A integrated hybrid fuzzy multiple-criteria decision-making model for non-performing Loans collections in the banking system (Case study: Shahr Bank)
محورهای موضوعی : Multi-Criteria Decision Analysis and its Application in Financial Management
kiamars fathi
1
(Department of Industrial Management, South Tehran Branch, Islamic Azad University, Tehran, Iran)
Majid Rashidi
2
(Department of Industrial management, Kish International Branch, Islamic Azad University, Kish Island, Iran)
Mahmoud Modiri
3
(Department of Industrial Management, South Tehran Branch, Islamic Azad University, Tehran, Iran)
Sayedeh Mahboubeh Jafari
4
(Department of Accounting, South Tehran Branch, Islamic Azad University, Tehran, Iran)
کلید واژه: Non-performing loans, Fuzzy multiple-criteria decision-making, Bank,
چکیده مقاله :
The increase in non-performing loans considerably reduces profit in the banking systems. To solve these problems, factors influencing the collection of non-performing loans have to be examined in a hopeful attempt to reduce it to an ac-ceptable level. The present study is conducted to analyze and study factors influ-encing the collection of non-performing loans in Shahr Bank. This research is an applied study regarding its goal. It was conducted in two sections, namely the qualitative and quantitative sections. In the qualitative sections, the factors influ-encing the collection of receivables were identified using the theoretical literature and interviews with senior managers of Shahr Bank through the encoding process. In the quantitative section, data was collected by surveying the opinions of 12 experts, including senior managers of Shahr Bank in 2020 using a questionnaire. Thereafter, the factors were selected using the fuzzy Delphi technique and the relationships between them were determined using the fuzzy DEMATEL method. Finally, the factors were weighted and prioritized using the fuzzy ANP method. The research findings showed that non-performing loans can be collected through six types of factors including organizational, regulatory, customer, banking, envi-ronmental, and operational factors. The environmental factor is the most influential factor, while the operational factor is the most influenced and most important factor. In addition, behavioural, contextual, and structural sub-factors have the highest level of importance in the collection of non-performing loans in the order mentioned. These findings can help bank managers make decisions to improve the collection of receivables.
[1] Khorramin, M., Talebnia, G., Ranjbar, M., Amiri, A., Ranking the Efficiency and Soundness of Business Banks Using a Combined Method of Data Envelopment Analysis and Fuzzy VIKOR, Advances in Mathematical Finance and Applications, 2021, 6(3), P. 425-439. Doi: 10.22034/amfa.2020.1874697.1267.
[2] Balouei, E., Anvary Rostamy, A., Sadeghi Sharif, S., Saeedi, A., The Impacts of Financial Structure on Financial Performance of Banks listed in Tehran Stock Exchange: An Empirical Application, Advances in Mathematical Finance and Applications, 2018, 3(3), P. 11-26. Doi: 10.22034/amfa.2018.544946.
[3] Radivojević, N., Cvijanović, D., Sekulic, D., Pavlovic, D., Jovic, S., Maksimović, G., Econometric model of non-performing loans determinants, Physica A: Statistical Mechanics and Its Applications, 2019, 520, P. 481-488. Doi: 10.1016/j.physa.2019.01.015.
[4] Tarchouna, A., Jarraya, B., and Bouri, A., Shadow prices of non-performing loans and the global financial crisis: Empirical evidence from US commercial banks, Journal of Risk Finance, 2019, 20(5), P. 411-434.
Doi: 10.1108/JRF-03-2018-0030.
[5] Arora, N., Arora, N. G., Kanwar, K., Non-performing assets and technical efficiency of Indian banks:a meta-frontier analysis, Benchmarking: An International Journal, 2018, 25(7), P. 2105-2125. Doi: 10.1108/BIJ-03-2017-0040.
[6] Jayaraman, T.K., Lee, C.-Y., Ng, C.-F., The Causal Factors behind Rising Non-performing Assets of India’s Commercial Banks: A Panel Study, Advances in Pacific Basin Business, Economics and Finance (Advances in Pacific Basin Business, Economics and Finance, 2019, 7, P.201-212. Doi:10.1108/S2514-465020190000007008.
