rating the factors affecting the credit rating in conditions of social distance in Governmental Banks

Document Type : Research Paper

Authors

1 Ph.D. Student, Department of Accounting, Qazvin Branch, Islamic Azad University, Qazvin, Iran.

2 Associate Professor, Department of Accounting, Qazvin Branch, Islamic Azad University, Qazvin, Iran.

3 Assistant Professor, Department of Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran.

Abstract

Subject and Purpose of the Article: Both public and private banks should pay attention to the existing economic conditions when validating and determining the amount of loans for legal clients. The purpose of this research is rating the factors affecting the credit rating in conditions of social distance in Governmental Banks.
Research Method: This research is applied and descriptive-combined (exploratory and survey) which was performed in two stages. The first step was to identify the criteria (four main axes including financial, non-financial, corporate governance and market criteria) with the Grounded Theory, the second step was to rank the criteria using the Analytical Hierarchy Process. In the first and second stages, the opinions of 20 experts were used to answer the questions.
Research Findings: All factors are important, but the growth of virtual sales has the highest rank as the most important change of the Corona era and the purpose of receiving the loan and the current ratio were the lowest-ranking.
Conclusion, Originality and its Contribution to the Knowledge: In the social distance position, paying attention to the risks associated with the activities of companies in this period has an effect on reducing the overdue receivables of banks, so it is necessary for state-owned banks to pay attention to these issues.

Keywords

References

اسلام­زاده، امید؛ حاجی­نژاد، ابوالفضل؛ زارع، پریسا و صفاری، مهدی (1400)، کنترل­های داخلی در بخش عمومی(دیدگاه نظری)، تهران: انتشارات ترمه.
بلوری، امین؛ مرادی، محمد و یزدانی، حمیدرضا، (1399)، طراحی مدل فرار مالیاتی بر پایه مالیات بر درآمد: رویکرد نظریه داده بنیاد، دوفصلنامه علمی حسابداری دولتی، سال 7، دوره 1، صص 9-30.
بولو، قاسم و اعرابی، مهران، (1398)، شناسایی عوامل موثر بر ریسک جامع بانک­های دولتی، دو فصلنامه علمی حسابداری دولتی، دوره 5، شماره 2، صص 25-46.
حاجیها، زهره و بخشی، نیره، (1396)، بررسی رابطه اطلاعات حسابداری و ریسک اعتباری در شرکت­های پذیرفته شده در بورس اوراق بهادار تهران، مطالعات حسابداری و حسابرسی، سال ششم، شماره 24، صص 53-68.
خداوردی، امید، (1388)، امتیازدهی ریسک اعتباری بیمه شدگان با استفاده از روش‌های هوشمند (مطالعه موردی در یک مؤسسه اعتبار صادراتی)، پایان نامه کارشناسی ارشد دانشگاه تهران.
ظهری، مریم؛ افشارکاظمی، محمدعلی، (1391)، طراحی مدل پیش­بینی ورشکستگی شرکت ها به وسیله شبکه­های عصبی فازی(مطالعه موردی: شرکت­های بورس اوراق بهادار تهران)، فصلنامه مهندسی مالی و اوراق بهادار، دوره 3، شماره 13.
مدرس، احمد و ذکاوت، سید مرتضی،(1386)، مدل­های ریسک اعتباری مشتریان بانک (مطالعه موردی)، فصلنامه حسابرس، شماره 19.
مفاخری، سعید، (1397)، مقایسه تکنیک­های درخت تصمیم و شبکه­های عصبی در طبقه­بندی و ارزش گذاری مشتریان بانکی و انتخاب عوامل بهینه با استفاده از ابزار داده کاوی، هشتمین همایش سالانه بانکداری الکترونیک و نظام­های پرداخت، تهران.
منتی، حسین، (1399)، بررسی اثرات ویروس کرونا کووید 19 بر اقتصاد جهانی، فصلنامه ارزیابی تأثیرات اجتماعی، دوره1، شماره2، صص 163-181.
مهرآرا، محسن؛ موسایی، میثم؛ تصوری، مهسا , حسن­زاده، آیت، (1388)، رتبه­بندی اعتباری مشتریان حقوقی بانک پارسیان، فصلنامه مدل­سازی اقتصاد، دوره 3، شماره4، صص121-150.
 
