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

محورهای موضوعی : مدیریت ریسک

رحمان رحیمی ¹ ( گروه حسابداری، واحد تهران جنوب، دانشگاه آزاد اسلامی، تهران، ایران. )
فاطمه صراف ² ( گروه حسابداری، واحد تهران جنوب، دانشگاه آزاد اسلامی، تهران، ایران. )
محبوبه جعفری ³ ( گروه حسابداری، واحد تهران جنوب، دانشگاه آزاد اسلامی، تهران، ایران. )
بیژن صفوی 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.

منابع و مأخذ:

Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723.

An, J; Rau, R. (2019). Finance, technology and disruption. The European Journal of Finance, 27(4-5), 1–12.

Angelini, E; Banbura, M; Rünstler, G. (2010). Estimating and forecasting the euro area monthly national accounts from a dynamic factor model. OECD Journal: Journal of Business Cycle Measurement and Analysis, 2010(1), 1–22.

Antoni Haber, J; D’yakonova, I; Milchakova, A. (2018). Estimation of fintech market in Ukraine in terms of global development of financial and banking systems. Public and Municipal Finance, 7(2), 14–23.

Artis, M. J; Banerjee, A; Marcellino, M. (2005). Factor forecasts for the UK. Journal of Forecasting, 24(4), 279–298.

Aye, G; Gupta, R; Hammoudeh, S; Kim, W. J. (2015). Forecasting the price of gold using dynamic model averaging. International Review of Financial Analysis, 41, 257–266.

Barra, C; Ruggiero, N. (2022). Bank-specific factors and credit risk: evidence from Italian banks in different local markets. Journal of Financial Regulation and Compliance.

Baur, D. G; Beckmann, J; Czudaj, R. (2016). A melting pot - Gold price forecasts under model and parameter uncertainty. International Review of Financial Analysis, 48, 282–291.

Baur, D. G; Glover, K. J. (2014). Heterogeneous expectations in the gold market: Specification and estimation. Journal of Economic Dynamics and Control, 40, 116–133.

Beck, R; Jakubik, P; Piloiu, A. (2013). Non-Performing Loans: What Matters in Addition to the Economic Cycle? SSRN Electronic Journal.

Bellotti, T; Crook, J. (2012). Loss given default models incorporating macroeconomic variables for credit cards. International Journal of Forecasting, 28(1), 171–182.

Belmonte, M; Koop, G. (2014). Model Switching and Model Averaging in Time-Varying Parameter Regression Models. Advances in Econometrics, 34, 45–69.

Bernanke, B. S; Boivin, J. (2003). Monetary policy in a data-rich environment. Journal of Monetary Economics, 50(3), 525–546.

Bester, H. (1994). The Role of Collateral in a Model of Debt Renegotiation. Journal of Money, Credit and Banking, 26(1), 72.

Boivin, J; Ng, S. (2006). Are more data always better for factor analysis? Journal of Econometrics, 132(1), 169–194.

Brownbridge, M. (1998). Financial Distress in Local Banks in Kenya, Nigeria, Uganda and Zambia: Causes and Implications for Regulatory Policy. Development Policy Review, 16(2), 173–188.

Buchak, G; Matvos, G; Piskorski, T; Seru, A. (2018). Fintech, regulatory arbitrage, and the rise of shadow banks. Journal of Financial Economics, 130(3), 453–483.

Buncic, D; Moretto, C. (2015). Forecasting copper prices with dynamic averaging and selection models. The North American Journal of Economics and Finance, 33, 1–38.

Cheng, M; Qu, Y. (2020). Does bank FinTech reduce credit risk? Evidence from China. Pacific-Basin Finance Journal, 63, 101398.

Cornelli, G; Frost, J; Gambacorta, L; Rau, R; Wardrop, R; Ziegler, T. (2020). Fintech and big tech credit: a new database. BIS Working Paper, 887.

Dadashi, I; Kordmanjiri, S; Khoshnoud, Z; Gholamnia Roshan, H. (2020). Investigating the Variables Affecting Banks’ Legal Customers Credit Risk, Using Support Vectors Machine and Decision Tree. Journal of Financial Management Perspective, 10(31), 53-73. [In Persian]

D’Agostino, A; Gambetti, L; Giannone, D. (2013). Macroeconomic Forecasting and Structural Change. Journal of Applied Econometrics, 28(1), 82–101.

Davies, R. B. (1987). Hypothesis Testing when a Nuisance Parameter is Present Only Under the Alternatives. Biometrika, 74(1), 33-43.

Derban, W. K; Binner, J. M; Mullineux, A. (2005). Loan Repayment Performance in Community Development Finance Institutions in the UK. Small Business Economics, 25(4), 319–332.

