Using LLMs as judges for validating deal cash flow models: A new frontier in securitization modeling
As securitization models become increasingly complex and differentiated, validation becomes a critical challenge. We’ve experimented with an innovative approach that leverages large language models (LLMs) as impartial judges to validate models implemented across different platforms.
The Dual-Implementation Challenge
In cash flow modeling, we often maintain parallel implementations—typically in Python for flexibility and Excel for transparency. How do we ensure both versions produce consistent results?
Enter the “LLM as Judge” approach!
A Real-World Case Study: Residential Transition Loan Funding
Consider a portfolio of residential transition loans with a funding structure including:
- 100 loans averaging $275,000 each
- 12-month average terms at 8.75%
- A 75% advance rate
- 2% loss reserve build-up
- Performance triggers based on delinquency rates
We implemented this structure in both Python and Excel, then submitted both models to an LLM for validation.
The LLM Validation Process
The LLM first analyzed the conceptual alignment between models, confirming both followed the same fundamental approach to cash flow projection, default assumptions, reserve mechanics, and triggers.
Next came a rigorous numerical comparison. The LLM detected a $100,000 investor distribution discrepancy in Month 2:
- Python model: $1,790,702
- Excel model: $1,690,702
Through logical analysis, the LLM determined this likely stemmed from differently evaluated trigger conditions. This kind of subtle implementation difference could easily go unnoticed in manual validation, potentially leading to significant valuation discrepancies over time.
Beyond Discrepancy Detection
The true power of this approach extends beyond finding differences. The LLM also provided:
- Stress testing recommendations tailored to our specific product, including scenarios for rapid defaults, extension waves, and interest rate shocks
- Model risk management insights highlighting documentation needs and suggesting a formal reconciliation process
- Code quality assessment noting strengths and weaknesses in both implementations
Why This Matters
For securitization professionals, this approach offers several advantages:
- Efficiency: Automation of tedious line-by-line comparisons
- Comprehensiveness: Identification of conceptual differences, not just numerical ones
- Regulatory compliance: Better documentation for model risk management requirements
- Objectivity: Unbiased third-party perspective
