Assessing the Effect of Credit Risk Identification on Loan Performance in Credit-Only Microfinance Institutions in Central Kenya

Abstract

Credit-only microfinance institutions play an important role in expanding access to credit among underserved households, micro-entrepreneurs and small businesses in Kenya. However, persistent loan defaults and declining portfolio quality continue to threaten their financial sustainability, particularly because many borrowers operate in informal markets with limited financial records and unstable income streams. This study examined the effect of credit risk identification on loan performance among credit-only microfinance institutions in Kirinyaga, Nyeri, Murang'a and Kiambu counties in Central Kenya. The study was anchored on Information Asymmetry Theory, which explains how limited borrower information can increase adverse selection and moral hazard in lending. A descriptive and explanatory research design was adopted. Data was collected from credit managers, junior credit officers and field credit officers using structured questionnaires and interviews. Quantitative data was analyzed using descriptive statistics and simple linear regression, while qualitative responses were analyzed thematically. Credit risk identification had a positive and statistically significant relationship with loan performance. Specifically, credit risk identification explained 41.2% of the variation in loan performance, with regression results indicating a significant positive coefficient, B = 0.735, p < .001. Results suggest that borrower screening, verification of loan application information, background checks, use of Credit Reference Bureau reports and assessment of borrower credit history are important in improving repayment performance and reducing default risk. Effective credit risk identification is a critical determinant of loan performance among credit-only microfinance institutions. There is need for these institutions to strengthen borrower appraisal systems, improve credit information sharing, train credit officers continuously and adopt data-driven tools for early identification of risky borrowers.