Di Filippo, G. (2015). Dynamic Model Averaging and CPI Inflation Forecasts: A Comparison between the Euro Area and the United States. Journal of Forecasting, 34(8), 619–648.

Drachal, K. (2016). Forecasting spot oil price in a dynamic model averaging framework - Have the determinants changed over time? Energy Economics, 60, 35–46.

Ebrahimi, M; Daryabor, A. (2012). Credit risk management in the banking system - A comparative approach of Data Envelopment Analysis and Neural Network and Logistic Regression. Journal of Investment Knowledge, 1(2), 35-62. [In Persian]

Espinoza, R. A; Prasad, A. (2010). Nonperforming Loans in the GCC Banking System and Their Macroeconomic Effects. SSRN Electronic Journal.

Fallahpour, S; Raei, R; Hendijani, M. (2013). Neural Network Approach Based on Artificial Bee Colony. Financial Engineering and Securities Management, 5(21), 33-53. [In Persian]

Ferreira, D; Palma, A. (2015). Forecasting Inflation with the Phillips Curve: A Dynamic Model Averaging Approach for Brazil. Revista Brasileira de Economia, 69(4).

Forni, M; Hallin, M; Lippi, M; Reichlin, L. (2004). The generalized dynamic factor model consistency and rates. Journal of Econometrics, 119(2), 231–255.

Furlonger, D; Newton, A. (2018). Digitalization Will Make Most Heritage Financial Firms Irrelevant by 2030. Gartner.

Garcia, R; Perron, P. (1996). An Analysis of the Real Interest Rate Under Regime Shifts. The Review of Economics and Statistics, 78(1), 111-125.

Garratt, A; Mitchell, J; Vahey, S; Wakerly, E. (2011). Real-time inflation forecast densities from ensemble Phillips curves. The North American Journal of Economics and Finance, 22(1), 77–87.

Ghaderi Moghaddam, R; baseri, B; falehi, N; abbasi, G. (2022). Investigating the Impact of Uncertainty in Macroeconomic Variables on Electricity and Gas Consumption in Iran (Markov-Switching approach). Quarterly Economic Research, 6(19), 77-97. [In Persian]

Ghasemi Armaky, A; Fallah, M; Alborzi, M. (2022). Development and Explanation of Bank Customers' Credit System Based on Hybrid Learning Models: A Case Study of Bank Mellat. Journal of Financial Management Perspective, 12(37), 69-94. [In Persian]

Gobbi, H; Dupont, W. D; Simpson, J. F; Plummer, W. Dale; Schuyler, P. A; Olson, S. J; Arteaga, C. L; Page, D. L. (1999). Transforming Growth Factor-β and Breast Cancer Risk in Women With Mammary Epithelial Hyperplasia. JNCI: Journal of the National Cancer Institute, 91(24), 2096–2101.

Greve, J; Grün, B; Malsiner‐Walli, G; Frühwirth‐Schnatter, S. (2022). Spying on the prior of the number of data clusters and the partition distribution in Bayesian cluster analysis. Australian & New Zealand Journal of Statistics, 64(2), 205–229.

Groen, J. J. J; Paap, R; Ravazzolo, F. (2013). Real-Time Inflation Forecasting in a Changing World. Journal of Business & Economic Statistics, 31(1), 29–44.

Guo, P; Shen, Y. (2016). The impact of Internet finance on commercial banks’ risk taking: evidence from China. China Finance and Economic Review, 4(1).

Habibi, R; Kouhi, H; Baidi, H. (2018). Bank Lending Decision Using Genetic Algorithm (Case Study: Bank Sepah Legal Customer). Quarterly Studies in Banking Management and Islamic Banking, 4(9), 33-71. [In Persian]

Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357-384.

Han, Y; Wang, T. (2021). Semi-Supervised Clustering for Financial Risk Analysis. Neural Processing Letters, 53(5), 3561–3572.

Horri, M. Sadegh; Mahdavi, K. (2015), Designing A Model to Predict the Credit Rating of Bank Customers Using Meta-Heuristic and Multi-Criteria Hybrid Algorithm of Fuzzy Neural Network - Ant Colony (Case Study of Post Bank Branches of Tehran Province). Management Research in Iran, 19(1), 91-116. [In Persian]

Huber, F; Koop, G; Onorante, L. (2021). Inducing Sparsity and Shrinkage in Time-Varying Parameter Models. Journal of Business & Economic Statistics, 39(3), 669–683.

Jafari Eskandari, M; Rohii, M. (2017). Credit Risk Management of Banking Customers Using Support Vector Machine Optimized by Genetic Algorithm with Data Mining Approach. Journal of Asset Management and Financing, 5(4), 17-32. [In Persian]

Kabari, L. G; Nwachukwu, E. O. (2013). Credit Risk Evaluating System Using Decision Tree – Neuro Based Model. International Journal of Engineering Research & Technology, 2(6), 2738-2745.

