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https://github.com/bhushan148/finance-domain-bank-loan-report-tableau

I analyzed 🏦 bank loan data to reveal trends, KPIs, and insights. Using Tableau 📈 for dashboards and SQL 🗃️ for data extraction, I visualized loan applications, borrower profiles, and repayment behaviors 💡.
https://github.com/bhushan148/finance-domain-bank-loan-report-tableau

bussiness-intelligence dashboard-design data-analysis data-visualization excel figma sql sqlqueries tableau

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I analyzed 🏦 bank loan data to reveal trends, KPIs, and insights. Using Tableau 📈 for dashboards and SQL 🗃️ for data extraction, I visualized loan applications, borrower profiles, and repayment behaviors 💡.

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README 

        

# 📊 Bank Loan Analysis Project Using Tableau



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## 📝 Project Overview



This project focuses on analyzing bank loan data to identify key trends, performance indicators, and risks associated with lending activities. Using **Tableau** for visualization and **SQL** for data manipulation, the project presents insights that help improve decision-making processes in managing a bank's loan portfolio. The dashboards showcase different aspects of loan performance and borrower profiles to highlight risk factors, profitability, and repayment behaviors.



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## 🎯 Project Objective



The primary goal of this project is to:

- **Analyze** bank loan data to gain insights into lending trends.

- **Identify** key performance indicators (KPIs) that reflect the overall health of the loan portfolio.

- **Evaluate** borrower performance based on factors like loan purpose, employment length, home ownership, etc.

- **Support** financial institutions in making data-driven decisions for better risk management and loan performance improvement.



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## 📂 Tools & Technologies



- **Tableau**: For building interactive dashboards and data visualizations.

- **SQL**: For data extraction, transformation, and cleaning.

- **Excel**: Used for basic data manipulation and exploration.

- **PowerPoint**: Summarizes project insights in a professional presentation.



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## 🔧 Project Steps



### 1. Data Import and Preparation

- **Data Source**: Bank loan dataset containing loan applications, borrower demographics, loan purposes, interest rates, repayment status, etc.

- **Data Cleaning**: SQL queries were used to clean the dataset, handle missing values, correct data types, remove duplicates, and filter important variables.

- **Integration with Tableau**: The cleaned dataset was imported into Tableau for analysis and visualization. Additional data transformation and refinement were performed within Tableau.



### 2. Dashboard 1: Summary Dashboard

This dashboard provides a high-level overview of key metrics related to the bank’s loan portfolio:

- **Total Loan Applications**: The number of applications received, displayed with Month-to-Date (MTD) and Month-over-Month (MoM) trends.

- **Total Funded Amount**: The total loan amount disbursed by the bank, with MoM and MTD comparisons.

- **Total Amount Received**: Total loan repayments received, showing MTD and MoM changes.

- **Average Interest Rate**: Average interest rate across loans, tracked over time.

- **Average Debt-to-Income Ratio (DTI)**: Reflecting the financial health of borrowers, this metric is analyzed for trends over time.

- **Good Loan vs. Bad Loan KPIs**:

    - **Good Loan**: The percentage and count of ‘Good Loans’ (loans with healthy repayment), including total funded and received amounts.

    - **Bad Loan**: The percentage and count of ‘Bad Loans’ (loans with issues such as defaults), along with related amounts.

- **Loan Status Grid View**: A detailed table displaying key metrics segmented by loan status.



### 3. Dashboard 2: Overview Dashboard

This dashboard dives deeper into specific metrics, revealing patterns and trends across different dimensions:

- **Monthly Trends by Issue Date**: Trends in loan issuance over time, identifying peak periods and seasonal patterns.

- **Regional Analysis by State**: A geographic breakdown of loan activity and performance across various states.

- **Loan Term Analysis**: Segmenting loans by terms (short-term, medium-term, long-term) to understand default risks and repayment behaviors.

- **Employee Length Analysis**: Investigating the relationship between borrowers' employment lengths and loan performance.

- **Loan Purpose Breakdown**: Analyzing loans by their purposes (e.g., education, home improvement) to evaluate performance based on reasons for borrowing.

- **Home Ownership Analysis**: Understanding how homeownership status impacts loan approvals, defaults, and overall loan performance.



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## 🔍 Key Insights from the Project



1. **Regional Disparities**: Some states showed a higher tendency toward loan defaults, indicating the need for region-specific lending strategies.

2. **Loan Term Risks**: Short-term loans generally had higher default rates compared to long-term loans.

3. **Employment Impact**: Borrowers with stable, long-term employment histories had significantly better repayment performance.

4. **Interest Rate Effects**: Higher interest rates were correlated with a greater likelihood of default, indicating a potential risk associated with high-rate lending.

5. **Loan Purpose**: Loans taken for certain purposes, such as education or home improvement, had better repayment behavior compared to others.



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## 🧠 Learning Outcomes



Through this project, I gained the following skills and knowledge:

- **Tableau Proficiency**: Mastered creating highly interactive dashboards with dynamic filters and drill-downs.

- **SQL Mastery**: Wrote complex queries to clean and transform data, ensuring data integrity for analysis.

- **Business Insight Development**: Developed an understanding of the critical financial metrics that drive loan portfolio performance.

- **Data-Driven Storytelling**: Learned to tell compelling stories through data visualizations to highlight key trends and support business decisions.



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## 📊 Explore the Tableau Dashboards



- [Live Tableau Dashboard](https://public.tableau.com/shared/GGBZ3YTPG?:display_count=n&:origin=viz_share_link)

- (Links will direct users to live versions of the dashboards hosted on Tableau Public or a similar platform.)



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## 📄 Project Files and Resources



- **[Download SQL Queries Document](https://github.com/Bhushan148/Finance-Domain-Bank-Loan-Report-Tableau/blob/main/Project%20SQL%20Queries.pdf)** (PDF containing the SQL queries used for data preparation)

- **[Download Full Project Report](https://github.com/Bhushan148/Finance-Domain-Bank-Loan-Report-Tableau/blob/main/Tableau%20Finannce%20Dashboard.pdf)** (Detailed PDF report with findings and analysis)

- **[Dataset](https://github.com/Bhushan148/Finance-Domain-Bank-Loan-Report-Tableau/blob/main/Other%20Resources/financial_loan.csv)** (CSV File)



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## 🌟 Future Enhancements



In future iterations of this project, I plan to:

1. **Integrate Real-Time Data**: Incorporate real-time data via APIs to keep the dashboards up-to-date.

2. **Predictive Analytics**: Add machine learning models to predict loan defaults and assess risk more accurately.

3. **Advanced Interactivity**: Introduce more advanced drill-down features and user interactivity for deeper insights.



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## 📧 Contact



Feel free to reach out to me for any questions, suggestions, or collaboration opportunities!



- **Name**: Bhushan Gawali

- **WhatsApp**: [+91 7743927365](http://Wa.me/+917743927365)

- **Email**: [[email protected]]([email protected])



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> **"Every data point is a stepping stone to a brighter insight. Keep analyzing, keep discovering!"** 🌟🔍