Loan Performance Analysis Project - Power BI Dashboard

Project Overview

This project demonstrates the creation of an interactive Loan Performance Dashboard in Power BI. The dashboard provides insights into loan performance across various metrics, helping stakeholders make data-driven decisions.

Objectives

• Analyze Loan Performance: Understand distribution, defaults, and repayment trends.
• Identify Key Metrics: Highlight critical KPIs such as total loan amount, default rate, and average tenure.
• Enhance Decision-Making: Provide actionable insights to optimize loan products and improve operational efficiency.

Dataset

• Source: Synthetic dataset representing over 10,000 loan records.
• Key Attributes:
  • Loan Details: Loan ID, Product Type, Amount, Start and End Dates.
  • Customer Details: Location, Age, Credit Score.
  • Loan Metrics: Loan Status, Interest Rate, Tenure, Late Payments.

Key Metrics & Visualizations

1. Total Loan Amount: Sum of all loan amounts.
2. Default Rate: Percentage of loans in default.
3. Loan Distribution: Analysis by product type and region.
4. Repayment Trends: Proportions of fully repaid, ongoing, and defaulted loans.

Power BI Workflow

1. Data Import: Loaded the dataset from an Excel file.
2. Data Cleaning:
   • Removed null values and duplicates.
   • Ensured correct data types for columns.
3. Data Transformation:
   • Added calculated columns: Loan Tenure and Monthly EMI.
4. Dashboard Creation:
   • Created bar charts, KPI indicators, maps, and pie charts for analysis.

Visualizations

• KPI Indicators: Show total loans, average tenure, and default rate.
• Loan Distribution by Product Type: Bar chart showing loan amounts across products.
• Default Rate by Region: Stacked bar chart for regional comparison.
• Loan Performance by Region: Visualization for regional trends.
• Repayment Trends: Pie chart showing repayment status proportions.

Insights

• Default Rates:
  • Higher defaults in certain regions and products.
  • Correlation between low credit scores and defaults.
• Loan Distribution:
  • Significant concentration in specific products/regions.
• Repayment Trends:
  • Major proportion of loans are either actively repaid or defaulted.

Recommendations

• Customer Segmentation: Focus on high-risk customers with tailored repayment plans.
• Product Optimization: Tighten eligibility criteria for high-default products.
• Operational Efficiency: Automate monitoring for high-risk loans using dashboards.

Repository Contents

1. Dataset: Synthetic dataset in Excel format.
2. Power BI File: .pbix file with the dashboard.
3. PPT Presentation: Slides summarizing the analysis and insights.
4. README: This document.

How to Run

1. Clone this repository:

   git clone https://github.com/your-repo/loan-performance-analysis.git

2. Open the .pbix file in Power BI Desktop.
3. Explore the interactive dashboard.

Contact

• Author: Vani Bhatt
• Email: bhattvani91@gmail.com
• LinkedIn: https://www.linkedin.com/in/vani-bhatt-b8a69a79/

Thank you for exploring this project!