Case Study | December 2025
From Raw Data to Securitization
AI-Powered Credit Risk Analysis with Power BI, MCP Tools, and Quarto
Watch how modern AI assistants transform semantic model data into actionable structured finance insights—in a single session.
The Challenge
This case study explores an end-to-end workflow that showcases the power of combining GitHub Copilot with specialized Model Context Protocol (MCP) tools to perform sophisticated financial analysis.
Starting with nothing but a Power BI semantic model containing consumer loan data, we built a complete securitization analysis—including tranche structuring, pricing, and risk assessment.
What We Covered
Connecting to Power BI
Connected to a Lending Bank SSAS tabular model on Power BI Premium. Discovered models, explored schema, and executed DAX queries.
Portfolio Analytics
Analyzed 2015 loan vintage: $6.42B across 421,000 loans. Grade distribution, default rates, interest spreads, geographic concentration.
Statistical Analysis
Correlation, regression, ANOVA, t-tests, and clustering using MCP Statistics tools. Found 0.986 correlation between rate and default.
Quantitative Finance
Bond pricing, SOFR swap analysis, VaR calculations (99% VaR = $141M). Full interest rate environment context.
Securitization Design
Designed 4-tranche ABS structure with cash flow waterfall, stress testing, and pricing recommendations.
Quarto Documentation
Generated publication-ready document with Python visualizations, LaTeX formulas, and self-contained HTML.
Advanced Statistical Analysis
Using specialized MCP tools, we performed rigorous statistical analysis:
| Analysis | Tool | Finding |
|---|---|---|
| Correlation | mcp_statistics_correlation |
Interest rate vs default rate: r = 0.986 |
| Regression | mcp_statistics_regression |
ROI = 3.28% + 0.35% × Interest Rate (R² = 93.5%) |
| ANOVA | mcp_statistics_anova |
Significant grade differences (p = 0.032) |
| T-Tests | mcp_statistics_test |
High-yield grades: 3.5× higher defaults |
| Clustering | mcp_ortools_analytics |
3 natural risk clusters identified |
Securitization Structure
We designed a 4-tranche ABS structure optimized for the portfolio characteristics:
Including cash flow waterfall analysis, scenario stress testing, and pricing recommendations
The Tech Stack
🤖 GitHub Copilot
Claude Opus 4.5 orchestrating the entire analysis workflow
📊 Power BI MCP Server
Connect to SSAS/Power BI semantic models via DAX
📈 MCP Statistics
Hypothesis testing, regression, ANOVA, correlation
⚙️ MCP OR-Tools
Optimization, clustering, time series analysis
💰 MCP Quantitative Finance
Bond pricing, VaR, derivatives, portfolio analytics
📄 Quarto
Professional document generation with Python & LaTeX
Key Takeaways
MCP Extends AI Capabilities
Beyond code generation, specialized tools enable domain-specific analysis in statistics, finance, and optimization.
End-to-End Workflows
From data connection → analysis → visualization → documentation in a single conversational session.
Mathematical Rigor
All calculations verified programmatically, ensuring the analysis is trustworthy and auditable.
The Result
A complete, professionally-formatted securitization analysis document ready for:
- ✅ Investment committee presentations
- ✅ Regulatory submissions
- ✅ Investor roadshow materials
- ✅ Internal risk management review
Try It Yourself
The MCP ecosystem is rapidly expanding. Explore our tools and see what you can build.