Lending Club Case Study

Welcome to our data analysis on lending club case study ,focused on understanding and uncovering patterns for loan defaulters. In this Study, we will delve into the intricacies of financial data to extract meaningful insights that can guide decision-making and risk management strategies

Table of Contents

• Introduction
• Data Collection and Overview
• Aim of Analysis
• Technology Used
• Conclusions

Introduction

• The analysis is done on the financial information provided about past loan applicants and whether they ‘defaulted’ or not.
• The primary objective of this analysis is to identify key factors contributing to loan defaults, providing stakeholders and investors with a nuanced understanding of the dynamics at play. By leveraging advanced analytical techniques, we aim to offer actionable insights to mitigate risks and optimize lending practices.

Data Collection and Overview

• These data has been collected from 2007 to 2011 across 48 different states ,having total 35420 unique entries comprising of thousands of people who are employed in different fields.
• These financial data is spanning across 111 valid Columns ,covering almost each and every financial aspect of individuals.

Aim of Analysis

• The aim is to identify patterns which indicate if a person is likely to default, which may be used for taking actions such as denying the loan, reducing the amount of loan, lending (to risky applicants) at a higher interest rate, etc..

Conclusions

• Charged Off borrowers are paying High Interest rate than fully paid, due to increased interest rate on outstanding loan amount or late fees because of failing in paying installment or emi,irrespective of their verification of their income source either they are verified or not.
• Highest utilization of revolving credit line in percentage in case of charged off borrowers is 50-75% slab whereas it is 25 -50% slab in fully paid borrowers.
• It is visible from graph that there is significant difference between number of charged off borrowers who have their rented or mortgage house rather than they own their house.
• Apart from debt consolidation, Large number of Charged Off borrowers has taken loan for Small Business and credit card debt cosolidation.
• Charged Off borrowers having grade B & C with Sub grade 2 to 5 ,are more likely to default on Loan .

Technologies Used

• Numpy - Version: 1.24.3
• seaborn - Version: 0.12.2
• matplotlib - Version: 3.7.1
• pandas - Version: 2.0.3

Contact

Created by @praveenkkushwaha - feel free to contact me!