Elevate Your Risk Management
Your scenarios should tell you what could break your portfolio tomorrow, given what's happening today.
We process what your team can't — hundreds of signals, exposed in minutes, with full attribution.
Every Scenario, Six Layers of Context
Context is not a feature. It is the methodology. Every scenario passes through six independent layers before it reaches your desk.
Regulatory Context
Scenarios are anchored in DFAST, EBA, and PRA supervisory guidance. RAG retrieval extracts shock parameters directly from regulatory PDFs — not from training data.
Market Context
Live data from FRED, Yahoo Finance, and 15+ sources flows into every scenario. Credit spreads, yield curves, equity indices, and commodities provide the current baseline.
Market Intelligence
News sentiment, social signals, prediction markets, and SEC filings add forward-looking intelligence. The scenario is shaped not just by where markets are, but where they are heading.
Statistical Context
C-Vine copulas propagate primary shocks into secondary effects with statistical coherence. Every correlation is computed, not assumed. Every pathway is traceable.
Historical Context
20+ years of risk factor data for calibration against historical distributions. Every shock is benchmarked against real precedent.
Likelihood Intelligence
Quantitative scoring compares each scenario against current market conditions across four signal dimensions. The result: a 0-100 likelihood score with confidence bands.
Four Scenario Types, One Platform
From regulatory compliance to tail-risk exploration — choose the scenario type that matches your stress testing objective.
Historical
Replay past crises (2008 GFC, COVID crash, eurozone debt) with historically-calibrated shocks across all risk factors.
Regulatory
Aligned with DFAST, EBA, PRA, and Basel III frameworks. RAG over supervisory guidance for parameter extraction.
Hypothetical
Forward-looking scenarios driven by market intelligence, news sentiment, and prediction market signals.
Black Swan
Extreme tail-risk events with low probability but severe market impact. Test portfolio resilience at the boundaries.
See the Output
Context-rooted scenarios with full shock tables, narrative documentation, and audit trail.
DFAST Severely Adverse 2026
Severe global recession: equities fall 58%, unemployment rises to 10%, house prices decline 30%, and corporate bond spreads widen to 5.7 pp.
SHOCK_TABLE
| FACTOR | CLASS | TYPE | MAGNITUDE |
|---|---|---|---|
| SP500 | Equity | PRIMARY | -58.00% |
| US_TREASURY_10Y | Rates | PRIMARY | -180.0 bps |
| EUR_USD | FX | PRIMARY | -15.00% |
| UNEMPLOYMENT | Macro | PRIMARY | +5.5 pts |
| HOUSE_PRICE_INDEX | Macro | PRIMARY | -30.00% |
| BBB_SPREAD | Credit | SECONDARY | +470.0 bps |
| VIX | Volatility | SECONDARY | +52.0 pts |
| US_TREASURY_3M | Rates | SECONDARY | -360.0 bps |
| CRE_PRICE_INDEX | Macro | SECONDARY | -39.00% |
| GDP_GROWTH | Macro | SECONDARY | -4.60% |
From Scenario to P&L Impact
See how stress scenarios ripple through real portfolio positions with instant impact attribution.
How Context Flows Through the Pipeline
Five stages, each adding a layer of context. From regulatory grounding to likelihood scoring, every stage enriches the scenario with verified, traceable intelligence.
RAG Analysis
Regulatory context extracted from your uploaded documents.
Market Intelligence
News, filings, prediction markets, and macro data build the current market context.
Economist Debate
Three AI economists debate primary shocks grounded in market context.
Copula Propagation
Statistical context via C-Vine copulas computes secondary shocks.
Output Generation
Validated shock tables with full provenance and audit trail.
Likelihood Ranking
Live market context scores each scenario for current-conditions alignment.
RAG Analysis
Regulatory context extracted from your uploaded documents.
Market Intelligence
News, filings, prediction markets, and macro data build the current market context.
Economist Debate
Three AI economists debate primary shocks grounded in market context.
Copula Propagation
Statistical context via C-Vine copulas computes secondary shocks.
Output Generation
Validated shock tables with full provenance and audit trail.
Likelihood Ranking
Live market context scores each scenario for current-conditions alignment.
We built StressGen because we think scenarios are the most accurate risk signals.
Powered by a causal engine that models how shocks propagate — not just how they correlate.
What Happens Between Your Prompt and Your Scenario
Every run orchestrates 10 specialized capabilities. Here is what fires under the hood.
Structured Extraction
AI reads unstructured text — regulatory guidance, analyst notes, macro commentary — and extracts structured risk parameters.
