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Reviewing and evaluating the customer validation system in risk management with the approach of fee income research in Melli Bank of Iran | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 16، دوره 15، شماره 10، دی 2024، صفحه 181-192 اصل مقاله (1.84 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2023.31286.4605 | ||
| نویسندگان | ||
| Hassan Zolfaghari1؛ Alireza Farokhbakht Foomani* 2؛ Nima Ranji Jafroudi2 | ||
| 1Department of Management, Astara Branch, Islamic Azad University, Astara, Iran | ||
| 2Department of Management, Bandar Anzali Branch, Islamic Azad University, Bandar Anzali, Iran | ||
| تاریخ دریافت: 27 فروردین 1402، تاریخ بازنگری: 22 مرداد 1402، تاریخ پذیرش: 24 مرداد 1402 | ||
| چکیده | ||
| This research was done with the aim of designing a customer validation system model based on risk management with the approach of realizing fee income in the National Bank of Iran. The current research is applied in terms of purpose and in terms of method, it is a mixed (mixed)-exploratory design, which is based on grounded theory in the qualitative stage and descriptive-survey method in the quantitative stage. Based on the theme analysis method, after the interviews with the experts, which were conducted after the theoretical saturation of 15 interviews, all the text of the interviews was entered into the Max QDA software, and then the primary codes and sub- and main categories were extracted. Then, based on the Strauss and Corbin model, the central, causal, strategic, consequences, interventionists and contextual factors categories were identified. Finally, the research model was designed. In order to ensure the coordination of the data with the factor structure, and the quality indicators of the model, a questionnaire consisting of 95 items was distributed to a quantitatively wide population including 384 employees of National Bank. The data were analyzed using the structural equation modelling approach. The results of the qualitative part showed that the central phenomenon includes a customer validation system, causal conditions including risk management, background conditions including customer orientation, strategies and measures including banking facilities, and intervening conditions including cultural factors. The consequences include fee income and profitability. According to the results presented in the research model review, it can be stated that causal conditions (risk management) have a positive and significant effect on the central category of the customer validation system. The central category of the customer validation system is on strategies (banking facilities) which have a positive and significant effect. Background conditions (customer-oriented) have a positive and significant effect on strategies. Intervening conditions (cultural factors) have a positive and significant effect on strategies. Strategies have a positive and significant impact on outcomes (fee income and profitability). | ||
| کلیدواژهها | ||
| risk management؛ customer validation؛ fee income research؛ National Bank of Iran | ||
| مراجع | ||
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[1] M. Al-Barzi, M. Pourzarandi, M. Ibrahim, and M. Khan Babaei, Application of genetic algorithm in optimization of decision trees for validation of bank customers, Inf. Technol. Manag. 2 (2010), no. 4, 23–38. [2] B. Al-Own and M. Minhat Simon Gao, Stock options and credit default swaps in risk management, J. Int. Financ. Markets Inst. Money 53 (2017), 200–214. [3] A. Arab Mazara and P. Rouyin Ten, Factors affecting the credit risk of bank customers, a case study of Keshavarzi Bank, Econ. Surveys 3 (2015), no. 6, 45–80. [4] A. Bafande and R. Rahimi, Providing a fuzzy expert system to validate the real customers of the bank (Study: Mellat Bank under the supervision of East Azarbaijan Province), Payam Noor University of Tehran Province, Technical and Engineering Research School, 2021. [5] Gh. Bennouna and M. Tkiouat, Scoring in microfinance: credit risk management tool Case of Morocco, Procedia Comput. Sci. 148 (2019), 522–531. [6] D. Bulbul, H. Hakenes, and C. Lambert, What influences banks’ choice of credit risk management practices? Theory and evidence, J. Financ. Stab. 40 (2018), 1–14. [7] W. Chen, G. Xiang, Y. Liu, and K. Wang, Credit risk Evaluation by hybrid data mining technique, Syst. Engin. Procedia 0 (2019), no. 3, 20–94. [8] N. Dehmardeh, J. Shahraki, S. Saifuddinpour, and M. Esfandiari, Validation of bank customers using the credit scoring approach: a case study of Sepeh Bank branches in Zahedan, Gen. Knowledge Manag. 