Requirement Document for Bank Loan Power BI Report
1. Introduction
The Bank Loan Report Dashboard in Power BI provides an in-depth analysis of loan applications, funded amounts, and received amounts. It allows financial institutions to track loan performance, risk assessment, and trends based on multiple parameters such as loan status, grade, purpose, and customer attributes.
2. Objective
The primary objective of this dashboard is to provide a data-driven visualization that enables:
- Tracking loan applications, funded amounts, and received amounts.
- Identifying loan performance by status (Fully Paid, Charged Off, Current).
- Analyzing good vs. bad loan trends.
- Evaluating average interest rate and debt-to-income (DTI) ratio.
- Monitoring loan distributions across regions, terms, and customer attributes.
3. Dashboard Components
The Power BI dashboard consists of the following key sections:
3.1 Key Performance Metrics Overview
Displays overall banking loan statistics:
- Total Loan Applications
- Total Funded Amount
- Total Amount Received
- Average Interest Rate (%)
- Average Debt-to-Income (DTI) Ratio
- Good Loan Issued vs. Bad Loan Issued
3.2 Loan Status Breakdown
A tabular breakdown of loans categorized by status:
- Fully Paid
- Charged Off
- Current
- Good vs. Bad Loan Performance
3.3 Monthly Trends Analysis
A line chart depicting trends in loan applications, funded amounts, and received amounts for each month.
3.4 Loan Distribution by Categories
- By Loan Purpose (Debt Consolidation, Credit Card, Home Improvement, etc.)
- By Home Ownership (Mortgage, Rent, Own, Other)
- By Employee Length
- By Loan Term (36 months vs. 60 months)
- By State (Geographical View)
4. Data Sources & Integration
- Primary Data Source: Loan transaction databases (SQL Server, Excel, or API feeds).
- Data Refresh Frequency: Daily/Weekly as per business requirements.
- Filters & Slicers:
- Loan Status
- Loan Grade
- Loan Purpose
- Term (36 months / 60 months)
- Home Ownership
- Employee Length
- State
5. Functional Requirements
5.1 Interactive Features
- Clickable drill-through for deeper insights into loan data.
- Dynamic filtering to refine data based on specific criteria.
- Hover tooltips displaying additional information.
5.2 Data Comparisons
- Month-over-Month (MoM) percentage change for key metrics.
- Year-over-Year (YoY) analysis for trend forecasting.
5.3 Conditional Formatting
- Highlight high-risk loans (bad loans) with red indicators.
- Use color-coded performance indicators for quick analysis.
5.4 Export & Sharing
- Export reports in PDF, Excel, and PowerPoint.
- Publish to Power BI Service for secure cloud-based access.
5.5 Security & Access Control
- Role-based access control (RBAC) to restrict sensitive data.
- Row-level security (RLS) for personalized data visibility.
6. Project Completion Method
The implementation will be completed using SQL Queries, Power Query, and DAX:
6.1 SQL Queries
- Data Extraction: Optimized SQL queries to pull loan application data.
- Data Transformation: Cleaning and structuring loan datasets before loading.
- Aggregations & Joins: Combining multiple loan tables for analysis.
- Example Query:
SELECT LoanID, LoanStatus, LoanGrade, FundedAmount, InterestRate, DTI, LoanPurpose, Term FROM LoanData WHERE IssuedDate BETWEEN '2023-01-01' AND '2023-12-31';
6.2 Power Query (ETL Process)
- Data Cleaning: Handling missing values, duplicate records, and inconsistent data.
- Data Transformation: Changing data types, renaming columns, and merging tables.
- Scheduled Refresh: Automating data updates for real-time insights.
6.3 DAX Calculations
- Key Performance Metrics:
Total Funded Amount = SUM(LoanData[FundedAmount]) Total Received Amount = SUM(LoanData[ReceivedAmount]) Good Loan Percentage = DIVIDE([Good Loan Applications], [Total Loan Applications], 0) - Time Intelligence Measures:
MoM Funded Growth = VAR CurrentMonth = SUM(LoanData[FundedAmount]) VAR LastMonth = CALCULATE(SUM(LoanData[FundedAmount]), DATEADD(LoanData[IssuedDate], -1, MONTH)) RETURN DIVIDE(CurrentMonth - LastMonth, LastMonth, 0)
7. Expected Outcomes
By implementing this Power BI Loan Report, the stakeholders will:
- Gain real-time insights into loan performance.
- Enhance risk assessment by analyzing bad loan trends.
- Improve decision-making using interactive loan data analysis.
- Track monthly and yearly loan funding trends.
- Ensure compliance and security through access control mechanisms.
8. Conclusion
The Bank Loan Report Dashboard serves as an essential financial tool for evaluating loan health, profitability, and risk. With interactive visualizations and predictive analysis, it will empower financial institutions to make informed lending decisions efficiently.
Sample bank loan data file for Power BI analysis:
📂 Download Sample Bank Loan Data
This dataset includes Loan ID, Loan Status, Loan Grade, Funded Amount, Received Amount, Interest Rate, Debt-to-Income (DTI) Ratio, Loan Purpose, Loan Term, Home Ownership, Employee Length, State, and Issued Date.