| Abstract |
In recent years, an increase in consumer spending has resulted in a rise
in consumer credit in India to $2,408.3 billion in 2010-11. To maintain
adequate profit margins in such a highly competitive and risky business
environment, lending institutions must take measures to manage credit
risks. Hence banks need to develop automated computer-based credit
risk models that can assess the risk of credit default within lesser time and
cost. The paper attempts to assess the credit risk of retail NBFC borrowers
based on certain consumer-specific characteristics, as well as, loanspecific
characteristics of borrowers. The objective of this paper is to find
out which are the most predictive variables affecting credit-worthiness of a
retail borrower. Data was collected Madhya Pradesh and Chhattisgarh.
Logistic Regression and Discriminant analysis were used. Similar results
were obtained by using both techniques. Results showed that only marital
status of borrower, source of loan, status of the borrower and tenure of
loan are the significant factors. |
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