Determinants of Loan Defaults in Some Selected Credit Unions in Kumasi Metropolis of Ghana
Edward Yeboah, Irene Mirekuah Oduro
Abstract
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Edward Yeboah, Irene Mirekuah Oduro
Abstract
Open-access reader
Credit Unions play a pivotal role in the Microfinance Industry in Ghana. They are not only deeply rooted in financial intermediation but also provide favorable terms and conditions in financial products and services to their members compared to banks and other financial institutions. The sustainability of Credit Unions has been threatened by the incidence of loan defaults or non-performing loans. The diagnostics of the causes of loan defaults in Credit Unions become paramount toward sound credit risk management practices. The study relied on primary data. Purposive sampling technique was applied to select 244 Credit Union members. Questionnaires were used for data collection and logistic regression model was adopted. The study utilized Statistical Product and Service Solution (SPSS v. 20) and Stata (v.14) as statistical tools for data analysis. The results reveal that education, loan diversion, monitoring, marital status and income are significant factors that influence loan default. Thus, credit education should be intensified and that effective loan monitoring should be vigorously pursued. Additionally, loan appraisal systems should be robust with the application and development of credit scoring systems that will factor in key variables of loan default.
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Credit Unions play a pivotal role in the Microfinance Industry in Ghana. They are not only deeply rooted in financial intermediation but also provide favorable terms and conditions in financial products and services to their members compared to banks and other financial institutions. The sustainability of Credit Unions has been threatened by the incidence of loan defaults or non-performing loans. The diagnostics of the causes of loan defaults in Credit Unions become paramount toward sound credit risk management practices. The study relied on primary data. Purposive sampling technique was applied to select 244 Credit Union members. Questionnaires were used for data collection and logistic regression model was adopted. The study utilized Statistical Product and Service Solution (SPSS v. 20) and Stata (v.14) as statistical tools for data analysis. The results reveal that education, loan diversion, monitoring, marital status and income are significant factors that influence loan default. Thus, credit education should be intensified and that effective loan monitoring should be vigorously pursued. Additionally, loan appraisal systems should be robust with the application and development of credit scoring systems that will factor in key variables of loan default.
Key concepts: Default, Loan, Business, Credit reference, Credit risk, Finance, Credit history, Microfinance