DEBT RECOVERY TECHNIQUES ON LOAN PERFOMANCE OF DEPOSIT TAKING MICROFINANCE INSTITUTIONS IN MOMBASA COUNTY
Abstract
The purpose of the study was to investigate the debt recovery techniques on loan performance of deposit taking MFIs in Mombasa County. The theories guiding the study were moral hazard theory, Value Based Portfolio Theory, credit market theory and loanable funds theory. The study used a cross-sectional descriptive research design. The target population of the study was management of the 6 deposit taking MFIs which were licensed by CBK and had fully-pledged branches in Mombasa. The study employed stratified sampling technique whereby the target population was divided into different groups and those with similar characteristics were grouped in the same stratum then sample for the study was selected at random from each stratum. The study employed Yamane formula to derive a sample of 58 respondents. The study used primary and secondary data. A structured questionnaire was used to collect the primary data. Collected data was quantitatively analyzed by use of Statistical Package for Social Science (SPSS) version 26 as the data analysis tool. The data analysis techniques used were descriptive statistics, correlation analysis and multiple regression analysis. Analyzed data was presented in tables and charts for ease of interpretation. The findings revealed that adverse credit listing, collection policy, loan limit reduction and fines & penalties have significant effect on loan performance. The microfinance repossesses security provided to acquire loan from the borrowers. Also the microfinance undertakes auction of the borrower’s assets to minimize loan loss and that the microfinance requires the guarantor to settle the loan amount due in case of default by borrower. The study concluded that deposit taking micro finance performs credit scoring on the borrower before issuing approval for loans. In many cases, the deposit taking MFI collaborates with credit bureaus closely through information sharing. The deposit taking MFI, uses adverse credit listing to improve the precision of the signal about the quality of potential borrower and the information shared by the credit bureaus offers MFI imprecise knowledge of a borrower’s likelihood of repaying. The study recommended that the management of MFIs should make use of private collection agents to help the institutions recover outstanding loan amounts from the non-paying borrowers. The use of these private collectors has a potential to recover the bad debts as they have unique deterrence measures which are not allowed in the MFIs sector.
Key Words: Fines and Penalties, Adverse Credit Listing, Loan Limit Reduction, Collection Policy
CITATION: Ganzallah, F. S., & Wekesa, M. (2023). Debt recovery techniques on loan performance of deposit taking microfinance institutions in Mombasa County. The Strategic Journal of Business & Change Management, 10 (2), 1069 – 1083.
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Altendorf, A., & Schreiber, J. (2016). Assistive technology in dementia care: methodological issues in research design. Journal of Assistive Technologies, 9(1), 23-51.
Altman, E., & Sironi, A. (2016). Credit Risk Modelling: A Review of the Literature and Empirical Evidence. Risk Management, 5(1), 56-71.
Kipsang B, (2020), Effects of debt recovery strategies on loan performance of Fintech Companies in Kenya, United State International University.
Ogola, B. A. (2016), Debt recovery as an operational strategy used by NIC bank to manage Non Performing Loan portfolio. Nairobi: University of Nairobi.
Baloro, J. (2018). African Responses to the Debt Crisis: The Relevance of Public International Law. Savings and Development, 24(1), 239-267.
Brealey, R. & Myers, S. (2016). Principles of corporate finance (8th edition), London: McGraw-Hill.
Brownbridge, M. (2016), The Cause of Financial Distress in Local Banks in Africa and Implications for Prudential Policy. UNCTAD/OSG/DP/132
Chamboko, R., & Bravo, J. (2016). On the modelling of prognosis from delinquency to normal performance on retail consumer loans. Risk Management, 18(4), 264-287.
Chan, T. K., & Abdul-Aziz, A.-R. (2017). Financial performance and operating strategies of Malaysian property development companies during the global financial crisis. Journal of Financial Management of Property and Construction, 22(2), 45-78.
