- Like most other lending companies, lending loans to ‘risky’ applicants is the largest source of financial loss (called credit loss). The credit loss is the amount of money lost by the lender when the borrower refuses to pay or runs away with the money owed. In other words, borrowers who default cause the largest amount of loss to the lenders. In this case, the customers labelled as 'charged-off' are the 'defaulters'.
- If one is able to identify these risky loan applicants, then such loans can be reduced, thereby cutting down the amount of credit loss. Identification of such applicants using EDA is the aim of this case study • In other words, we are trying to find the driving factors (or driver variables) behind loan default, i.e. the variables which are strong indicators of default. The company can utilise this knowledge for its portfolio and risk assessment.
- We do this by analysing the details that are available at the time of loan approval such as customer demographics (employment details, housing details, dti) and loan characteristics (loan amount, interest rate, grade, sub grade, issue date, installment amount, term, purpose, verification status) to understand the pattern of defaulters. Knowledge of these patterns will help in identifying risky customers and rejecting / reducing the amount lent to such customers.
- For our analysis we are not considering the customers who were in the process of repaying their loan amount as we do not know if they will end up fully repaying their loan or defaulting them.