Financial Factors and Beyond: A Survey of Credit Risk Assessment for VSBs by Moroccan Banks
Abstract
This article analyzes the extent to which Moroccan banks adopt a holistic approach to credit risk assessment, incorporating criteria that go beyond simple balance sheets and financial ratios. To this end, Multiple Correspondence Analysis and advanced statistical techniques were employed using R software. A detailed questionnaire comprising 14 questions, was distributed to a sample of Moroccan bankers. The results of this analysis reliably confirm our initial hypotheses. They reveal a growing trend among Moroccan banks towards a more complex and nuanced assessment of credit risk. In addition to traditional financial aspects such as financial statement analysis, banks are increasingly considering non-financial criteria such as collateral requirements, quality of corporate governance, sustainability of business practices, and technological positioning. This diversified approach gives banks a more accurate and comprehensive view of a company's risk profile. It also enables them to make better-informed and more balanced lending decisions, considering the multitude of factors that can influence a company's ability to repay.
References
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[28] Frame, W. S., Srinivasan, A., and Woosley, L. (2001). The Effect of Credit Scoring on Small-Business Lending. Journal of Money, Credit and Banking, 33(3): 813–825. DOI: https://doi.org/10.2307/2673896
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[31] Khemakhem, S., and Boujelbene, Y. (2018). Predicting credit risk on the basis of financial and non-financial variables and data mining. Review of Accounting and Finance, 17(3): 316–340. DOI:https://doi.org/10.1108/RAF-07-2017-0143
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[36] Mileris, R. (2012). Macroeconomic determinants of loan portfolio credit risk in banks. Engineering Economics, 23(5): 496–504. DOI: https://doi.org/10.5755/j01.ee.23.5.1890
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[40] Partovi, E., and Matousek, R. (2019). Bank efficiency and non-performing loans: Evidence from Turkey. Research in International Business and Finance, 48: 287–309. DOI:https://doi.org/10.1016/j.ribaf.2018.12.011
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[43] Rossi, S. P. S., Schwaiger, M. S., and Winkler, G. (2009). How loan portfolio diversification affects risk, efficiency and capitalization: A managerial behavior model for Austrian banks. Journal of Banking and Finance, 33(12): 2218–2226. DOI: https://doi.org/10.1016/j.jbankfin.2009.05.022
[44] Siddique, A., Khan, M. A., and Khan, Z. (2021). The effect of credit risk management and bank-specific factors on the financial performance of the South Asian commercial banks. Asian Journal of Accounting Research, 7(2): 182–194. DOI: https://doi.org/10.1108/AJAR-08-2020-0071
[45] Sumna, P. (2013). Credit risk dynamics in Czech Republic (Dynamique du risque de crédit en République tchèque). European Scientific Journal, 9(16).
[46] Tarchouna, A., Jarraya, B., and Bouri, A. (2017). How to explain non-performing loans by many corporate governance variables simultaneously? A corporate governance index is built to US commercial banks. Research in International Business and Finance, 42: 645–657. DOI:https://doi.org/10.1016/j.ribaf.2017.07.008
[47] Wickham, H. (2014). Tidy Data. Journal of Statistical Software, 59(10): 1 – 23. DOI:https://doi.org/10.18637/jss.v059.i10
[48] Zhou, K. (2014). The effect of income diversification on bank risk: evidence from China. Emerging Markets Finance and Trade, 50(sup3): 201–213. DOI: https://doi.org/10.2753/REE1540-496X5003S312
[49] Zizi, Y., Jamali-Alaoui, A., El Goumi, B., Oudgou, M., and El Moudden, A. (2021). An Optimal Model of Financial Distress Prediction: A Comparative Study between Neural Networks and Logistic Regression. Risks, 9(11): 200. DOI: https://doi.org/10.3390/risks9110200