Koop, G. (2012). Using VARs and TVP-VARs with Many Macroeconomic Variables. Central European Journal of Economic Modelling and Econometrics, 4(3), 143–167.

Koop, G; Korobilis, D. (2009). Bayesian Multivariate Time Series Methods for Empirical Macroeconomics. SSRN Electronic Journal.

Koop, G; Korobilis, D. (2012). Forecasting Inflation Using Dynamic Model Averaging. International Economic Review, 53(3), 867–886.

Koop, G; Korobilis, D. (2011). UK macroeconomic forecasting with many predictors: Which models forecast best and when do they do so? Economic Modelling, 28(5), 2307–2318.

Koop, G; Korobilis, D; Pettenuzzo, D. (2019). Bayesian compressed vector autoregressions. Journal of Econometrics, 210(1), 135–154.

Koop, G; McIntyre, S; Mitchell, J; Poon, A. (2020). Regional output growth in the United Kingdom: More timely and higher frequency estimates from 1970. Journal of Applied Econometrics, 35(2), 176–197.

Korobilis, D. (2021). High-Dimensional Macroeconomic Forecasting Using Message Passing Algorithms. Journal of Business & Economic Statistics, 39(2), 493–504.

Kuck, K; Schweikert, K. (2017). A Markov regime-switching model of crude oil market integration. Journal of Commodity Markets, 6, 16–31.

Louzis, D. P; Vouldis, A. T; Metaxas, V. L. (2010). Macroeconomic and Bank-Specific Determinants of Non-Performing Loans in Greece: A Comparative Study of Mortgage, Business and Consumer Loan Portfolios. SSRN Electronic Journal.

Mansourfar, Gh; Piri, P; Alikhani, Z; Asadi, M. (2018). Forecasting financial helplessness according to the effects of adjustment clauses in the independent auditor's report. The 16th National Accounting Conference of Iran. [In Persian]

Marcellino, M; Stock, J. H; Watson, M. W. (2003). Macroeconomic forecasting in the Euro area: Country specific versus area-wide information. European Economic Review, 47(1), 1–18.

Min, J. H; Lee, Y. C. (2008). A practical approach to credit scoring. Expert Systems with Applications, 35(4), 1762–1770.

Mirghafouri, S. H; Amin Ashouri, Z. (2015). Presenting a Model for Measuring Credit Risk of Bank Customers using Data Mining Approach. Journal of Business Administration Researches, 7(13), 247-266. [In Persian]

Mohaghegh Nia, M. J; Dehgan Dahnavi, M. A; Baei, M. (2019). The Effect of internal and external factors of credit banks and banks' credit industry in Iran. Financial Economics, 13(46), 127-144. [In Persian]

Mohammadian Haji Kord, Amin; Asgharzadeh Zafarani, Malihe; Emam Doost, Mostafa. (2016). Credit risk assessment of corporate customers using support vector machine and genetic algorithm hybrid model - a Case Study of Tejarat Bank. Financial Engineering and Portfolio Management, 7(27), 17-32. [In Persian]

Moradi, S; Mokhatab Rafiei, F; Saghaee, A. (2022). Identifying patterns of the dynamic credit risk of banks customers and financial institutions: case study- an Iranian bank. Journal of Monetary and Banking Research, 15(51), 121-154. [In Persian]

Motedayen, N; Nazarian, R; Damankeshideh, M; Seifipour, R. (2022). Identification and Evaluation of the Factors Affecting Credit Risk Management Using the Multinomial Logistic Regression Model. Iranian Economic Review, 26(3), 707-720.

Naser, H. (2014). An Econometric Investigation of Forecasting GDP, Oil Prices, and Relationships among GDP and Energy Sources. Etheses.whiterose.ac.uk.

Naser, H; Alaali, F. (2018). Can oil prices help predict US stock market returns? Evidence using a dynamic model averaging (DMA) approach. Empirical Economics, 55(4), 1757–1777.

Nazaraghaei, M; Ghiasi, H; Asgharkhah Chafi, M. (2019). Classification of Customer’s Credit Risk Using Ensemble learning (Case study: Sepah Bank). Journal of Monetary and Banking Research, 12(39), 129-166. [In Persian]

Ngo, T; Le, V; Le, H. (2021). Factors affecting credit risk in lending activities of joint-stock commercial banks in Vietnam. Journal of Eastern European and Central Asian Research (JEECAR), 8(2), 228–239.

Nicoletti, G; Passaro, R. (2012). Sometimes it Helps: The Evolving Predictive Power of Spreads on GDP Dynamics. SSRN Electronic Journal.

