~
0
%
Faster underwriting model build
~3 days
Model build time, down from approximately 5 days
Building Institutional-Grade Credit Models with Claude
About the Client
The client is a private credit firm underwriting prospective transactions across an active diligence funnel. Its evaluation process involves building detailed credit models, analyzing capital structures, testing covenants, and assessing returns under different operating and financing scenarios.
Business Challenge
Key challenges to building complex, consistent credit models
1. Heavy modeling workload
Building 12+ tab credit models from scratch was the heaviest lift in diligence, requiring approximately five days per deal
2. Fragmented raw inputs
CIMs, financial packages, and DDQ responses arrived as disjointed data dumps requiring synthesis before modeling
3. Client-specific standards
Models had to reflect strict EBITDA-based underwriting guidelines, capital structure conventions, and the client’s credit philosophy
4. Judgment and consistency requirements
Structural risks and downside cost pliability required experienced credit judgment and formatting consistency with prior work.
Strategic Approach
TresVista's Solution: AI-assisted model construction in a structured Claude CoWork environment
TresVista created an AI-assisted model-building workflow within a structured, single-environment CoWork navigation system carrying a deep institutional persona, task-specific inputs, and precedent materials.
01
Persona and context ingestion
A 20+ page brief embedded the client’s underwriting guidelines and credit philosophy into the working environment
02
Centralized source ingestion
CIMs, financial packages, and DDQ responses were organized within structured deal-file repositories
03
Dynamic model construction
The workflow supported a 12+ tab model with debt and revolver schedules, net working capital, and excess cash flow sweeps
04
Scenario, returns, and reconciliation
Outputs included Net Leverage and FCCR testing, MOIC and XIRR analysis, and human reconciliation against a Past Work library
TresVista Impact
Shorter build cycles. Dynamic analysis. Institutional consistency.
Before
100%
Baseline Effort
After
65%
Of Baseline Effort
18%
Execution effort:
~35% Efficiency gain in execution effort
Reinvested in higher-value analytical work.
Outcome
- Reduced underwriting model build time from approximately five days to approximately three days
- Automated the heaviest lift of diligence to produce a bespoke model and LTV analysis from raw data
- Improved institutional consistency by grounding the AI workflow in a client-specific persona and precedent work
- Created a dynamic deliverable in which terms, leverage, and pricing grids could flex ahead of Investment Committee discussions
Tools & Technologies
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