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Leading club case study

As we 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:

  1. If the applicant is likely to repay the loan, then not approving the loan results in a loss of business to the company.
  2. 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.

Table of Contents

General Information

  • The company wants to understand the driving factors (or driver variables) behind loan default (loan_status = 'Charged Off'), i.e. the variables which are strong indicators of default. The company can utilise this knowledge for its portfolio and risk assessment
  • What is the background of your project : Understaing the data set , cleaning the data and deriver the variables from it and finding conclusions of the applications.
  • What is the business probem that your project is trying to solve : portfolio and risk assessment
  • What is the dataset that is being used : we are using the leading club case study data set to analysis the problem format is .csv

Conclusions

  • Conclusion 1 from the analysis:
  • As per the analysis made more loan interest rate and borrower from rent and mortgage are more “charged off” chances.
  • image
  • Conclusion 2 from the analysis:
  • Interest rate increasing clearly that grades moving A to G, as the higher the grade higher the “charged off” <<<<<<< HEAD
  • image
  • Conclusion 3 from the analysis:
  • Annual income as the borrowers in very low means their are then to more “charged off”
  • image =======
  • 4
  • Conclusion 3 from the analysis:
  • Annual income as the borrowers in very low means their are then to more “charged off”
  • 3

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Technologies Used

  • pandas version - version 1.3.4
  • numpy version - version 1.20.3
  • sea born version - version 0.11.2

Acknowledgements

Give credit here.

  • This project was inspired by Fintech compnay and online leding company for small loan
  • References for pandas and matplotlibs
  1. https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.qcut.html
  2. https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.pivot.html
  3. https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.boxplot.html
  • This project was based on fintech company like ZestMoney and paytm etc..

Contact

Satyanarayana k & vinoth.kanagarathinam batch -1993 PG in ML and AI -feel free to contact us!