Assessment of Credit Risk Identification on Loan Repayment Performance of Saccos in Imenti South Sub-county, Meru, Kenya
Mercy Ngugi Gitonga, Christopher Mutembei
Asian Journal of Economics, Business and Accounting · pp. 326–334 · Published 29 Oct 2022
10.9734/ajeba/2022/v22i23877Abstract
With the emergence of COVID-19 pandemic the economic status in the country has drastically changed due to the loss of livelihood of many citizens. Repayment of loans has been affected and thus there is need to assess the effect of credit risk identification on the loan repayment performance of SACCOs in Imenti South Sub-county, Meru. The study adopted a correlation research design to establish the relationship between independent and dependent variables. The target population for this study was 36 employees employed by SACCOs in Imenti South Sub County. The sample size was 36 respondents since census study was carried out to get information on the study variables. Data were collected by use of questionnaire from the respondents. The regression analysis model was applied by the researcher to determine the association between the study variables. Results have been presented using frequency tables, graphs and pie charts. To ensure that ethical consideration was taken into account, the researcher obtained informed consent from the participants so that only the participants who agreed participated voluntarily. The researcher was also honest so that the information obtained from the study was used for this research study only. The research found that identifying credit risk has an impact on how well loans are repaid. This is shown by the significance threshold for credit risk identification, which was 0.0174–0.05, showing a substantial link between credit risk identification and loan payback performance. Identification of risk is important in order to improve risk management and control the hazards SACCOs may face. SACCOs should establish proper credit risk identification practices such as having a clearly defined process of risk reporting and training all staff on risk identification to avoid loss and defaults.
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