📊 Bank Loan Analysis Project

Project Overview 📃

The Bank Loan Analysis Project provides a comprehensive overview of a bank's loan portfolio, focusing on key performance indicators (KPIs), trends, and insights into loan applications, funding, repayments, and customer profiles. This project aims to enable bank stakeholders to assess the health of loan portfolios, understand customer behavior, and drive data-driven lending decisions.

This project includes an interactive Power BI dashboard with three main views: Synopsis, Overview, and Details, each designed to provide targeted insights.

Table of Contents 📋

• Project Overview
• Key Performance Indicators (KPIs)
• Dashboard Breakdown
  • Dashboard 1: Synopsis
  • Dashboard 2: Overview
  • Dashboard 3: Details
• Functionalities and Methodologies
• Business Benefits of Loan Data Analysis
• Technology Stack
• How to Use
• Conclusion
• Dashboard ScreenShots
• Author

Dataset Source 📀

The dataset used in this project is sourced from Kaggle, a widely recognized platform for datasets. This dataset provides comprehensive information on loan applications, borrower profiles, and loan performance metrics, making it ideal for conducting in-depth loan portfolio analysis.

Key Performance Indicators (KPIs) 📈

• Total Loan Applications: Measures the count of loan applications received, with Month-to-Date (MTD) and Month-over-Month (MoM) tracking.
• Total Funded Amount: Reflects the total funds disbursed as loans, along with MTD and MoM metrics.
• Total Amount Received: Tracks the repayments from borrowers, showing cash flow with MTD and MoM insights.
• Average Interest Rate: Monitors the average interest rate on loans to analyze lending costs.
• Average Debt-to-Income Ratio (DTI): Evaluates borrowers' financial health through the average DTI, with MTD and MoM fluctuations.
• Good Loan vs. Bad Loan KPIs:
  • Good Loans: Applications, Funded Amount, Received Amount.
  • Bad Loans: Application Percentage, Funded Amount, Received Amount.

Dashboard Breakdown ✨

Dashboard 1: Synopsis This high-level dashboard provides an overview of the loan portfolio’s performance with a snapshot of KPIs:

• Loan Applications: Overview of the total, MTD, and MoM.
• Funded and Received Amounts: Displays total and MTD metrics.
• Average Interest Rate and DTI: Helps assess loan portfolio quality.

Dashboard 2: Overview The Overview Dashboard is designed to show trends and breakdowns, with visuals including:

• Monthly Trends by Issue Date (Line Chart): Visualizes seasonality and long-term trends in lending.
• Regional Analysis (Filled Map): Identifies regions with significant lending activity.
• Loan Term Analysis (Donut Chart): Explores distribution of loan terms.
• Employee Length Analysis (Bar Chart): Shows lending metrics by employment length.
• Loan Purpose Breakdown (Bar Chart): Provides insights into loan purpose distribution.
• Home Ownership Analysis (Tree Map): Analyzes how home ownership impacts loan applications.

Dashboard 3: Details A detailed view of individual loan data, borrower profiles, and loan performance metrics. This Details Dashboard is ideal for accessing in-depth data on each loan, including attributes like Purpose, Home Ownership, Loan Grade, Funded Amount, Interest Rate, and Installment Amounts.

Functionalities and Methodologies 🔣

Key techniques and methodologies utilized in this project:

• Data Cleaning & Processing: Ensuring data integrity and accuracy.
• Data Modeling: Structuring data for analysis.
• Power Query: ETL process for data transformation.
• DAX Calculations: Creating custom KPIs and metrics.
• Date and Time Intelligence Functions: Accurate monthly and time-based aggregations.
• Visualization: Leveraging Power BI’s tools for interactive visualizations.

Business Benefits of Loan Data Analysis ✔️

• Risk Assessment: Analyzes borrower profiles for credit risk evaluation.
• Decision-Making Support: Provides data-driven insights for loan approvals.
• Portfolio Management: Assesses portfolio health and identifies underperforming loans.
• Fraud Detection: Detects inconsistencies and unusual patterns in loan data.
• Regulatory Compliance: Supports mandatory data collection and reporting.
• Profitability Analysis: Analyzes interest income and loan costs for profitability insights.

Technology Stack 🔧

• Power BI: For interactive data visualizations.
• Power Query: Data transformation.
• DAX: Calculations for KPI generation and advanced analytics.

How to Use ⁉️

• Clone this repository.
• Open Power BI and load the .pbix file to interact with the data.
• Navigate through the Synopsis, Overview, and Details dashboards for a comprehensive analysis.

Conclusion 🔹

The Bank Loan Analysis Project demonstrates the power of data analytics in understanding and managing a financial institution's loan portfolio. By integrating dynamic, interactive visualizations with key metrics, this project provides a user-friendly tool that helps bank stakeholders monitor loan performance, assess borrower creditworthiness, and make data-driven lending decisions.

Through the KPIs and visual insights presented in the dashboards, bank leaders and analysts can identify patterns, detect risks, and evaluate profitability, contributing to more strategic management of loan portfolios. This project also offers a scalable foundation for future enhancements, including predictive modeling and automated reporting, which can further enrich the insights for more proactive and effective loan portfolio management.

This project not only emphasizes the value of analytics in financial services but also showcases the capabilities of Power BI as a tool for translating complex loan data into actionable insights.

Dashboard ScreenShots 💡

Author 🎓

• Mainak Mukherjee

• Email: subha.mainak@gmail.com

• Linkedin: www.linkedin.com/in/mainakmukherjee08

• GitHub: https://github.com/Mainak-97