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Lending Club Case Study

Table of Contents

General Information

Lending club is the largest peer-to-peer marketplace connecting borrowers with lenders. Borrowers apply through an online platform where they are assigned an internal score. Lenders decide 1) whether to lend and 2) the terms of loan such as interest rate, monthly instalment, tenure etc. Some popular products are credit card loans, debt consolidation loans, house loans, car loans etc.

Conclusions

  • Lending club should reduce the high interest loans for 60 months tenure, they are prone to loan default.
  • Grades are good metric for detecting defaulters. Lending club should examine more information from borrowers before issuing loans to Low grade (G to A).
  • Lending Club should control their number of loan issues to borrowers who are from CA, FL and NY to make profits.
  • Small business loans are defaulted more. Lending club should stop/reduce issuing the loans to them.
  • Borrowers with mortgage home ownership are taking higher loans and defaulting the approved loans. Lending club should stop giving loans to this category when loan amount requested is more than 12000.
  • People with more number of public derogatory records are having more chance of filing a bankruptcy. Lending club should make sure there are no public derogatory records for borrower.

Technologies Used

  • Programming Language : python 3.10
  • libraries : pandas, numpy, matplotlib, seaborn

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Created by [@rvk16] - feel free to contact me!

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