[7] Lafuente, E., Vaillant, Y., Vendrell-Herrero, F., Conformance and performance roles of bank boards: The connection between non-performing loans and non-performing directorships, European Management Journal, 2019, 33(1), P. 2–15. Doi: 10.1016/j.emj.2019.04.005.
[8] Esfandiar, M., Saremi, M., Jahangiri Nia, H., Assessment of the efficiency of banks accepted in Tehran Stock Exchange using the data envelopment analysis technique, Advances in Mathematical Finance and Applications, 2018, 3(2), P: 1-11. Doi: 10.22034/amfa.2018.540815
[9] Mehrabian, A., Seifipour, R., Pathology of current receivables in the Iranian banking system, Financial Economics, 2016, 10(136), P. 73-86. (In Persian)
[10] Sharifi, S., Haldar, A., Rao, S. V. D. N., The relationship between credit risk management and non-performing assets of commercial banks in India, Managerial Finance, 2019, 45(3), P. 399-412. Doi: 10.1108/MF-06-2018-0259.
[11] Piatti, D., Cincinelli, P., Does the threshold matter? The impact of the monitoring activity on non-performing loans, Managerial Finance, 2019, 45(2), P. 190-221. Doi: 10.1108/MF-02-2018-0077.
[12] Podpiera, J., Weill, L., Bad luck or bad management? Emerging banking market experience, Journal of Financial Stability, 2008, 4(2), P. 135-148. Doi: 10.1016/j.jfs.2008.01.005
[13] AlKhouri, R., Arouri, H., The effect of diversification on risk and return in banking sector: Evidence from the Gulf Cooperation Council countries, International Journal of Managerial Finance, 2019, 15(1), P. 100-128. Doi: 10.1108/IJMF-01-2018-0024.
[14] Khan, M.A., Siddique, A. and Sarwar, Z., Determinants of non-performing loans in the banking sector in developing state, Asian Journal of Accounting Research, 2020, 5(1), P. 135-145. Doi: 10.1108/AJAR-10-2019-0080
[15] CEIC, 2019, https://www.ceicdata.com/en/indicator/iran/non-performing-loans-ratio. Updated on 28 Nov 2019.
[16] Oynaka, N. N., Factors Affecting Non-Performing Loans in Commercial Banking Sector: A Comparative Study of Public and Private Banks a Case Study of Commercial Bank of Ethiopia and Dashen Bank District in Southern Region of Ethiopia, Research Journal of Finance and Accounting, 2019, 10(3), P.50-61. Doi: 10.7176/rjfa/10-3-07.
[17] Baudino, P., Yun, H., Resolution of non-performing loans – policy options. Bank for International Settlements. on policy implementation, FSI Insights, 2017, P. 5–35.
[18] Manz, F, Determinants of non-performing loans: What do we know? A systematic review and avenues for future research. Management Review Quarterly, 2019, 69(1), P.389-401. Doi: 10.1007/s11301-019-00156-7.
[19] Barongo, W. M., Factors Contributing to Nonperforming Loans in Non-Banking Institutions in Tanzania: A Case of National Security Fund, M.A. thesis, The Open University of Tanzania, Tanzania, 2013.
[20] Barazzetti, A., Iorio, A. D., Fintech: The Recovery Activity for Non-Performing Loans. Information and Communication Technologies (ICT) in Economic Modelling, 2019, P. 117–128. Doi: 10.1007/978-3-030-22605-3-7.
[21] Argaw, S.A., Factors Affecting Non-Performing Loans: In Case of Commercial Bank of Ethiopia. M.A. thesis, Mekelle University, Ethiopia, 2016.
[22] Anastasiou, D., Management and Resolution Methods of Non-Performing Loans: A Review of the Literature. SSRN Electronic Journal, 2016, P. 1-28. Doi: 10.2139/ssrn.2825819.