[50] Zizi, Y., Oudgou, M., and Moudden, A. El. (2020). Determinants and predictors of smes’ financial failure: A logistic regression approach. Risks, 8(4): 1–21. DOI: https://doi.org/10.3390/risks8040107
[51] Bank Al-Maghrib (2023) Rapport annuel présenté à SM le Roi
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[2] Accornero, M., Cascarino, G., Felici, R., Parlapiano, F., and Sorrentino, A. M. (2018). Credit risk in banks' exposures to non-financial firms. European Financial Management, 24(5): 775–791. DOI:https://doi.org/10.1111/eufm.12138
[3] Alfaro, E., Garcia, N., Gamez, M., and Elizondo, D. (2008). Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks. Decision Support Systems, 45(1): 110–122. DOI:https://doi.org/10.1016/j.dss.2007.12.002
[4] Ali, M., Khattak, M. A., and Alam, N. (2023). Credit risk in dual banking systems: does competition matter? Empirical evidence. International Journal of Emerging Markets, 18(4): 822–844. DOI:https://doi.org/10.1108/IJOEM-01-2020-0035
[5] Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4): 589–609. DOI: https://doi.org/10.2307/2978933
[6] Altman, E. I., Sabato, G., and Wilson, N. (2008). The value of non-financial information in SME risk management. Available at SSRN 1320612. DOI: http://dx.doi.org/10.2139/ssrn.1320612
[7] Altman, E. I. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. The Journal of Finance, 23(4): 589–609. DOI: https://doi.org/10.2307/2978933
[8] Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 71–111. DOI: https://doi.org/10.2307/2490171
[9] Berger, A. N., and Frame, W. S. (2006). Small business credit scoring and credit availability. Journal of Small Business Management, 44(2): 171-192. DOI: https://doi.org/10.1111/j.1540-627X.2007.00195.x
[10] Berger, A. N., and Udell, G. F. (2006). A more complete conceptual framework for SME finance. Journal of Banking and Finance, 30(11): 2945–2966. DOI: https://doi.org/10.1016/j.jbankfin.2006.05.008
[11] Bharadwaj, A., El Sawy, O. A., Pavlou, P. A., and Venkatraman, N. (2007). Digital Business Strategy: Toward the Next Generation of Insights. MIS Quarterly, 37(2): 471-482. DOI:https://www.jstor.org/stable/43825919
[12] Bhatt, T. K., Ahmed, N., Iqbal, M. B., and Ullah, M. (2023). Examining the determinants of credit risk management and their relationship with the performance of commercial banks in Nepal. Journal of Risk and Financial Management, 16(4), 235. DOI: https://doi.org/10.3390/jrfm16040235
[13] Boot, A. W. A., and Thakor, A. V. (1994). Moral hazard and secured lending in an infinitely repeated credit market game. International Economic Review, 35(4): 899-920. DOI: https://doi.org/10.2307/2527003
[14] Boot, A. W. A., and Thakor, A. V. (2000). Can relationship banking survive competition? The Journal of Finance, 55(2): 679-713. DOI: https://doi.org/10.1111/0022-1082.00223
[15] Chaplinska, A. (2012). Evaluation of the borrower's creditworthiness as an important condition for enhancing the effectiveness of lending operations. SHS Web of Conferences, 2, 9. DOI:https://doi.org/10.1051/shsconf/20120200009
[16] Chen, K.-H., and Tsai, T.-Y. (2020). Bankruptcy Study Using Artificial Intelligence. Proceedings of the 2020 4th International Conference on Deep Learning Technologies (ICDLT), 109–112. DOI:https://doi.org/10.1145/3417188.3417199
[17] Cucinelli, D., Di Battista, M. L., Marchese, M., and Nieri, L. (2018). Credit risk in European banks: The bright side of the internal ratings based approach. Journal of Banking and Finance, 93: 213–229. DOI:https://doi.org/10.1016/j.jbankfin.2018.06.014
[18] Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3): 297–334. DOI: https://doi.org/10.1007/BF02310555
