Identify driving factors (or driver variables) behind loan default, i.e., the variables which are strong indicators of default. The company can utilize this knowledge for taking actions such as denying the loan, reducing the amount of loan, lending (to risky applicants) at a higher interest rate, etc.
Lending club is a consumer finance company providing loan to urban customers. Like most other lending companies, lending loans to ‘risky’ applicants is the largest source of financial loss (called credit loss). They need to identify these risky loan applicants to reduce their credit loss
Identify driving factors (or driver variables) behind loan default, i.e., the variables which are strong indicators of default. The company can utilize this knowledge for taking actions such as denying the loan, reducing the amount of loan, lending (to risky applicants) at a higher interest rate, etc.
Complete loan data for all loans issued through the time period 2007 t0 2011.
- There is a sharp increase in default rate from 36 to 60 months loan terms.
- Verified customers are defaulting more than the non verified except for very high income groups.
- Users with 2 or more public record bankruptcies are high risk.
- For very high income group, there is a high concentration of defaulting for ‘other’ type of home ownership.
- Higher loan amount has higher default rate
- There is a 6% increase in default rate when we move from high income group to low income group
- Pandas
- Seaborn
- Numpy
- We would like to thank our tutor Sayan for helping with all the queries and helping us understand the approach
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