Inside the Underwriting Engine
How raw deal inputs become a verified, decision-ready underwriting package — analyzed at effectively unlimited depth, in a single structured pass.
Using Anthropic's Claude, I developed an AI-powered underwriting engine that analyzes investment data at effectively unlimited depth. Built on a library of maintained, reusable skills, it ingests and cross-references source data, back-checks every figure for consistency, and produces a detailed underwriting model — complete with risk-management metrics, clear conditions for approval, and a concise summary of the deal.
One structured pass, four phases
The engine moves top to bottom — intake, market context, deep financial analysis, and finally the model and its decision. Each figure carries an automated back-check as it flows down the pipeline.
Data loading
Financial data loading
Source financials, rent rolls, and deal documents are ingested into the model as the analysis baseline.
Market & asset analysis
Location analysis
Submarket, positioning, and the locational strengths and risks of the asset.
Market liquidity analysis
Depth of buyers, sellers, and financing — how readily the asset can transact.
Appraisal analysis
Valuation inputs and appraised value tested against the underwriting assumptions.
Market comparables
Benchmarked against comparable activity across four dimensions.
Deep financial analysis
Asset & guarantor deep-dive
Detailed analysis at the asset level and on personal guaranty items, with automated back-check and cross-referencing of every figure.
Model & output
Full-picture underwriting model
All available data is consolidated into a complete underwriting model database — one coherent view of the deal.
Risk, approval & exit
Risk-management metrics, clear conditions for approval, and a defined exit strategy.
Hours of manual analysis compressed into minutes of verified, decision-ready output — every figure cross-referenced and back-checked before it reaches a conclusion.
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