karanvidhan/lendingclubcasestudy

Lending Club Case Study

Lending loans to ‘risky’ applicants is the largest source of financial loss (called credit loss). The credit loss is the amount of money lost by the lender when the borrower refusesto pay or runs away with the money owed.

The main objective is to be able to identify these risky loan applicants, then such loans can be reduced thereby cutting down the amount of credit loss. Identification of such applicants using EDA is the aim of this case study.

Perform an analysis to understand the driving factors (or driver variables) behind loan default, i.e.the variables which are strong indicators of default.
The company can utilise this knowledge for its portfolio and risk assessment.

Table of Contents

• Data Cleaning 1
• Univariate Analysis
• Segemented Univariate Analysis
• Bivaraiate/Multivariate Analysis
• Results

Conclusions

• Low grade loans have high tendency to default. Grading system is working as expected.
• Loans having higher interest rate have more defaulters. Check the background of applicant thoroughly if interest rate is high.
• Extra scrutiny must be done for the applicants belonging to CA state, as tendency to default is high.
• When the purpose is debt consolidation check applicant thoroughly as it has high tendency to default.

Contributors

Karan Vidhan Omkar Satapathy

-- Developed as part of the Exloratory Data Analysis Module required for Post Graduate Diploma in Machine Learning and AI - IIIT,Bangalore.