[23] Yang, C. C., Reduction of non-performing loans in the banking industry: an application of data envelopment analysis. J Bus Econ Manag, 2017, 18(5), P. 411-434 Doi: 10.3846/16111699.2017.1358209
[24] Arrina Rachman, R., Berenika Kadarusman, Y., Anggriono, K., Setiadi, R., Bank-specific Factors Affecting Non-Performing Loans in Developing Countries: Case Study of Indonesia. Journal of Asian Finance, Economics and Business, 2018, 5(2), P. 833-851. Doi: 10.13106/jafeb.
[25] Arham, N., Salisi, M. S., Mohammed, R. U., Tuyon, J., Impact of macroeconomic cyclical indicators and country governance on bank non-performing loans in Emerging Asia, Eurasian Economic Review, 2020, 10, P. 707–726. Doi: 10.1007/s40822-020-00156-z.
[26] Berti, K., Engelen, C., Vasicek, B., A macroeconomic perspective on non-performing loans (NPLs), Quarterly Report on the Euro Area (QREA), 2017, 16(1), P. 7–21.
[27] Amin, A. S., Imam, M. O., Malik, M., Regulations, Governance, and Resolution of Non-Performing Loan: Evidence from an Emerging Economy, Emerging Markets Finance and Trade, 2019, 55(10), P. 2275-2297.
Doi: 10.1080/1540496X.2018.1523788.
[28] Kumar, R. R., Stauvermann, P. J., Patel, A., Prasad, S. S., Determinants of non-performing loans in banking sector in small developing island states: a study of Fiji, Accounting Research Journal, 2018, 31(2), P.192-213. Doi: 10.1108/ARJ-06-2015-0077.
[29] Olivares-Caminal, R., Miglionico, A., Non-performing Loans: Challenges and Options for Banks and Corporations. In: Monokroussos P., Gortsos C. (eds) Non-Performing Loans and Resolving Private Sector Insolvency, Palgrave Macmillan Studies in Banking and Financial Institutions, Palgrave Macmillan, Cham, 2017, P. 17-45. Doi: 10.1007/978-3-319-50313-4.
[30] Adegboye, A., Ojeka, S., Adegboye, K., Corporate governance structure, Bank externalities and sensitivity of non-performing loans in Nigeria, Cogent Economics and Finance, 2020, 8(1), P.1816611.
Doi: 10.1080/23322039.2020.1816611.
[31] Mohamad, A., Jenkins, H., Corruption and banks’ non-performing loans: empirical evidence from MENA countries, Macroeconomics and Finance in Emerging Market Economies, 2021, 14(3), P.1–14.
Doi: 10.1080/17520843.2020.1842478.
[32] Campbell, A., Bank insolvency and the problem of non-performing loans. Journal of Banking Regulation, 2007, 9(1), P. 25–45. Doi: 10.1057/palgrave.jbr.2350057.
[33] Adewusi, A. O., Oyedokun, T. B., Bello, M. O., Application of artificial neural network to loan recovery prediction, International Journal of Housing Markets and Analysis, 2016, 9(2), P. 222–238. Doi: 10.1108/IJHMA-01-2015-0003.
[34] kordmanjiri, S., dadashi, I., Khoshnood, Z., gholamnia roshan, H., Identifying Factors Affecting Non-curent Debts of Banks Using Neural Networks and Support Vector Machine Algorithm. Economic Modeling, 2020, 14(49), P. 127-151. (In Persian). Doi: 10.30495/ECO.2020.672520.
[35] Ebrahimzadeh, M., Investigating the effective factors in collecting past due and overdue receivables of banks (Case study: Mehr Eghtesad Bank branches in the west of Mazandaran province), Quarterly Journal of New Banking Studies, 2009, 2(2), P. 289-312. (In Persian).
[36] Mahaei, A., Application of AHP in identifying and ranking the factors affecting overdue receivables in National Bank of Mazandaran province. M.A. thesis, Amol Non-Profit Higher Education Institute, Amol, Iran, 2017 (In Persian).
