Every credit proposal tells the borrower's best-case story. Our Synthetic Data Narrative Network constructs alternative futures—plausible scenarios where their assumptions might not hold—so your credit committee can see the full range of repayment outcomes before extending capital.
Borrower projections are designed to secure capital. Every revenue forecast, margin assumption, and cash flow projection is framed to show comfortable debt service. You need to test the numbers, not just review them.
Building comprehensive stress scenarios manually takes days per deal. With proposal volumes, you can't stress-test every assumption—but your credit committee still expects to see what happens when projections slip.
Proposals present covenants in isolation. In reality, revenue shortfalls cascade through EBITDA, coverage ratios, and leverage tests simultaneously. A single missed assumption can trigger multiple breaches.
When borrower performance deteriorates, collateral values often decline in tandem. Static collateral coverage masks the correlation risk that emerges precisely when you need protection most.
The SDNN doesn't just stress-test ratios—it generates complete alternative narratives. Plausible futures where borrower assumptions break, with full causal chains showing exactly how repayment capacity degrades.
Borrower financials, projections, and loan documentation
Extract assumptions, stress covenants, generate downside scenarios
Stress scenarios with covenant triggers and recovery analysis
Automatically identifies the explicit and implicit assumptions embedded in borrower projections—the revenue growth, margin expansion, and working capital dynamics that debt service depends on.
Constructs scenarios where specific assumptions fail and traces through to covenant impact: "What if revenue grows 8% instead of 15%?" "What if working capital needs spike?" Each scenario shows exactly which covenants breach and when.
When an assumption changes, the SDNN traces all downstream effects through the cash flow waterfall. A revenue miss doesn't just affect top-line—it impacts EBITDA, debt service, covenant headroom, and liquidity reserves.
For each stress scenario, the SDNN models recovery outcomes—accounting for collateral value correlation, enforcement timelines, and restructuring costs. See loss severity alongside probability of default.
Submit the loan documentation. Receive stress scenarios that challenge every critical assumption.
Upload borrower financials, projections, loan terms, and covenant package.
SDNN extracts every assumption—explicit projections and hidden dependencies that drive debt service capacity.
Downside scenarios constructed with covenant triggers, breach timing, and recovery estimates.
Receive a complete stress analysis: scenarios compared, covenants tested, risks quantified.
From initial screening to final credit committee, challenge borrower projections with stress scenarios.
Rapidly identify which proposals warrant deeper analysis. Surface the assumptions that matter most and flag proposals where borrower projections are most vulnerable.
Full stress-test analysis before Credit Committee. Show the complete range of outcomes—from borrower projections to severe downside scenarios with covenant breach timing.
Use stress scenarios to structure appropriate covenant levels. Set thresholds that provide early warning while avoiding false triggers in realistic downside cases.
Re-stress existing credits when conditions change. Track which original assumptions still hold and identify credits requiring enhanced monitoring or restructuring conversations.
Your credit committee doesn't want opinions—they want analysis they can verify. Every stress scenario, every covenant test, every recovery estimate links back to the evidence that supports it.
Start with a single proposal or build stress-test analysis into your standard credit process.
Not another AI wrapper. A governed reasoning engine designed to challenge borrower projections with stress scenarios.
The core of stress scenario generation. Constructs complete alternative futures where borrower assumptions fail—not just sensitivity tables, but internally consistent narratives showing how cash flows degrade and covenants breach.
Patent Pending24 interconnected enterprise domains provide the semantic structure. When an assumption changes, the metamodel ensures all downstream effects cascade correctly through the cash flow waterfall.
Open Source FoundationEvery inference is constrained by explicit rules. Stress scenarios aren't hallucinated—they're constructed through auditable reasoning steps that your credit committee can verify and challenge.
Enterprise ReadySubmit a credit proposal and receive stress scenarios that test every critical assumption. See the downside cases borrowers aren't showing you.