Credit risk assessment and performance of loans in selected commercial banks in Kamwenge District. A cross-sectional study.
DOI:
https://doi.org/10.51168/rfdfqz17Keywords:
Credit risk, loan performance, credit history, Credit scoringAbstract
Background:
The study aimed to examine the relationship between credit risk assessment and the performance of loans in selected commercial banks in Kamwenge District.
Methodology:
This study adopted a quantitative, descriptive correlational, cross-sectional survey design. Data were collected once from 75 respondents selected from 94 employees in three commercial banks in Kamwenge District using purposive and simple random sampling. Questionnaires and interviews provided primary data, supported by secondary sources. Instrument validity (CVI=0.93) and reliability (α=0.94) were ensured. Data were analyzed using descriptive statistics, Pearson correlation, and multiple regressions in SPSS. Ethical requirements were strictly observed throughout all stages of data collection, analysis, and reporting.
Results:
The study recorded a 93.3% overall response rate, with managers at 77.8%, loan officers at 80%, and support staff at 100%. Most respondents were aged 31-45 years (68.6%), followed by 45+ years (22.9%) and 20-30 years (8.6%). University graduates formed 78%, diploma holders 14%, and certificate holders 8%. Regarding experience, 43% had 3-5 years, 29% had 6-10 years, 17% had 0-2 years, and 11% had over 11 years. Credit scoring (34%) and collateral assessment (26%) dominated assessment methods. Mean scores were high for reviewing credit history (4.8) and collateral requirement (4.9). Pearson correlation showed a strong positive relationship between credit risk assessment and loan performance (r=0.628, p<0.01). Industry and macroeconomic considerations recorded neutrality at 61.4%, while income assessment averaged 4.1 and debt-to-income ratio averaged 3.6 among respondents across surveyed banks locally.
Conclusion:
Effective credit risk assessment has a strong positive influence on loan performance in commercial banks.
Recommendation:
Commercial banks should adopt advanced data analytics and credit scoring models to strengthen credit risk assessment accuracy.
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