Presenting financial and non-financial indices model affecting the credit risk on the Maskan Bank.

Subject Areas : Financial Accounting

Yasanolah  Poor Ashraf ¹ (Department of Financial Management, Ilam Branch, Islamic Azad University, Ilam, Iran)
Sobhan  Hoosein Beigy ² (Department of Planning & Management, Ilam Branch, Islamic Azad University, Ilam, Iran)
Karam  Khalili ³ (Department of Management, Ilam Branch, Islamic Azad University, Ilam, Iran)

Received: 2022-04-26 Accepted : 2022-07-07 Published : 2023-07-01

Keywords: Indicators, Maskan Bank, Financial and non-financial, Credit Risk,

Abstract :

Providing credit facilities to clients can be regarded as one of the most important tasks of banks. As a result, the current research is carried out with the aim of developing a model of financial and non-financial factors that influence credit risk in the Maskan Bank. This study was divided into two sections: qualitative and quantitative. In the qualitative portion, the Delphi technique was used to identify financial and non-financial variables impacting credit risk in the Maskan Bank by interviewing 18 experts. In the quantitative section, the selected components of the questionnaire were initially created in a 5-point Likert scale. The questionnaire was then presented to 361 legal clients of Maskan Bank from the statistical sam-ple of the quantitative section. The quantitative component was completed using LISREL software and modeling the structural equations of SEM. Three primary components (financial, market, and management) and 33 sub-components were identified as financial and non-financial indicators impacting credit risk based on the results of the qualitative section. The quantitative findings also revealed that the indicators of guarantee status in the financial component, price level in the market component, and manpower motivation and productivity, as well as man-agement continuity and trade in the management component, had the largest factor loads.

References:

[1] Ahmadian Yazdi, F., Ebrahimi Salari, T., Jandaghi, F., Rajab Zadeh Moghani, N., Investigating the effective factors on human capital accumulation in Iran in the period 1971-2012, Journal of Applied Economics Studies in Iran, 2015, 15(4), P.201-228. Doi: 20.1001.1.23222530.1394.4.15.10.1

[2] Mirghafouri, S., Amin, Z., Presenting a Model for Measuring Credit Risk of Bank Customers using Data Mining Approach, Journal of Business Administration Researches, 2015, 13(7), P.147-166.

Doi: 20.1001.1.2645386.1394.7.13.12.8

[3] Chen, C., Lu, H., and Sougiannis, T., The Agency Problem, Corporate Governance, and the Asymmetrical Behavior of Selling, General, and Administrative Costs, Contemporary Accounting Research, 2012, 29(1), P. 92-98. Doi: 10.1111/j.1911-3846.2011.01094.x

[4] Jalili, M., Khodai Vala Zagherd, M., Koneshloo, M., Accreditation of Real Customers in the Country's Banking System, Quantitative Studies in Management, 2010, 6(3), P.127-148.

[5] Cooper, MT., Bray, SJ., Frizzled regulation of Notch signalling polarizes cell fate in the Drosophila eye, Nature, 1999, 39(7), P. 526-530. Doi: 10.1038/17395

[6] Salchenberger, L., Cinar, E, Lash, N., Neural networks: a new tool for predicting thrift failures, Decision Sciences, 1992, 23(4), P. 899-916. Doi: 10.1111/j.1540-5915.1992.tb00425.x

[7] Liu, J., Siu, O., Shi, K., Transformational leadership and employee well-being: The mediating role of trust in the leader and self-efficacy, Applied Psychology: An International Review, 2010, 59(3), P. 454-479.

 Doi: 10.1111/j.1464-0597.2009.00407.x

[8] Khodakarimi, P., Piri, P., Predicting Financial Distress with using combined model of Accounting and Market Data with Logistic Regression Approach, Empirical Studies in Financial Accounting, 2017, 14(55), P. 145-168. Doi: 10.22054/qjma.2018.11118.1366

[9] Akhbari, M., Mokhatab Rafiei, F., Neuro-Fuzzy Inference System Application for Credit Rating of Bank Customers, Journal of Economic Research (Tahghighat- E- Eghtesadi), 2010, 45(3), P.27-39.

 Doi: 20.1001.1.00398969.1389.45.3.1.6

[10] Yaqubi, N., Shokri, A., Rahat Dahmarde, M., A Study of the Structural Enabling Factors of Organizational Agility in Banking System, Strategic Management Thought, 2015, 6(1), P. 133-158. Doi: 10.30497/smt.2012.1026

[11] Dong, G., Credit scorecard based on logistic regression with random coefficients, Journal of Procedia Computer Science, 2010, 16(1), P.2463-2468. Doi: 10.1016/j.procs.2010.04.278

[12] Tabagari, S., Credit Scoring by Logistic Regression, (MS), University of Tart, 2015, 19(6), P.204-224.