[37] Mohammadzadeh, A., Ataei, M., Salimi, H., Identifying and Prioritizing the Obstacles Leading to Bank Overdue, Using DEMATEL and VIKOR, Journal of Development and Evolution Mnagement, 2014, 16, P. 15-26. (In Persian).
[38] Ben Saada, M., The impact of control quality on the non-performing loans of Tunisian listed banks, Managerial Auditing Journal, 2018, 33(1), P. 2–15. Doi: org/10.1108/MAJ-01-2017-1506.
[39] Partovi, E., Matousek, R., Bank Efficiency and Non-Performing Loans: Evidence from Turkey, Research in International Business and Finance, 2019, 48, P. 287-309. Doi: 10.1016/j.ribaf.2018.12.011.
[40] Mirpourian, S., Caragliu, A., Di Maio, G., Landoni, P., & Rusinà, E., Determinants of loan repayment performance among borrowers of microfinance institutions: Evidence from India, World Development Perspectives, 2016, 1, P. 49–52. Doi: 10.1016/j.wdp.2016.06.002.
[41] Sufiyan, M., Haleem, A., Khan, SH., Khan, M. I., Evaluating food supply chain performance using hybrid fuzzy MCDM technique, Sustainable Production and Consumption, 2019, 20, P. 40–57. Doi: 10.1016/j.spc.2019.03.004.
[42] Dinçer, H., Yüksel, S., Martínez, L., Interval type 2-based hybrid fuzzy evaluation of financial services in E7 economies with DEMATEL-ANP and MOORA methods, Applied Soft Computing, 2019, 79, P. 186-202.
Doi: 10.1016/j.asoc.2019.03.018.
[43] Chen, Z., Ming, X., Zhang, X., Yin, D., Sun, Z., Chen, Z., Sun, Z., A rough-fuzzy DEMATEL-ANP method for evaluating sustainable value requirement of product service system, Journal of Cleaner Production, 2019, 228, P. 485-508. Doi: 10.1016/j.jclepro.2019.04.145.
[44] Lin, S.-H., Zhao, X., Wu, J., Liang, F., Li, J.-H., Lai, R.-J., Tzeng, G.-H., An evaluation framework for developing green infrastructure by using a new hybrid multiple attribute decision-making model for promoting environmental sustainability, Socio-Economic Planning Sciences, 2020, 75, P.100909. Doi: 10.1016/j.seps.2020.100909.
[45] Shen, K.-Y., Hu, S.-K., Tzeng, G.-H., Financial modeling and improvement planning for the life insurance industry by using a rough knowledge based hybrid MCDM model, Information Sciences, 2017, 375, P. 296–313. Doi: 10.1016/j.ins.2016.09.055.
[46] Shen, K.-Y., Tzeng, G.-H., A new approach and insightful financial diagnoses for the IT industry based on a hybrid MADM model, Knowledge-Based Systems, 2015, 85, P. 112–130.
Doi: 10.1016/j.knosys.2015.04.024.
[47] Shen, K.-Y., Yan, M.-R., Tzeng, G.-H., Combining VIKOR-DANP model for glamor stock selection and stock performance improvement, Knowledge-Based Systems, 2014, 58, P. 86–97.
Doi: 10.1016/j.knosys.2013.07.023.
[48] Wu, H.-Y., Tzeng, G.-H., Chen, Y.-H., A fuzzy MCDM approach for evaluating banking performance based on Balanced Scorecard, Expert Systems with Applications, 2009, 36(6), P. 10135–10147. Doi: 10.1016/j.eswa.2009.01.005.
[49] Hu, S., Lu, M.-T., Tzeng, G.-H., Improving Mobile Commerce Adoption Using a New Hybrid Fuzzy MADM Model, International Journal of Fuzzy Systems, 2015, 17(3), P. 399–413. Doi: 10.1007/s40815-015-0054-z.
