In this case study, we used EDA to understand how consumer attributes and loan attributes influence the tendency of default.

• General Info
• Technologies Used
• Conclusions
• Acknowledgements

You work for a consumer finance company which specialises in lending various types of loans to urban customers. When the company receives a loan application, the company has to make a decision for loan approval based on the applicant’s profile. Two types of risks are associated with the bank’s decision:

• If the applicant is likely to repay the loan, then not approving the loan results in a loss of business to the company.

• If the applicant is not likely to repay the loan, i.e. he/she is likely to default, then approving the loan may lead to a financial loss for the company.

There is a higher probability to find a defaulting customer in the following cases:

• Those who receive interest at the rate of 21-24% and have an income in the range of 70k-80k.
• If employment tenure is 10 yrs and loan amount is 12k-14k.
• If it is a verified loan for an amount ranging over 16k.
• When the loan is for 60k-70k and the applicant took it for "Home Improvement".
• Customers who take a loan for 60k-70k and don't own a home, i.e. either Rent or Mortgage.
• When the grade is F and loan amount is between 15k-20k.

We also observed a few good traits for people who fully paid their loans

• If home is self owned, such customers are more likely to fully pay.
• Lower interest rates were an important factor in fully paid loans.
• Lower sanctioned amounts between 5k-10k were mostly fully paid.
• Lower employment tenure between 3-6 years showed a good trend.
• Grade B loans were generally fully paid.
• Lower ratio of loan amount to annual income meant that these customers were less likely to default.

• Jupyter Notebook
• Python
• Modules - Seaborn, Matplotlib, Pandas, Numpy

• Manasa for helping with EDA and PPTx.

Created by Prashant @prixroxx - feel free to contact me!