Credit Management Practices and Bad Debt Levels of Microfinance Institutions in Nairobi City County, Kenya
| dc.contributor.author | Choda,Linus James Odongo | |
| dc.contributor.author | Koori,Jeremiah | |
| dc.contributor.author | Makori,Daniel | |
| dc.date.accessioned | 2025-04-08T09:40:26Z | |
| dc.date.available | 2025-04-08T09:40:26Z | |
| dc.date.issued | 2025-02 | |
| dc.description | Article | |
| dc.description.abstract | Between the years 2018 to 2021, the bad debt levels of MFIs in Nairobi City County, Kenya have been increasing by 18% 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; modern portfolio theory (MPT), capital asset pricing model (CAPM), credit risk theory and PRISM model of credit risk management. The study employed a descriptive research design, targeting 54 active microfinance institutions in Nairobi City County, Kenya, with a sample size of 15 selected through stratified random sampling. Primary data (credit management practices) and secondary data (bad debt levels) were collected using data collection sheets and questionnaires. These were administered to credit managers, finance analysts, accountants, and debt portfolio assistants via the drop and pick technique. Data was analyzed using SPSS version 29, incorporating descriptive statistics, diagnostic tests (normality, multicollinearity, heteroscedasticity, Hausman test), correlation analysis, regression analysis, and hypothesis testing. The study found that despite implementing credit management practices, microfinance institutions struggled to curb rising bad debt levels due to lenient loan issuance and collection policies. It concluded that instant loans, straightforward application processes, and weak credit monitoring have contributed to high default rates. The study recommends that microfinance institutions adopt AI and big data analytics for improved credit management and establish a shared credit identification system to reduce multiple borrowing and defaults. | |
| dc.identifier.citation | Odongo, L., J., C., Koori, J. & Makori, D. (2025). Credit Management Practices and Bad Debt Levels of Microfinance Institutions in Nairobi City County, Kenya. Journalof Finance and Accounting, 9(1) pp.59-73 | |
| dc.identifier.other | DOI: https://doi.org/10.53819/81018102t3116 | |
| dc.identifier.uri | https://ir-library.ku.ac.ke/handle/123456789/29934 | |
| dc.language.iso | en | |
| dc.publisher | Stratford | |
| dc.title | Credit Management Practices and Bad Debt Levels of Microfinance Institutions in Nairobi City County, Kenya | |
| dc.type | Article |