| dc.contributor.author | Adem, A | |
| dc.contributor.author | Gichuhi, AW | |
| dc.contributor.author | Otieno, Romanus Odhiambo | |
| dc.date.accessioned | 2018-11-20T16:30:40Z | |
| dc.date.accessioned | 2020-02-06T13:48:24Z | |
| dc.date.available | 2018-11-20T16:30:40Z | |
| dc.date.available | 2020-02-06T13:48:24Z | |
| dc.date.issued | 2012 | |
| dc.identifier.citation | Adem, A., Gichuhi, A. W., & Otieno, R. O. (2012). Parametric Modeling of Probability of Bank Loan Default in Kenya. Journal of Agriculture, Science and Technology, 14(1). | en_US |
| dc.identifier.uri | file:///C:/Users/Library/Downloads/OJS20120300005_28255412.pdf | |
| dc.identifier.uri | http://repository.must.ac.ke/handle/123456789/948 | |
| dc.description.abstract | Commercial banks in Kenya are the key players not only in the financial market but also in spurring the economic growth that has been witnessed in the country in the recent past. Besides Safaricom and East Africa Breweries, the other top ten most
profitable companies in Kenya are the Commercial banks. The biggest part of these huge profits emanates from the interests charged on loans they advance to their customers. If these loans non-perform, these blue chip companies will come
tumbling down and the entire economy will be threatened. This makes the study on probability of a customer defaulting very useful while analyzing the credit risk policies. In this paper, we use a raw data set that contains demographic information about the borrowers. The data sets have been used to identify which risk factors associated with the borrowers contribute towards default. These risk factors are gender, age, marital status, occupation and term of loan. Results show
that male customers have high odds (1.91) of defaulting compared to their female counter parts, single customers have a higher likelihood (odds of 1.48) of defaulting compared to their married customers, younger customers have high
odds of defaulting unlike elderly customers, financial sector customers have equal likelihood of default as support staff customers and long term loans have less likelihood of defaulting compared to short term loans. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | JAGST | en_US |
| dc.subject | The logistic model, the logit transformation, parameter estimation | en_US |
| dc.title | Parametric Modeling of Probability of Bank Loan Default in Kenya | en_US |
| dc.type | Article | en_US |