References
Baby, S., (2013), AHP Modeling for Multicriteria Decision-Making and to Optimise Strategies for Protecting Coastal Landscape Resources, International Journal of Innovation, Management and Technology, Vol. 4, No. 2, April 2013.
Bolori, A., Moradi, M., Yazdani, H. (2021). Design a Tax Evasion Model Based on Income Tax: Grounded Theory Approach. Journal of Governmental Accounting, Vol. 7, N. 1, pp. 9-30. (In Persian).
bulu, G., aarabi, M. (2019). Identifying Effective Factors on Comprehensive Risk of Government-Owned Banks. Journal of Governmental Accounting, 5(2), pp. 25-46.
Driss, H., Drobetz, w., Ghoul, s., Guedhami, o., (2021), Institutional investment horizons, corporate governance, and credit ratings: International evidence, Journal of Corporate Finance, vol. 67, issue C, pp. 1-26.
Estran, R., Souchaud, A., Abitbol, D., (2022), Using a genetic algorithm to optimize an expert credit rating model, Expert Systems with Applications, Vol. 203, in progress.
Habib, A., Ranasinghe, D., (2022), Labor investment efficiency and credit ratings, Finance Research Letters, Vol.48, in progress.
Hajiha, Z., Bakhshi, N., (2017), Investigating the relationship between accounting information and credit risk in companies listed on the Tehran Stock Exchange, Accounting and Auditing Studies, Vol. 6, No. 24, pp. 53-68. (In Persian).
Hao,Y., Li, S., (2021), Does firm visibility matter to debtholders? Evidence from credit ratings, Advances in Accounting, Vol. 52, pp. 1-12.
He, H., Zhang, W., Zhang, S., (2018), A novel ensemble method for credit scoring: adaption of different imbalance ratios, Expert Systems with Applications, Vol. 98, pp. 105–117.
Islamzadeh, O., Hajinejad, A., Zare, P., Saffari, M, (2021), internal controls in the public sector (theoretical perspective), Tehran: Termeh Publications. (In Persian).
Joshi, R., Banwet, D., & Shankar, R., (2011), «A Delphi-AHP-TOPSIS based benchmarking ‎framework for performance improvement of a cold chain», journal of Expert Systems ‎with Applications, vol. 38, n. 2, pp. 10170-10182.‎
Khodaverdi, O., (2009), Credit risk scoring of policyholders using intelligent methods (case study in an export credit institution), Master Thesis, University of Tehran. (In Persian).
Mafakheri, S., (2018), Comparison of decision tree techniques and neural networks in classifying and valuing bank customers and selecting optimal factors using data mining tools, 8th Annual Conference on Electronic Banking and Payment Systems, Tehran.(In Persian).
Mennati, H., (2020), The effects of Corona Covid virus 19 on the global economy, Social Impact Assessment Quarterly, Vol. 1, N. 2, pp. 163-181. (In Persian).
Mehrara, M., Mosaei, M., Tasori, M., Hassanzadeh, A., (2009), Credit Ranking of Legal Customers of Parsian Bank, Economic Modeling Quarterly, Vol. 3, Number 4, pp. 121-150. (In Persian).
Modarres, A., Zakavat, S. M., (2007), Credit risk models of bank customers (case study), Auditor Quarterly, No. 19. (In Persian).
Nazemi, F. Fatemi Pour, K. Heidenreich, F.J. Fabozzi, (2017), Fuzzy decision fusion approach for loss-given-default modeling, European Journal of Operational Research, Vol. 262, n.2, pp. 780–791.
Papouskova,M., Hajek,P., (2019),Two-stage consumer credit risk modelling using heterogeneous ensemble Learning, Decision Support Systems, Vol. 118, pp .33-45.
Park, J., W., Nam, G., Tsang, A., Lee, Y., (2022), Firstborn CEOs and credit ratings, The British Accounting Review, in press.
Serrano-Cinca C, Gutiérrez-Nieto B, López- Palacios L (2015), Determinants of Default in P2P Lending. PLoS ONE 10(10): e0139427.
Serrano-Cinca, C., Gutiérrez-Nieto, B., (2016), The use of profit scoring as an alternative to credit scoring systems in peer-to-peer (P2P) lending, Decision Support Systems, Vol. 89, pp.113–122.
Smales, L.A., (2021), Investor attention and global market returns during the COVID-19 crisis, International Review of Financial Analysis, in press.
Verbraken, T., Bravo, C., Weber, R., Baesens, B., (2014), Development and application of consumer credit scoring models using profit-based classification measures, European Journal of Operational Research, Vol. 238, pp. 505–513.
Zhang, W. ,Wang,c., Zhang , Y., Wang, j., (2020), credit Risk Evaluation Model With Textual Features From Desctiptions For P2P Lending, Electronic Commerce Research and Applications, Vol. 42, pp.1-30.
Zohri, M., Afshar Kazemi, M. A., (2012), Designing a bankruptcy prediction model for companies using fuzzy neural networks, Case Study: Tehran Stock Exchange Companies), Quarterly Journal of Financial Engineering and Securities, Vol. 3, N. 13. (In Persian).
 
Governmental Accounting
Volume 9, Issue 1 - Serial Number 17
December 2022
Pages 27-36

Files

History

  • Receive Date: 28 May 2022
  • Revise Date: 17 July 2022
  • Accept Date: 12 October 2022

Share

How to cite

Statistics