Credit Management Practices and Bad Debt Levels of Microfinance Institutions in Nairobi City County, Kenya
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Date
2025-10
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Kenyatta University
Abstract
Between the years 2018 to 2021, the bad debt levels of MFIs in Nairobi City County, Kenya have been increasing by 12.46% annually. The increasing bad debt levels have negatively affected MFIs’ operations and their profits to the extent of some being declared bankrupt. The general objective of the study is to establish the effect of credit management practices on bad debt levels of microfinance institutions in Nairobi City County, Kenya. The specific objectives of the study include to evaluate the effect of credit risk identification on bad debt levels of microfinance institutions in Nairobi City County, Kenya, to assess the effect of credit risk monitoring on bad debt levels of microfinance institutions in Nairobi City County, Kenya, to assess the effect of collection policies on the bad debt levels of microfinance institutions in Kenya, to establish the effects of credit appraisal policies on the bad debt levels of microfinance institutions in Nairobi City County, Kenya, and to determine the effect of CBK regulations on bad debt levels of microfinance institutions in Nairobi City County, Kenya. The theories underpinning this study include; anticipated income theory, modern portfolio theory (MPT), capital asset pricing model (CAPM), credit risk theory, PRISM model of credit risk management, and public interest theory. The study employed descriptive research design with a target population of 13 active microfinance institutions based in Nairobi City County, Kenya. A sample size of 13 microfinance institutions was selected through census. Both secondary (for bad debt levels) and primary data for credit management practices was collected. Secondary data was collected from journal articles, books, universities repositories for unpublished dissertation and documentary letters. The data collection sheets and questionnaires were administered to the microfinance institutions middle and senior employees such as credit managers, finance analysts, accounts and debt portfolio assistants through the drop and pick technique. The data analysis and entry were done using the SPSS (Statistical Package for Social Science) software. The diagnostic tests that were carried out include normality, multicollinearity, heteroscedasticity, stationarity, autocorrelation, and model specification The ethical considerations that were employed in the study include anonymity were assured; responses were used purely for academic purposes and treated with confident. To determine the reliability and validity of the data instruments a pilot test was conducted. The data collected from different respondents was sorted, cleaned, coded, and analyzed using SPPS software version 29. The data was analyzed using descriptive statistics and diagnostic statistics such as normality, multicollinearity, heteroscedasticity, and the Hausman tests and inferential statistics including correlation analysis, regression analysis, and hypothesis testing. The study established that despite many microfinance institutions developing and implementing credit management practices they were still ineffective to cap the increasing bad debt levels. The study concluded that instant loan issuance without collateral, straightforward loan application processes and the lenient credit monitoring and collection policies have led to a significant proportion of consumers failing to repay their delinquent loans increasing the number of borrowers while concurrently increasing the number of defaulters, resulting into high levels of bad debt among microfinance institutions in Nairobi City County, Kenya. The study recommends that microfinance institutions should adopt technological advancements such as artificial intelligence and big data analytics to enhance their credit management practices and decrease the ballooning bad debt levels. The study also recommends the need for a harmonious credit identification policy where when one microfinance institution has disbursed loan to a borrower that information should be readily available through an integrated system to be used by other microfinance institutions to reduce high bad debt levels occasioned by borrowers moving from one microfinance institution to another with outstanding loans
Description
A Research Thesis Submitted to the School of Business, Economics, and Tourism in Partial Fulfilment for the Award of the Degree of Master of Science (Finance) of Kenyatta University, October, 2025
Supervisors:
1.Jeremiah Koori
2.Daniel Makori