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.