Chava, S. (2014). Environmental Externalities and Cost of Capital. Management Science, 60(9), 23-47.
David, M. (2018). Effects of Mobile-Based Lending Process on Non-Performing Loans in Commercial Banks in Nakuru Town. Nairobi: JKUAT.
Dubois & Anderson (2010), Managing household debts: Social service provision in the EU. Dublin: European Foundation for the Improvement of Living and Working Conditions.
Kamar, H., & Ayuma, C. (2016). Effect of Debt Recovery Techniques on Performance of Selected Financial Institutions in Eldoret Town. International Journal of Humanities and Social Science Invention, 1-15.
Kavassalis, P., & Stieber, H. (2018). An innovative Reg Tech approach to financial risk monitoring and supervisory reporting. The Journal of Risk Finance, 19(1), 45-67.
Kawalec (2002), Different Models of Debt Restructuring, ‘Observations based on experiences with bad debt difficulties in industrialized countries and Central European transition economies. Presentation at the International Seminar on Comparative Experiences in Addressing Banking Sector Issues in Central/ Eastern Europe and Central Asia.
Ketchen, D. J., Bergh, D., & Boyd, B. (2019). The Research Design Canvas: A Tool for Creating Better Studies. Research Methodology in Strategy and Management, 11(1), 56-91.
Kipsang, B. (2020), Effect on debt recovery strategies on loan performance of FinTech firms in Kenya. Unpublished Thesis, United States International University-Africa, Kenya.
Kisala, P. (2014). The Effect of Credit Risk Management Practices on Loan Performance. Nairobi: University of Nairobi.
Lough, W. H. (2016). Business Finance, a Practical Study of Financial Management in Private Business Concerns. United Kingdom Retrieved from: http://chestofbooks.com/finance/private/Business/Factors-Considered-By-Banks-In-Making-Loans.htm
Luoto, J., McIntosh, C., & Wydick, B. (2017). Credit Information Systems in Less Developed Countries: A Test with Microfinance in Guatemala. Economic Development and Cultural Change, 55(2), 313-334
Markowitz, H.M. (1959). Portfolio Selection: Efficient Diversification of Investments. New York: John Wiley & Sons
Mawele, N. (2020), Assessment of debt recovery in banks. A case of Zanaco in Zambia. Unpublished thesis, Cavendish University, Zambia.
Mniwasa, E. E. (2014). The financial intelligence unit and money laundering control in Tanzania: The law, potential and challenges. Journal of Money Laundering Control, 14(2), 23-65.
Mohamed, H. S. (2017). Factors Affecting Debt Recovery in Commercial Banks In Kenya a Case Study at Equity Bank Kenya Limited. Imperial Journal of Interdisciplinary Research (IJIR), 3(10), 54-64.
Sharma, A. J. (2016). When debt comes knocking. Emerald Emerging Markets Case Studies, 6(4), 76-121.
Muturi, E., & Rotich, G. (2016). Effect of Credit Management Practices on Loan Performance in Deposit Taking Microfinance Banks in Kenya. International Journal of Innovations, Business and Management (IJIBM), 10(1).
Nyawira, I. (2019), Debtors management and financial performance of Microfinance Institutions in Kenya. Unpublished Thesis, Kenyatta University, Kenya.
Owich, M. A. (2021), Debt management and loan performance of commercial banks in Kenya. International Academic Journal of Economics and Finance, 3(7), pp. 42-71
Ping, L., (2017). “China Banking Regulatory Commission,” New Delhi, November 14- 16, [Online]http://www.imf.org
Visaria, S., (2016). Legal Reform and Loan Repayment: The Micro Economic Impact of Debt Recovery Tribunal in India. American Economic Journal: Applied Economics Retrieved from: http://www.jstor.org/discover/10.2307/25760171?uid=3737696&uid=2&uid=4&sid=47 698992983537
DOI: http://dx.doi.org/10.61426/sjbcm.v10i2.2651
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