[19] DeYoung, R., Glennon, D., and Nigro, P. (2008). Borrower-Lender Distance, Credit Scoring, and Loan Performance: Evidence from Informational-Opaque Small Business Borrowers. Journal of Financial Intermediation, 17(1): 113–143. DOI: https://doi.org/10.1016/j.jfi.2007.07.002
[20] Diamond, D. (1984). Financial Intermediation and Delegated Monitoring. Review of Economic Studies, 51: 393-414. DOI: https://doi.org/10.2307/2297430
[21] Diamond, D. W. 1991. Monitoring and reputation: The choice between bank loans and directly placed debt. Journal of Political Economy, 99: 689-721. DOI: https://doi.org/10.1086/261775
[22] Donovan, J., Jennings, J., Koharki, K., and Lee, J. (2021). Measuring credit risk using qualitative disclosure. Review of Accounting Studies, 26: 815–863. DOI: https://doi.org/10.1007/s11142-020-09575-4
[23] Du, G., Liu, Z., and Lu, H. (2021). Application of innovative risk early warning mode under big data technology in Internet credit financial risk assessment. Journal of Computational and Applied Mathematics, 386, 113260. DOI: https://doi.org/10.1016/j.cam.2020.113260
[24] Ghanem, Y., and Achouche, M. (2016). Développement Des Systèmes Financiers Quelle Réalité Pour Les Pays De La Zone Mena. Roa Iktissadia Review, (11). DOI: https://doi.org/10.12816/0036799
[25] Greuning H. V. and Bratanovic S. B. (2004). Analyse et gestion du risque bancaire: un cadre de référence pour l’évaluation de la gouvernance d’entreprise et du risque financier, traduction de Rozenbaum M., Edition Eska, Paris, 384 p.
[26] Godlewski, C. J. (2005a). Information, organisation et prise de risque dans la banque. Thèse de Doctorat en Sciences de Gestion, Université Robert Schuman Strasbourg, 220p.
[27] Gupta, R. (2023). Financial determinants of corporate credit ratings: Indian evidence. International Journal of Finance and Economics, 28(2): 1622–1637. DOI: https://doi.org/10.1002/ijfe.2497
[28] Frame, W. S., Srinivasan, A., and Woosley, L. (2001). The Effect of Credit Scoring on Small-Business Lending. Journal of Money, Credit and Banking, 33(3): 813–825. DOI: https://doi.org/10.2307/2673896
[29] Jolliffe, I. T. (2002). Principal Component Analysis. Springer Series in Statistics. New York: Springer. DOI:https://doi.org/10.1002/0470013192.bsa501
[30] Jöreskog, K. G. (1969). A General Approach to Confirmatory Maximum Likelihood Factor Analysis. Psychometrika, 34(2): 183–202. DOI: https://doi.org/10.1007/BF02289343
[31] Khemakhem, S., and Boujelbene, Y. (2018). Predicting credit risk on the basis of financial and non-financial variables and data mining. Review of Accounting and Finance, 17(3): 316–340. DOI:https://doi.org/10.1108/RAF-07-2017-0143
[32] Kuh, G. D., and Whitt, E. J. (1988). The Invisible Tapestry: Culture in American Colleges and Universities. ASHE-ERIC Higher Education Report No. 1. Washington, DC: Association for the Study of Higher Education.
[33] Levine, R. (2005). Finance and Growth: Theory and Evidence. In Handbook of Economic Growth (Vol. 1, pp. 865-934). Elsevier.
[34] Louzis, D. P., Vouldis, A. T. and Metaxas, V. L. (2012). Macroeconomic and bank-specific determinants of non-performing loans in Greece: A comparative study of mortgage, business and consumer loan portfolios. Journal of Banking and Finance, 36: 1012-1027. DOI: https://doi.org/10.1016/j.jbankfin.2011.10.012
[35] Mehmood, A., and De Luca, F. (2023). Financial distress prediction in private firms: developing a model for troubled debt restructuring. Journal of Applied Accounting Research. DOI: https://doi.org/10.1002/ijfe.2497
[36] Mileris, R. (2012). Macroeconomic determinants of loan portfolio credit risk in banks. Engineering Economics, 23(5): 496–504. DOI: https://doi.org/10.5755/j01.ee.23.5.1890
[37] Modigliani, F., and Miller, M. H. (1958). The Cost of Capital, Corporation Finance, and the Theory of Investment. American Economic Review, 48(3): 261-297. http://www.jstor.org/stable/1809766.
