/LendingClubCaseStudy

Primary LanguageJupyter Notebook

LendingClubCaseStudy

Lending Club is a marketplace for personal loans that matches borrowers who are seeking a loan with investors looking to lend money and make a return.

Table of Contents

General Information

  • Problem Statement

    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

  • Buisiness Objectives

    The company wants to understand the 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 its portfolio and risk assessment. 

  • Scope of the Case study :

    Scope is to identify patterns that indicates whether an applicant is likely to default or not . Based on that Lending Club/Investor can take timely decision.

  • Buisiness Objectives

    The company wants to understand the 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 its portfolio and risk assessment. 

  • Scope of the Case study :

    Scope is to identify patterns that indicates whether an applicant is likely to default or not . Based on that Lending Club/Investor can take timely decision.

Summary of the analysis and key take-aways

  • Univariate Analysis
  • Segmented Univariate Analysis
  • Bi-variate Analysis

Conclusions

  • Key attributes which deciding whether a loan applicant tend to default or not :
    • Annual Income
    • Verification Status
    • Grades
    • Interest rates
    • DTI
    • Pub_rec_bankruptcies
  • Additional points to consider while approving the loan
    • Verification of documents / Source
    • Income in the range 50000-100000 are tend to default
    • with past pub_rec_bankruptcies
    • Grades above D grade( E,F,G ) are tended to default
    • Clients from 'CA' state showing defaulter nature
  • Detailed conclusion mentioned in the "JoshyPJ__Sheetal_R_Lending Club.pdf"

Technologies Used

  • numpy
  • pandas
  • matplotlib.pyplot
  • missingno
  • seaborn
  • datetime

Acknowledgements

Contact

Created by Joshy PJ & Sheetal R