Lendingclub Case Study

A consumer finance company aims to optimize its loan approval process by leveraging historical data to minimize both business loss and financial risk associated with loan approvals. This case study cleans and analyses the loan data provided by the company and gives insights on what parameters should be taken into consideration while giving loans

Table of Contents

• General Info
• Technologies Used
• Conclusions
• Acknowledgements

General Information

OBJECTIVE:

• The objective is to analyze past loan applicant data to identify patterns that signal potential defaults, enabling the company to make informed decisions such as denying loans, reducing loan amounts, or adjusting interest rates for risky applicants.

BUSINESS PROBLEM:

• The company aims to optimize its loan approval process by leveraging historical data to minimize both business loss and financial risk associated with loan approvals.

DATA SET: Loan data set loan.xlsx is being used in the project.

Conclusions

• Term: Percentage of people who defaulted in 60 months term is 25% compared to 36 months term which is 10.8%. Be cautious while giving loans for 60 months term.
• Loan Amount: Around 16% of total loan amount is defaulted. Try to limit the exposure.
• Employment length: People with employment length 10+ years have taken highest loan amount. Higher the loan amount installments also rise which can cause a default. Be wary of them.
• Last Credit Pull Date: The last credit data pulled for majority of defaulters is 7 years ago. Please make sure credit pull is done at the earliest and give the loan.

Technologies Used

• seaborn - version 0.13.0
• matplotlib - version 3.8.0
• pandas - version 2.1.1

Acknowledgements

• This project was a case study for Exploratory Data Analysis tutorial on Upgrad.

Contact

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