Preserve throughput
Low-risk operations auto-approve and execute at machine speed. Authority controls do not create approval bottlenecks for routine actions.
Use Cases / Financial
AI trading agents, treasury systems, and portfolio automation execute at scale with deterministic controls. Every financial AI action is risk-scored, authority-gated, and recorded with immutable audit evidence for regulatory examination.
AI-driven trading, treasury management, and portfolio rebalancing are growing rapidly. Regulatory frameworks require evidence that every action was authorized, that risk was assessed, and that human oversight was applied to high-risk decisions. Intended produces this evidence automatically as part of the enforcement loop.
Low-risk operations auto-approve and execute at machine speed. Authority controls do not create approval bottlenecks for routine actions.
High-value transfers, position limit changes, and cross-market trades escalate to human approvers with full risk context.
Every decision produces a hash-chained audit record and HMAC-signed evidence bundle. Regulators examine evidence, not dashboards.
trade.execute → equity-portfolio (staging)
Below threshold, non-production, reversible. Auto-approved per policy fin-trading-v2.
treasury.transfer → fx-settlement (production)
Within position limits, production but low blast radius. Approved with evidence bundle.
trade.execute → equity-block (production)
Exceeds single-trade threshold. Routed to trading desk lead for approval.
risk.override_limit → portfolio-wide
Exceeds deny threshold. Position limit overrides require manual workflow.
Immutable hash-chained evidence for every financial AI action. Authority decisions link directly to compliance controls. Evidence bundles are exportable for external audit.
7-year immutable retention architecture. S3 Object Lock (Compliance mode) prevents deletion. Audit chain verification available on demand.
Replayable decision paths document exactly why each trade was authorized. Risk scores, policy rules, and approval chains provide complete execution rationale.
Quantitative risk scoring for every AI action. Risk factor decomposition provides the granular data operational risk models require.