Loan Applications Analysis

Tools Used: Python (Jupiter Notebook), Power BI

Background

The Explanatory Data Analysis on a Bank's Application Loan Data, in order to identify:

• Main trends among all applications, focusing on loan decisions, loan amount, and property types of loans.
• The differences between approved applications and rejected ones.
• Factors that significant to the loan amount, and the Model to estimate the loan amount of a new applicant.
• The low-risk model that accurately predicts loan decisions.

Data sources and Files

Datasets of the Analysis can be be found in the datasets folder:

• loanapp.csv: The dataset contains data on loan applications to a bank, including various types of information on the applicant and the purpose of the loan, along with the eventual loan decision
• loanapp_desc.txt: A detailed description of the dataset columns

The Interactive Power BI Dashboard can be downloaded here.

The detailed Statiscal Analysis can be found here.

The Project's directory structure:

loan applications/
├── visuals/ # visualisations folder 
├── datasets/
│   ├── loanapp.csv
│   ├── loan_applications_BI.scv # dataset of BI dashboard
│   └── loan_app_detail.txt
├── loan_appications_analysis.ipynb # main analysis and insights
├── BI_report_loan_app.pbix # Power BI interactive dashboard
└── readme.md

Executive Summary

Loan Decisions:

• 87% of loans were approved, 13% were rejected.
• 90% of loans that applied for purchasing property type 2 were approved .
• The average annual income and average liquid assets value of approved applicants is significantly greater than that of rejected applicants.
• The higher an applicant Mortgage Payment History Score is, the more likely their application is being rejected.

Loan Amount:

• Majority of loans (47%) are around $100,000 to $150,000.
• Most applicants (98.6%) applied for loans less or equal to the Purchase Price.
• 94% applicants applied for loans that larger than 50% of the Purchase Price.
• Most influential factors to an application's loan amount: applicant's income, value of their liquid assets, property type and its purchase price.

Property Types:

• Most of loans (70%) applied for purchasing Property Type 2, while this of Type 3 is only 9%

Proposed Questions:

• Which criteria does the bank currently prioritise when approving or rejecting loans?
• Are there concerns about bias in the current decision-making process of loans?
• What is the bank's target distribution for loan amounts (e.g. ideal percentage of approved loans below $100,000)?
• Should the analysis consider external factors (e.g., market trends or economic conditions) that may influence rejections?
• Is the bank more interested in minimising false rejections (rejecting good applicants) or false approvals (approving risky applicants)?

Insights Deep-dive

1. Loan Applications Overview

• Majority of Loans (47%) are around $100,000 to $150,000.
• 87% of loans were approved, 13% were rejected.
• Most of loans (70%) applied for purchasing Property Type 2 .

• The Average Income of Applicants is $66,000.
• The Average Liquid Assets Value of Applicants is $48,000.
• Most Applicants (42%) have their annual income between $50,000 to $75,000.
• The higher an applicant Mortgage Payment History Score is, the more likely their application is being rejected.
• 92.5% of applications with Mortgage Payment History Score of 1 were Approved.

2. Loan Decision Analysis

Statistics of Appoved Applications:

Statistics of Rejected Applications:

Accoring to the Dashboard's visualisations and the Statistical tests performed:

• The Average Yearly Income of Approved applicants ($66,000) is significantly greater than that of Rejected applicants ($63,000).
• The Average Liquid Asset Value of Approved applicants ($50,000) is significantly greater than that of Rejected applicants ($35,000).
• There no significant differences in Mortage Payment History and Consumer Credit History between Approved and Rejected Applications.
• There are significant associations between applicant's Self-Empoyed, Filed-Bankruptcy statuses and their Loan Decision.

Loan Decision Prediction Model:

The predictive model (Logistic Regression) of a loan's decision based on the applicant's provided information, with 69% accuracy for loan rejections and 68% for approvals: here.

3. Loan Amount Analysis

Loan Amount vs. Properties' Purchase Price:

Applicant have the tendency to apply for loans less or close to Purchase Price, with 98.6%:

• 94% applicants applied for loans that larger than 50% of the Purchase Price, and 78% applied for loans greater than 70% of Purchase Price.

Loan Amount vs. Applicants' Income:

As an applicant's annual Income increases, their Loan Amount also increases:

Most influential factors to an Application's Loan Amount:

• Applicant's Monthly Income and Annual Income.
• Property Type.
• Property Purchase Price.
• Amount of an applicant's Liquid Assets value.

The Prediction Model for Loan Amount:

The predictive model of a loan's amount based on the applicant's provided information, explains 70% (R-squared = 0.7) of the variability in the loan amounts: here.

4. Property Types Analysis

Loans to purchase Property Type 1:

Loans to purchase Property Type 2:

Loans to purchase Property Type 3:

Loan Decisions:

• Properties of Type 2 account for the most loans (70%), and also for the highest approved percentage (90%)
• Properties of Type 3 account for the least loans (9%), and for the lowest approved percentage (69%) - or 31% of rejection.

Loan Amounts:

• Majority (43%) of Loans for Type 1 Properties were between $50,000 to $100,000, while this of the $100,000 to $150,000 range is 40%.
• Majority (51%) of Loans for Type 2 Properties were between $100,000 to $150,000.
• 36% of Loans for Type 3 Properties were between $150,000 to $200,000, while this of the $100,000 to $150,000 range is 35%.

Recommendations

• Focus on Popular Loan Ranges: Design loan products around the most common loan range ($100,000 to $150,000), with flexible options for applicants.

• Increase Appeal of Loans for puchasing Property Type 3: Develop targeted campaigns or incentives to attract applicants for Property Type 3, which currently makes up only 9% of loans.

• Review Loan thresholds based on Purchase Price: Since 94% of applicants request loans that are more than half of the purchase price, adjust policies to attract more applicants that wish to apply for loans with smaller amount (loans with lower default risk).