Economics and Business
ISSN 2775-9237 (Online)
Published: 19 August 2018
Credit Risk in Microfinance Institutions: Empirical Evidence from Accra Metropolis of Ghana
Nicholas Oppong-Mensah, Edward Yeboah, Benjamin Korley Amartey
University of Energy and Natural Resources (Ghana), Kwame Nkrumah University of Science and Technology (Ghana)
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This study investigates the credit risk in the Microfinance Industry in Ghana using Microfinance Institutions (MFIs) in Accra Metropolis as the test case. The study used the loan default rate as a proxy variable to measure credit risk and examined the effect of some explanatory variables on loan default. Primary data was used, and the purposive sampling techniques were adopted to select 90 respondents from 20 Microfinance Institutions out of 43. The multivariate linear regression model was used to analyze the relationship between the dependent and explanatory variables. The results indicated that interest rates have a positive and significant effect on loan default whereas loan maturity period has a negative and significant effect on loan default. Also, Credit Officers' educational level have a negative and significant effect on loan default while having a marketing department has a positive and significant effect on loan default. However, the loan appraisal process, lending gap, and governance quality have no significant effect on loan default. Thus, MFIs should promote sound loan pricing policies in order to charge the appropriate interest rate and adopt loan repayment regimes that boost liquidity. Additionally, Credit Officers should be highly educated, and hence management of MFIs should put in place continuous development programs to upgrade the skills of all personnel in the credit delivery system in relation to best practices in lending.
Ahmed, S. F and Malik, Q.A. (2015).Credit management and Loan performance; Empirical investigation on microfinance Banks of Pakistan. International Journal of Economics and Financial Issues, 5(2), 574-579.
Awunyo-Vitor, D (2013). Determinants of Loan Repayment Default among Farmers in Ghana. African Journal of Economics, Vol. 1(3), Pp. 071-076.
Derban, W.K., Binner, J.M., and Mullineux, A. (2005). Loan repayment performance in community development finance institutions in the UK. Small Business Economics, 25, 319-332.
Kiplimo, K.S, and [G1] Kalio, A.M (2014). Influence of Credit Risk Management Practices on Loan Performance of Microfinance Institutions in Baringo County. International Journal of Science and Research (IJSR), Volume 3 Issue 10, Pp. 2260-2267.
Klein, N. (2013). Non-Performing Loans in CESEE; Determinants and Impact on Macroeconomic Performance.IMF Working Papers 13/72.
Kohansal M. R., Mansoori, H. (2009). Factors affecting loan repayment performance of farmers in Kharasan- Razavi province of Iran. A paper presented in a conference on International Research on Food Security. Natural Resource Management and Rural Development, University of Hamburg, October 6-8, 2009.
Maini, J.N., Kinyaririo, D.K and Muturi, H.M (2016). Influence of Credit Risk Management Practices on Loan Delinquency in Savings and Credit Cooperative Societies in Meru County, Kenya. International Journal of Economics, Commerce, and Management, Vol. IV, Issue 2, Pp. 763-773.[G2]
Moti, H.O., Masinde, J.S., Galo, M.N and Sindani, M.N. (2012). “Effectiveness of Credit Management System on Loan Performance: Empirical Evidence from Micro Finance Sector in Kenya” International Journal of Business, Humanities, and Technology[G3] , 2(6).
Mutambanadzo, T., Bhiri, T., and Makunike, S. (2013).An analysis challenges faced by Zimbabwean microfinance institutions in providing financial services to the poor and informal sector in the dollarized regime.Global Journal of Commerce and Management Perspectives, Vol. 2(3), 154-159.
Mwengei, K.B.O. (2013). Assessing the factors contributing to non-performing loans in Kenyan banks. European Journal of Business Management, 5(32), 155-163.
Statistics Solutions.(2005). Assumptions and considerations for regression. Retrieved December 22, 2005 from www.statisticsolutions.com/Multiple_Regression.htm