This repository involves the analysis of a home loan dataset from Kaggle for the development of an interactive dashboard using Power BI. The dashboard provides detailed insights into various metrics such as loan applications, sanctions, disbursements, and recoveries, aiding in informed decision-making and strategic planning.
Financial institutions often struggle to analyze and optimize their loan processes due to the complexity and volume of data. There is a need for a comprehensive analytical solution that can provide clear insights into loan application trends, channel and product performance, customer segmentation, and recovery rates.
The primary objective of this project is to create a robust dashboard that enables financial analysts and decision-makers to:
- Visualize and monitor key metrics related to home loans.
- Identify trends in loan applications over time.
- Analyze the performance of various channels and products.
- Assess branch-wise loan performance geographically.
- Segment customers based on demographic factors and understand their influence on loan amounts.
- Provide actionable insights to improve loan recovery rates and optimize the loan process.
- Loan Performance:
- Total Amount Applied: 245K
- Total Sanction Amount: 212.73K
- Total Disbursed Amount: 195.71K
- Total Recovery Amount: 104.20K (Recovery Rate: 53.24%)
- Channel Performance:
- Online channels contributed the most to loan applications, with 89K (36.46%) of the total amount applied.
- Product Analysis:
- The highest amount applied was for loans combined with group insurance, totaling 62K.
- Trend Analysis:
- Loan applications have shown consistent growth over the years from 2017 to 2019.
- Customer Segmentation:
- Age groups 44-55, 56-63, and 63+ were identified as key influencers in higher loan application amounts.
Explore the interactive dashboard to gain insights into home loan trends and performance.
- Optimize Online Channels: Given the high contribution of online channels, further investment in online marketing and customer acquisition strategies could enhance application rates.
- Focus on Product Bundling: The success of loans combined with insurance suggests that bundling products could be a profitable strategy.
- Targeted Marketing: Tailoring marketing efforts towards the identified key age groups could improve loan application numbers and amounts.
- Enhance Recovery Strategies: Considering the current recovery rate, implementing more robust recovery mechanisms or customer engagement strategies might improve the overall recovery percentage.
The Home Loan Trends Dashboard effectively addresses the challenges faced by financial institutions in analyzing home loan data. By providing clear, data-driven insights, the dashboard aids in optimizing loan processes, improving customer targeting, and enhancing recovery strategies. The project demonstrates the value of integrating advanced data visualization and analysis techniques into financial decision-making processes.
- Power BI Desktop: Data visualization and dashboard creation.
- Esri: Geographic data visualization integration.
- Download and Install Power BI Desktop: Download here.
- Clone this Repository:
git clone https://github.com/sanasayyed2001/Home-Loan-Trends-Dashboard.git - Open the Power BI File: Open the .pbix file in Power BI Desktop.
- Explore the Dashboard: Interact with the visualizations to gain insights into home loan metrics.
- Navigate through Tabs: Explore different aspects such as overall metrics, channel performance, and branch analysis.
- Filter Data: Apply filters to focus on specific channels, products, or time periods.
- Drill-Down Features: Utilize drill-down options for detailed analysis.
Contributions to this project are welcome. Please fork the repository, create a feature branch, and submit a pull request for any improvements or additions.
This project is licensed under the MIT License. See the LICENSE file for details.
For questions or feedback, please contact sayyedsy2001@gmail.com.