5 (2018), no. 18, 135-155. [9] H. Hillbun, Keng-Ming (Terence) TienSonika Singh, J. Retail. Consumer Serv. Hillbun HoKeng-Ming (Terence) 59 (2021), 102391. [10] A. Incekara and H. Ctinkaya, Credit risk management: A panel data analysis on the Islamic banks in Turkey, Procedia Comput. Sci. 158 (2019), 947–954. [11] M. Jalili, M. Khodayi Waleh Zakard, and M. Kenshlo, Validation of real customers in the country’s banking system, Quant. Stud. Mmanag. 1 (2019), no. 3, 127–148. [12] S. Kazemi, Investigating the factors affecting the credit risk of the customers of Qarz Al-Hasneh Mehr Bank, Kohgiluyeh and Boyer Ahmad provinces of Iran, 4th Int. Conf. Knowledge Technol. Third Millennium of Iran’s Econ. Manag. Account., Tehran, 2021. [13] H. Khaleghifar, Presenting the model of factors affecting credit risk using logit regression in Ekhtaz Navin Bank, First Nat. Conf. Dev. Monetary Bank. Manag., Tehran, 2018. [14] M.A. Kimiagari, M.J. Amini, H. Tabatabai Arani, and L. Hosseini, credit risk model of repayment facilities for bank credit customers (the case of Shahreza district of Melli Bank of Iran), 9th Int. Ind. Engin. Conf., Tehran, 2011. [15] J. Lin and L. Han, Lattice clustering and its application in credit risk management of commercial banks, Procedia Comput. Sci. 183 (2021), 145–151. [16] Y. Lui, New Issues In Credit Scoring Applications, George Agust, University Gottingen, Institute For Wirtschafts informatics, 2011. [17] T. Nikraftar, E. Hosseini, M. Aqeli, and M. Moslimi Kaviri, Examining the moderating role of religious prejudice in consumers’ moral behavior intention, Case study: Food consumers in Isfahan city, Res. Relig. Health 3 (2019), no. 6, 18–30. [18] H. Mirzaei, R. Nazarian and R. Bagheri, Investigating factors affecting the credit risk of legal entities of banks (Case study of branches of the National Bank of Iran, Tehran), Econ. Res. Trends 19 (2013), no. 58, 67–98. [19] R. Rai and A. Soroush, Validation of small and medium-sized legal customers of banks using logit and probit models, Econ. Res. J. 12 (2011), no. 44, 131–145. [20] S. Sadeghi Khani and F. Ramezani, Credit risk and rating of Bank Saderat Ilam customers using econometric method and logit regression model, 8th Nat. Conf. Econ. Manag. Account., Shirvan, 2021. [21] F. Shamsi Mirfaiz, Designing and explaining the credit risk model in the country’s banking system, Proc. 16th Islamic Bank. Conf., 2016. [22] S.M. Shariat Panahi and S. Hashemi Barkadehi, Presenting a model for validating customers in Sanat and Mine Bank, J. Empir. Stud. Financ. Account. (2019), no. 21. [23] S. Shi, R. Tse, W. Luo, S. D’Addona, and G. Pau, Machine learning-driven credit risk: A systemic review, Neural Comput. Appl. 34 (2020), no. 17, 14327–14339. [24] S. Singh, B.P.S. Murthi, and E. Steffes, Developing a measure of risk-adjusted revenue (RAR) in credit cards market: Implications forcustomer relationship management, Eur. J. Oper. Res. 2 (2013), no. 224, 425–434. [25] M. Sivabalakrishnan, S. Boopesh, M.K. Priyan, S. Jeeva, P. Vigneshwaran, and M. Islabudeen, Artificial intelligence email automater and insights generator of credit card customers based on unsupervised learning, Proc. Int. Conf. Emerg. Trends Materials Sci. Technol. Engin., 2021. [26] M. Tagvi, A.A. Lotfi, and A. Sohrabi, Credit risk model and rating of legal customers of Keshavarzi Bank, Econ. Res. J. 4 (2017), 128–199. [27] F. Tari and A. R. Ghasemi, Designing the validation model of bank customers and its role in reducing the credit risk of banks (a case study of New Economy Bank), Faculty of Economics, Allameh Tabatabai University, 2012. [28] L.C. Thomas, A survey of credit and behavioral scoring: Forecasting financial risk of lending to consumers, Int. J. Forecast. 16 (2000), no. 2, 149–172. [29] H. Van Greuning and S. Brajovic-Bratanovic, Analyzing Banking Risk: A Framework for Assessing Corporate Governance and Risk Management, World Bank Publications, 2019. [30] T.C. Wilson, Portfolio credit risk(I), Federal Reserve Bank of New York, Econ. Policy Rev. 9 (1997), no. 10, 111–117. [31] J. Wu and Y. L. Chan, Lot-sizing policies for deteriorating items with expiration dates and partial trade credit to credit-risk customers, Int. J. Prod. Econ. 155 (2016), 292-301. [32] C. Zhao, M. Li, and J. Wang, The mechanism of credit risk contagion among internet P2P lending platforms based on a SEIR model with time-lag, Res. Int. Bus. Finance 57 (2021). | ||
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