Noroozi, P. (2014). The Effect of Macroeconomic Variables on Credit Risk of Banks in Iran. Journal of Monetary and Banking Research, 7(20), 237-257. [In Persian]

Pérez-Martín, A; Pérez-Torregrosa, A; Vaca, M. (2018). Big Data techniques to measure credit banking risk in home equity loans. Journal of Business Research, 89, 448–454.

Pesaran, M. H; Timmermann, A. (2000). A Recursive Modelling Approach to Predicting UK Stock Returns. The Economic Journal, 110(460), 159–191.

Petralia, K; Philippon, T; Rice, T; Véron, N. (2019). Banking disrupted? Financial intermediation in an era of transformational technology. Geneva Reports on the World Economy.

Phan, D. H. B; Narayan, P. K; Rahman, R. E; Hutabarat, A. R. (2020). Do financial technology firms influence bank performance? Pacific-Basin Finance Journal, 62, 101210.

Pourkazemi, M. H; Sedaghat Parast, E; Dehpanah, R. (2018). Estimating Default Probability of Bank Customers Using Neural Networks Method (Case Study: Pasargad Bank). Quarterly Studies in Banking Management and Islamic Banking, 3(6-7), 1-23. [In Persian]

Qiao, H; Chen, M; Xia, Y. (2018). The Effects of the Sharing Economy: How Does Internet Finance Influence Commercial Bank Risk Preferences? Emerging Markets Finance and Trade, 54(13), 3013–3029.

Raei, R; Fallahpour, S. (2009). Support Vector Machines Application in Financial Distress Prediction of Companies Using Financial Ratios. Accounting and Auditing Review, 15(4). [In Persian]

Raftery, A. E; Kárný, M; Ettler, P. (2010). Online Prediction Under Model Uncertainty via Dynamic Model Averaging: Application to a Cold Rolling Mill. Technometrics, 52(1), 52–66.

Risse, M; Kern, M. (2016). Forecasting house-price growth in the Euro area with dynamic model averaging. The North American Journal of Economics and Finance, 38, 70–85.

Rostami, M. R; Nabizade, A; Shahi, Z. (2019). Factors Affecting Credit Risk of Commercial Banks of Iran with Emphasis on Banking and Macroeconomic Specific Factors. Journal of Asset Management and Financing, 6(4), 79-92. [In Persian]

Rostamzadeh, P; Shahnazi, R; Neisani, M S. (2018). Identification of Factors Affecting on Credit Risk in the Iran Banking Industry of Iran Using Stress Test. Journal of Economic Modeling Research, 9(32), 91-128. [In Persian]

Sadigov, S; Vasilyeva, T; Rubanov, P. (2020). Fintech in economic growth: cross-country analysis. 55th International Scientific Conference on Economic and Social Development.

Safari, S; Ebrahimi Shaghaghi, M; Taherifard, M. (2020). Credit Risk Management In The Banking System. Commercial Strategies, 9(47), 121-140. [In Persian]

Sala-I-Martin, X. X. (1997). I Just Ran Two Million Regressions. The American Economic Review, 87(2), 178–183.

Santomero, A. M. (1997). Commercial Bank Risk Management: An Analysis of the Process. Journal of Financial Services Research, 12(2-3), 83–115.

Schumacher, C. (2007). Forecasting German GDP using alternative factor models based on large datasets. Journal of Forecasting, 26(4), 271–302.

Stock, J; Watson, M. (2005). An Empirical Comparison of Methods for Forecasting Using Many Predictors. Scholar.harvard.edu.

Stock, J; Watson, M. (1998). Diffusion Indexes. RePEc - Econpapers.

Stock, J. H; Watson, M. (2006). Forecasting with Many Predictors. RePEc - Econpapers.

Tari, F; Ebrahimi, S. A; Mousavi, S. J; Kalantari, M. (2018). Comparison Between Neural Network, Genetic Algorithm and Logit Models in Evaluating Consumer Credit Risk. Journal of Monetary and Banking Research, 10(34), 657-680. [In Persian]

Torabian, A; Nahidi Amirkhiz, M. R; Jani, S; Hasanzadeh, R. (2022). Bank Client Accreditation And Ranking: A Case Study Of Saderat Bank Of Iran. Journal Of Investment Knowledge, 11(41), 145-162. [In Persian]

Wang, R; Liu, J; Luo, H. (2020). Fintech development and bank risk taking in China. The European Journal of Finance, 27(4-5), 397–418.

YU, L; WANG, S; LAI, K. (2008). Credit risk assessment with a multistage neural network ensemble learning approach. Expert Systems with Applications, 34(2), 1434–1444.

Zhao, Q; Tsai, P. H; Wang, J. L. (2019). Improving Financial Service Innovation Strategies for Enhancing China’s Banking Industry Competitive Advantage during the Fintech Revolution: A Hybrid MCDM Model. Sustainability, 11(5), 1419.