LOGISTIC REGRESSION BASED FRAMEWORK FOR CREDIT RISK DECISION

Abstract

Risk is ‘said to be an uncertainty ‘of occurrence of economic loss. Credit risk is one of the most important topics in risk management, it is the risk of default on a debt that may arise from a ‘borrower failing to make the required payments. In this ‘project, I focus on the ‘credit risk problem at the firm level. I try to identify key financial ratios that would help to distinguish between credit worthy companies which are ‘unlikely to default ‘and ‘less credit worthy companies which are more likely to default in India based on the credit ratings given by various ‘credit rating ‘agencies. I consider the organizations with credit ratings of “Baa2” or higher to be stable and the organizations with credit ratings lower than “Baa2” to be ‘unstable. A framework of multinomial logistic regression is used to identify the key financial factors from a pool of 33 financial ratios. This model ‘will ‘help the organizations to mitigate the losses by giving ‘loans to the companies which are less ‘likely to ‘default.