[38] Naili, M., and Lahrichi, Y. (2022). The determinants of banks’ credit risk: Review of the literature and future research agenda. International Journal of Finance and Economics, 27(1): 334–360. DOI:https://doi.org/10.1002/ijfe.2156
[39] Nunnally, J. C., and Bernstein, I. H. (1994). Psychometric Theory (3rd ed.). New York: McGraw-Hill.
[40] Partovi, E., and Matousek, R. (2019). Bank efficiency and non-performing loans: Evidence from Turkey. Research in International Business and Finance, 48: 287–309. DOI:https://doi.org/10.1016/j.ribaf.2018.12.011
[41] Pierandrei, L. (2015). Risk Management, Gestion des risques en entreprise, banque et assurance, Paris, Dunod, 320p. 2015.
[42] Rajan, R. G., and Zingales, L. (1995). What Do We Know about Capital Structure? Some Evidence from International Data. The Journal of Finance, 50(5): 1421-1460. DOI: https://doi.org/10.1111/j.1540-6261.1995.tb05184.x
[43] Rossi, S. P. S., Schwaiger, M. S., and Winkler, G. (2009). How loan portfolio diversification affects risk, efficiency and capitalization: A managerial behavior model for Austrian banks. Journal of Banking and Finance, 33(12): 2218–2226. DOI: https://doi.org/10.1016/j.jbankfin.2009.05.022
[44] Siddique, A., Khan, M. A., and Khan, Z. (2021). The effect of credit risk management and bank-specific factors on the financial performance of the South Asian commercial banks. Asian Journal of Accounting Research, 7(2): 182–194. DOI: https://doi.org/10.1108/AJAR-08-2020-0071
[45] Sumna, P. (2013). Credit risk dynamics in Czech Republic (Dynamique du risque de crédit en République tchèque). European Scientific Journal, 9(16).
[46] Tarchouna, A., Jarraya, B., and Bouri, A. (2017). How to explain non-performing loans by many corporate governance variables simultaneously? A corporate governance index is built to US commercial banks. Research in International Business and Finance, 42: 645–657. DOI:https://doi.org/10.1016/j.ribaf.2017.07.008
[47] Wickham, H. (2014). Tidy Data. Journal of Statistical Software, 59(10): 1 – 23. DOI:https://doi.org/10.18637/jss.v059.i10
[48] Zhou, K. (2014). The effect of income diversification on bank risk: evidence from China. Emerging Markets Finance and Trade, 50(sup3): 201–213. DOI: https://doi.org/10.2753/REE1540-496X5003S312
[49] Zizi, Y., Jamali-Alaoui, A., El Goumi, B., Oudgou, M., and El Moudden, A. (2021). An Optimal Model of Financial Distress Prediction: A Comparative Study between Neural Networks and Logistic Regression. Risks, 9(11): 200. DOI: https://doi.org/10.3390/risks9110200
[50] Zizi, Y., Oudgou, M., and Moudden, A. El. (2020). Determinants and predictors of smes’ financial failure: A logistic regression approach. Risks, 8(4): 1–21. DOI: https://doi.org/10.3390/risks8040107
[51] Bank Al-Maghrib (2023) Rapport annuel présenté à SM le Roi
[52] Haut-Commissariat au Plan. 2018. Note D’information Relative aux Comptes Régionaux de L’année 2018. Casablanca: Haut-Commissariat au Plan.
[53] Haut-Commissariat au Plan. 2019. Enquête Nationale Auprès des Entreprises, Premiers Résultats 2019. Casablanca: Haut-Commissariat au Plan.
[54] Inforisk. 2024. Étude Inforisk, Défaillances Maroc 2023. Casablanca: Inforisk.
Published
2024-09-30
How to Cite
KHANCHAOUI, Youssef; ZIZI, Youssef; EL MOUDDEN, Abdeslam.
Financial Factors and Beyond: A Survey of Credit Risk Assessment for VSBs by Moroccan Banks.
Theoretical and Practical Research in Economic Fields, [S.l.], v. 15, n. 3, p. 695 - 708, sep. 2024.
ISSN 2068-7710.
Available at: <https://journals.aserspublishing.eu/tpref/article/view/8597>. Date accessed: 21 dec. 2024.
doi: https://doi.org/10.14505/tpref.v15.3(31).14.
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