 Doi: 10.5373/JARDCS/V11SP11/20193134

[13] R‌a‌z‌m‌i, J., S‌h‌a‌h‌b‌a‌z‌i, M., Comparison of different neural network models in the credit rating of the banking system and introduction of the best model, Sharif Journal of Industrial Engineering & Management, 2019, 32(2), P.127-140.

[14] Ghasemnia Arabi, N., Safaei Ghadikolaei, A., Comparison of the Performances of Classical Models and Artificial Intelligence in Predicting Bank Customers' Credit Status, Journal of Business Administration Researches, 2019, 10(20), P. 51-69. Doi: 10.29252/bar.2019.1320

[15] Rastegar, M., Eidi Goosh, M., Credit Risk Modeling: Spline based logistic regression Survival approach, Journal of Investment Knowledge, 2020, 9(34), P. 167-184.

[16] Gholamrezapoor, M., Kazemi, P., Amirniya, N., Cash Holding Adjustment Speed: The Role of Managerial Ability and Moderating Role of Political Connections in Tehran Stock Exchange, Advances in Mathematical Finance and Applications, 2022, 7(3), P.759-776. Doi: 10.22034/amfa.2021.1891437.1364

[17] Moslemi, A., Pourzamani, Z., Jahanshad, A., Ranking of Banks’ Risk Reporting Using Data Envelopment Analysis, Advances in Mathematical Finance and Applications, 2021, 6(4), P.695-715.

 Doi: 10.22034/amfa.2021.1899631.1436

[18] Strauss, A., Corbin, J., Grounded theory methodology: an overview, in Denzin, N. and Lincoln, Y. (Eds), Handbook of Qualitative research, N.P: Sage publications, 1994, P. 16-21.

[19] Sarmad, Z., Bazargan, A., Hejazi, E. Research Methods in Behavioral Sciences, Agah Publications, 2020, P. 32-39.

[20] Jahanseir Khararoudi, F., Azar, A., Karimi, T., Evaluating the Performance of an Ambidextrous Bank Using an Agent-based Modeling Approach: A Case Study of Sepah Bank, Advances in Mathematical Finance and Applications, 2019, 4(4), P.39-49. Doi: 10.22034/amfa.2019.1874765.1269

[21] Feizi, K., Irandoost, M., Delphi: A method for research, decision making and futures research, IndustrialManagement Organization Publications, 2013, P. 59-63.

[22] Poordavoodi, A., Reza, M., Haj, H., Rahmani, A. M., Izadikhah, M., Toward a More Accurate Web Service Selection Using Modified Interval DEA Models with Undesirable Outputs, CMES-Computer Modeling in Engineering & Sciences, 2020, 123(2), P. 525–570. Doi: 10.32604/cmes.2020.08854

 

[23] Azadi, M., Izadikhah, M., Ramezani, F., Hussain, F.K., A mixed ideal and anti-ideal DEA model: an application to evaluate cloud service providers, IMA Journal of Management Mathematics, 2000, 31(20), P. 233–256, Doi: 10.1093/imaman/dpz012

 

[24] Izadikhah, M., Financial Assessment of Banks and Financial Institutes in Stock Exchange by Means of an Enhanced Two stage DEA Model, Advances in Mathematical Finance and Applications, 2021, 6(2), P. 207-232. Doi: 10.22034/amfa.2020.1910507.1491

 

[25] Zamani, S., Zanjirdar, M., Lalbar, A. The effect of information disclosure on market reaction with meta-analysis approach. Advances in Mathematical Finance and Applications, 2022, 7(3), P. 629-644.

Doi: 10.22034/amfa.2021.1937478.1625

 

[26] Zanjirdar, M., Overview of Portfolio Optimization Models, Advances in Mathematical Finance and Applications, 2020. 5(4), P.419-435. Doi: 10.22034/amfa.2020.674941.

 

[27] Zanjirdar, M., Kasbi, P., Madahi, Z., Investigating the effect of adjusted DuPont ratio and its components
on investor & quot; s decisions in short and long term, Management Science Letters, 2014, 4(3), P.591-596.
Doi: 10.5267/j.msl.2014.1.003

[28] Rahmani, A., Zanjirdar, M., Ghiabi, H. Effect of Peer Performance, Future Competitive Performance, and Factors of Correlation with Peer Companies on Manipulation of Abnormal Real Operations, Advances in Mathematical Finance and Applications, 2021, 6(1), P. 57-70. Doi: 10.22034/amfa.2020.1875478.1272