News Intelligence
Hundreds of news articles processed per run through topic-focused search agents, distilled into scenario-relevant intelligence.
SEC Filing Analysis
Corporate filings parsed for earnings guidance, risk disclosures, and forward-looking statements that shape sector-level shocks.
Prediction Market Signals
Forward probabilities from Kalshi, Polymarket, and other prediction markets quantify event likelihood in real time.
Live Risk Signal Aggregation
Tens of live signals — credit spreads, yield curves, VIX, MOVE, CDS — aggregated into a coherent market baseline.
Causal Transmission Engine
A directed acyclic graph models how shocks propagate: trade wars to supply chains, rate hikes to mortgage defaults, contagion across borders.
Risk-Focused Web Search
Exploration agents run targeted web searches, filtering for risk-relevant content rather than generic results.
Statistical Cross-Validation
Every shock is cross-validated with correlation analysis, Granger causality tests, and copula-based dependency propagation.
Hybrid Regulatory RAG
Dense and sparse retrieval over 200+ pages of DFAST and EBA guidance ensures scenarios meet supervisory constraints.
Portfolio Impact Dashboard
See exactly what moved, why it moved, and how each shock flows through your portfolio with full attribution.
And all of that...
in minutes.
Beyond Generation: Analyze, Query, Understand
Generating scenarios is step one. StressGen also lets you interrogate them, query the regulatory sources behind them, and understand how they relate to current market conditions.
Scenario Analysis
Ask questions about any generated scenario in natural language. Why was this shock chosen? How does it compare to 2008? What happens if the Fed cuts rates instead? The AI answers grounded in the scenario data, not general knowledge.
Regulatory Document Querying
Query your uploaded regulatory documents directly. Extract shock parameters, severity benchmarks, and scenario design constraints from primary regulatory sources — not from LLM training data.
Market Conditions Analysis
Understand what current market conditions mean for your scenarios. Live data from 15+ sources is synthesized into actionable intelligence: which scenarios are most likely, which signals are flashing, and what to watch next.
Market Context: 12+ Sources, One Coherent Signal
Real-time macro indicators, news sentiment, market data, and prediction markets — all feeding into every scenario as live context.
FRED
ECB
Federal Reserve
News Providers
Yahoo Finance
Kalshi
Polymarket
ChromaDB RAG
Twitter / X
Coming soonBloomberg
Coming soonReuters
Coming soonRegulatory Context: Built on Primary Sources
Alignment is not a badge — it is a methodology. Every scenario is calibrated against your uploaded regulatory documents, extracted via RAG, not training data.
Dodd-Frank Act Stress Testing — generates scenarios consistent with the Fed supervisory stress test methodology.
European Banking Authority EU-wide stress test — scenarios calibrated to EBA adverse and baseline methodology across 64+ banks.
Covers all 28 DFAST-required variables — 16 domestic macro and 12 international path variables.
Nonbank financial stress pathways and forward-looking tail risk analysis beyond the core supervisory scenarios.
Risk Factor Coverage
- •90+ risk factors across 8 categories
- •55 primary + 39 secondary factors
- •Equity, rates, FX, commodities, credit, volatility, macro, inflation
Cross-Asset Propagation
- •Archimedean copulas (Clayton, Gumbel, Student-t)
- •Primary → secondary shock transmission
- •Correlation-aware dependency modeling
Regulatory RAG
- •Vector search over your uploaded regulatory documents
- •Shock parameter extraction from DFAST supervisory guidance
- •Severity benchmarks and scenario constraints
Methodology That Answers ‘Where Did This Number Come From?’
Every shock is debated, propagated through copulas, and documented with full provenance for regulatory examination.
Economist Debate
Three AI economists with distinct macro perspectives propose and challenge primary shocks over multiple rounds, converging on a consensus through structured argumentation.
C-Vine Copulas
Cross-asset dependency propagation using Archimedean copulas (Clayton, Gumbel, Student-t) to compute secondary shocks from primary factor movements.
Regulatory RAG
Vector search over ingested DFAST and EBA guidance documents to extract shock parameters, severity benchmarks, and scenario design constraints.
Likelihood Ranking
Quantitative scoring compares each scenario against live market conditions across four signal dimensions to produce a 0-100 likelihood score with confidence bands.
PII Sanitization
All user data is sanitized before reaching LLM providers. No personally identifiable information leaves your environment.
Audit Trail
Full scenario provenance — every shock traced from economist proposal through copula propagation to final validation.
See Context-Rooted Scenarios for Your Portfolio
Schedule a technical demo with our team. We'll walk through scenario generation, regulatory alignment, and how context flows through every layer.