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Loan Portfolio Dashboard

A Power BI report that surfaces portfolio health, risk drivers, and performance patterns in minutes.

Tools: Power BI • DAX • Power Query Role: Solo builder Users: Friends and testers exploring lending datasets

Problem

Teams needed a clear view of loan performance and risk indicators without digging through tables. They lacked a fast way to see where defaults cluster and which traits matter most.

Solution

I designed a Power BI model (star schema) with DAX measures for delinquency, charge-offs, and cohort views. Interactive pages show portfolio KPIs, borrower traits, and performance by term, region, and verification.

Impact

The dashboard highlighted that long-term loans (60 months) carried higher defaults and that higher DTI correlated with risk—insights that guided filtering strategies and credit policy experiments.

Role & Context

Solo project over ~2 weeks. Built for practice with lending data and feedback from peers. Tech: Power BI, DAX, Power Query.

RESULTS & KEY INSIGHTS

  • Portfolio Overview: 71.8% of loans fully paid; 13.7% charged off
  • Risk Analysis: Long-term (60-month) loans show higher default counts
  • Financial Impact: Charged-off borrowers received $13.7M but repaid only $4.2M
  • Borrower Traits: Home ownership and verification status significantly impact default rates
  • Performance Patterns: Borrowers with multiple accounts show increased risk
  • High-Risk Indicators: High-risk borrowers correlate with higher DTI and lower income levels

KEY FEATURES

  • Portfolio Overview Dashboard: Comprehensive view of overall portfolio health and metrics
  • Loan Status Analysis: Detailed breakdown of loan statuses with trend analysis
  • Risk & Performance Metrics: Advanced risk assessment metrics and performance indicators
  • Borrower Profile Analysis: Analysis of borrower characteristics and demographic patterns
  • Key Takeaways Page: Actionable insights and recommendations for decision-making
  • Interactive Filtering: Cross-filtering capabilities across all visualizations

RECOMMENDATIONS

  • Focus on reducing long-term loan defaults by tightening approval criteria
  • Implement stricter verification processes for high-risk borrower segments
  • Monitor DTI ratios closely to identify potential defaulters early
  • Develop targeted financial literacy programs for high-risk borrowers
  • Explore predictive modeling to forecast loan performance

SKILLS DEMONSTRATED

Data Cleaning Data Preparation Data Modeling Star Schema Design DAX Calculations Power Query Data Visualization Interactive Dashboards Risk Analysis Financial Analytics Portfolio Analysis Data Storytelling Cross-Filtering

TECHNOLOGIES USED

Power BI Power Query DAX Data Modeling Star Schema Excel

Project Video

Gallery

Power BI loan portfolio dashboard overview showing portfolio health metrics, loan status distribution, and key performance indicators
Loan status analysis dashboard displaying fully paid, current, charged off, and defaulted loan percentages
Risk and performance metrics dashboard analyzing loan delinquency rates, charge-offs, and portfolio risk indicators
Borrower profile analysis dashboard showing default rates by home ownership, verification status, and debt-to-income ratios
Key insights and recommendations dashboard summarizing loan portfolio performance and risk mitigation strategies

CHALLENGES & SOLUTIONS

Challenge: Data gaps - Limited dataset size (5,000 records) may not generalize to larger portfolios
Solution: Designed scalable data model with star schema that can accommodate larger datasets. Used statistical methods to ensure insights are robust despite sample size.
Challenge: Feature scope - Some borrower attributes (e.g., credit score) were unavailable
Solution: Created derived metrics using available data (DTI, income, loan terms) to assess risk effectively. Implemented DAX calculations to compensate for missing attributes.
Challenge: Tool constraints - Power BI's interactivity is limited compared to custom-built dashboards
Solution: Leveraged Power BI's native interactivity features including cross-filtering, drill-through pages, and dynamic visualizations to maximize user experience.

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