متغیرهای غیرشکننده در مدل مارکوف سویچینگ بر ریسک اعتباری مورد تجزیه‌وتحلیل قرار گرفت. اکثریت متغیرها به‌جز متغیر مشتریان حقوقی تأثیر منفی و معناداری بر سطح ریسک اعتباری دارند و با حرکت از سمت رونق بالا به‌ سمت رکود عمیق میزان تأثیرگذار متغیرها بر ریسک اعتباری افزایش یافته ‌است.اصالت / ارزش‌افزوده علمی: نتایج نشان داد مدل بهینه ریسک اعتباری در هر بانک متفاوت است. براین‌اساس اقدام به طراحی یک مدل بهینه صرفاً برای ریسک اعتباری در بانک تجارت بر اساس الگوهای میانگین‌گیری بیزین گردید. بر اساس رویکرد مارکوف سویچینگ نیز این نتیجه حاصل شد که ریسک اعتباری در حالت رکود اقتصادی حساسیت بالاتری نسبت به متغیرهای توضیح‌دهنده خود دارد. همچنین بر اساس نتایج مشخص گردید که شاخص‌های مرتبط با فین‌تک بر سطح ریسک اعتباری در بانک تجارت تأثیر معناداری دارند.

چکیده انگلیسی:

Purpose: Credit risk assessment is one of the most challenging issues in the banking sector. The main problem is the correct modeling of this type of risk. Variety of variables affecting credit risk and The lack of a specific model is one of the most important reasons for the failure of traditional models. Based on this, the aim of this research is to model the credit risk of Tejarat Bank in different economic regimes.Methodology: The time period of the research is (2017-2021) and it has been selected from among 105 thousand customers of Tejarat Bank. information on indicators affecting credit risk was entered into BMA, TVP-DMA and TVP-DMS models. After estimating the model, 8 main variables were identified.Findings: the effect of non-fragile variables on the credit risk in the Markov switching model was analyzed. most of the variables, have a negative and significant effect on the level of credit risk, and by moving from high prosperity to deep recession, the impact of variables on credit risk has increased. Originality / Value: The results showed that the optimal credit risk model is different in each bank. Based on this, an optimized model was designed Only for credit risk in Tejarat Bank based on Bayesian averaging patterns. Credit risk in the state of economic recession has a higher sensitivity to its explanatory variables.Also, based on the results, it was determined that indicators related to fintech have a significant effect on the level of credit risk in Tejarat Bank.

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