Predicting likelihood for loan default among bank borrowers

Aslam, Muhammad and T., Senthil Kumar and Sorooshian, Shahryar (2020) Predicting likelihood for loan default among bank borrowers. International Journal of Financial Research, 11 (1). pp. 318-328. ISSN 1923-4023 (print); 1923-4031 (online). (Published)

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Poverty is a threat to the world. In its extreme form at any part of the world, it will make endanger rest of the world. In fact, it is the source of crime and the worst form of violence. The poor people do not commit any crime but they get punishment out of being born as a poor that is not controllable in their hand. Microfinance has been designed to eliminate poverty and help marginal and poor people through small income generating activities. The borrowers need capital to materialize their dream, may be in a small amount and microfinance can play important role in this scenario. Through microfinance, small entrepreneurs may acquire necessary inputs to start their business. Both local governments and international agencies are trying to eliminate poverty through microfinance programs, services and guidelines. With this concept, Microfinance has been hosted primarily in Bangladesh. Grameen Bank (GB) has been serving large number of people below poverty level in Bangladesh. However, impact of microfinance is still questionable in several studies. Microfinance used properly and returned back to the lender with stipulated amount and time shows its working effectively for poverty alleviation. Otherwise, there must be loan default and the whole system may be in question. We survey with questionnaire to find out factors contributing to loan default among GB borrowers using binomial logistic regression. The results showed that some factors were crucial for loan default and should be treated properly at the start of lending.

Item Type: Article
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Microfinance; Poverty alleviation; Logistic regression; Loan default factors
Subjects: H Social Sciences > HG Finance
Q Science > QA Mathematics
Faculty/Division: Faculty of Industrial Management
Institute of Postgraduate Studies
Depositing User: Mrs Norsaini Abdul Samat
Date Deposited: 12 Mar 2020 02:57
Last Modified: 12 Mar 2020 02:57
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