In the 1920s, the United States of America encountered unusual bank failures that had a ripple effect on other continents, including Africa and Asia. Among the African nations most severely impacted by bank collapses were Zimbabwe, Kenya, Nigeria, and South Africa. An increase in non-performing loans was the primary factor found in each case. In Zimbabwe, this issue arose following its independence in 1980 and has persisted despite multiple interventions by the Central Bank of Zimbabwe to modify lending policies. The overarching study aim is to establish how effective these loan models are in ensuring the sustainable performance of Commercial Banks. To address these concerns, the efficiency of the loan models was examined using a sequential explanatory design using a mixed-method approach. A standardized questionnaire with a 5-point Likert scale was employed for the quantitative phase of data gathering. Using a stratified sampling technique, data were gathered from 406 participants employed by 14 Commercial Banking Institutions in Zimbabwe. Using AMOS v. 24.0 and SPSS version 24.0, this collected data was examined to produce descriptive and inferential statistics. For a more thorough comprehension and explanation of the survey results, a structured interview was utilised in the qualitative phase of the research approach with both current and former chief risk officers. Structural Equation Modelling provided estimates of the strength of all hypothesised relationships, where necessary, whilst Novel was used to analyse qualitative data. The Credit Score model has been embraced by many banks, according to the statistics, while the Z-score model has been employed the least. The results revealed that a loan model’s characteristics can, in certain cases, affect the model’s efficacy throughout the loan cycle. There is a negative correlation between various loan models and theories of credit risk. In addition, non-performing loans result from parameters in the loan model being inadequate; these parameters are typified by old models, unfavourable markets, insufficient cash flows, and improper use of a loan model as the bankers perceived the problem. The findings have implications for commercial banks, one of which is that loan models should be reviewed frequently to manage credit risks resulting from the present business environment.
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Matika, F. T. , Potwana, N. , Ogunsola, S. A. and Dlamini, B. I. (2024). Analysing the Impact of Loan Portfolio Management Models on the Performance of Commercial Banks in Zimbabwe. Open Access Library Journal, 11, e1311. doi: http://dx.doi.org/10.4236/oalib.1111311.
Bolton, P., Cecchetti, S., Danthine, J.P. and Vives, X. (2019) Sound at Last? Assessing a Decade of Financial Regulation. The Future of Banking. CEPR Press.
Olalekan, L.I., Olumide, M.L. and Irom, I.M. (2018) Financial Risk Management and the Profitability: An Empirical Evidence from Commercial Banks in Nigeria. Journal of Management Sciences, 16, 117-137.
Banda, G. (2022) Evolution of Zimbabwe’s Maize Innovation Ecosystems: Building an Institutional Innovation Infrastructure That Supports Food Security. Africa Development, 47, 167-195. https://doi.org/10.57054/ad.v47i3.2679
Kamau, A., Nkaabu, C. and Cherono, V. (2023) Innovation Orientation and Firm Performance: The Role of Organizational Commitment among Commercial Banks in Meru County, Kenya. Human Resource and Leadership, 3, 29-47. https://edinburgjournals.org/journals/index.php/journal-of-human-resource/article/view/170
Alsharari, N.M. (2023) The Interplay of Strategic Management Accounting, Business Strategy and Organizational Change: As Influenced by a Configurational Theory. Journal of Accounting & Organizational Change, 20, 153-176. https://doi.org/10.1108/jaoc-09-2021-0130
Bod'a, M. and Zimková, E. (2021) Overcoming the Loan-to-Deposit Ratio by a Financial Intermediation Measure—A Perspective Instrument of Financial Stability Policy. Journal of Policy Modeling, 43, 1051-1069. https://doi.org/10.1016/j.jpolmod.2021.03.012
Scholtens, B. and van Wensveen, D. (2000) A Critique on the Theory of Financial Intermediation. Journal of Banking & Finance, 24, 1243-1251. https://doi.org/10.1016/s0378-4266(99)00085-0
Allen, F. and Santomero, A.M. (1997) The Theory of Financial Interme-diation. Journal of Banking & Finance, 21, 1461-1485. https://doi.org/10.1016/s0378-4266(97)00032-0
Brei, M., Borio, C. and Gambacorta, L. (2020) Bank Intermediation Activity in a Low‐Interest‐Rate Environment. Economic Notes, 49, e12164. https://doi.org/10.1111/ecno.12164
Madeira, C. (2018) Explaining the Cyclical Volatility of Consumer Debt Risk Using a Heterogeneous Agents Model: The Case of Chile. Journal of Financial Stability, 39, 209-220. https://doi.org/10.1016/j.jfs.2017.03.005
Els, G., Toit, D. E., Erasmus, P., Kotze, L., Ngwenya, S., Thomas, K. and Viviers, S. (2010) Corporate Finance. A South African Perspective. 2nd Edition, Oxford University Press.
Alzeaideen, K. (2019) Credit Risk Management and Business Intelligence Approach of the Banking Sector in Jordan. Cogent Business & Management, 6, Article 1675455. https://doi.org/10.1080/23311975.2019.1675455
Saleh, M.M.A., Alkasasbeh, L.A.M. and Bader, A.A. (2017) The Role of Financial Analysis Tools in Granting Loans. Field Study on Banks Operating within Aqaba Special Economic Zone). Interna-tional Journal of Academic Research in Accounting, Finance and Management Sciences, 7, 75-85. https://doi.org/10.6007/ijarafms/v7-i1/2541
Teddlie, C. and Tashakkori, A. (2009) Foundations of Mixed Research Methods: Integrating Quantitative and Qualitative Approaches in the Social and Behavioural Sciences. 3rd Edition, SAGE Publications.
Abdou, H.A. and Pointon, J. (2011) Credit Scoring, Statistical Techniques and Evaluation Criteria: A Review of the Literature. Intelligent Systems in Accounting, Finance and Management, 18, 59-88. https://doi.org/10.1002/isaf.325
Sabah, N.M. (2022) The Impact of Social Media-Based Collaborative Learning Environments on Students’ Use Outcomes in Higher Education. International Journal of Human-Computer Interaction, 39, 667-689. https://doi.org/10.1080/10447318.2022.2046921
Sheard, J. (2018) Quantitative Data Analysis. In: Williamson, K. and Johanson, G., Eds., Research Methods, Chandos Publishing, 429-452. https://doi.org/10.1016/b978-0-08-102220-7.00018-2
Rosak-Szyrocka, J. and Tiwari, S. (2023) Structural Equation Modeling (SEM) to Test Sustainable Development in University 4.0 in the Ultra-Smart Society Era. Sustain-ability, 15, Article 16167. https://doi.org/10.3390/su152316167
Ugoani, J.N.N. (2015) Poor Bank Liquidity Risk Management and Bank Failures: Nigerian Perspective. Proceedings in Finance and Risk Series, 14, 659-678.
Samreen, A., Zaidi, F.B. and Sarwar, A. (2013) Design and Development of Credit Scoring Model for the Commercial Banks in Pakistan: Forecasting Creditworthiness of Corporate Borrowers. International Journal of Business and Commerce, 2, 1-26.
Desai, V.S., Crook, J.N. and Overstreet, G.A. (1996) A Comparison of Neural Networks and Linear Scoring Models in the Credit Union Environment. European Journal of Operational Research, 95, 24-37. https://doi.org/10.1016/0377-2217(95)00246-4