AI that moves money needs a gate between the model and the ledger.
Bookbag blocks transactions above thresholds, holds high-value refunds for human approval, and produces EU AI Act + SR 11-7 + SOC 2 evidence from the same runtime traces — so your compliance team isn't reconstructing decisions weeks after the fact.
What keeps you up at night
Concrete risks Bookbag is built to mitigate.
Unauthorized transactions
An agent issues a refund, triggers a transfer, or sends a trade beyond its authority. Runtime enforcement is the only credible control — a post-hoc audit log is not a control.
Model risk management (SR 11-7)
The Fed expects documented controls on model inputs, outputs, and use. You need traceable evidence that every decision was gated, logged, and reviewable.
EU AI Act high-risk obligations
Credit scoring and lending are Annex III high-risk. Logging requirements, human oversight, transparency — these need evidence, not intent.
How Bookbag helps
Four products. Concrete capabilities. One data layer.
Block transactions above thresholds
Hold refunds > $X for approval. Block trades against policy. Redact PII before it leaves your walls.
Every decision is auditable
Timestamps, arguments, reviewer identity, reason — the evidence SR 11-7 expects you to produce on demand.
Score adherence to underwriting policy
Taxonomy-driven QA scores whether each output matches your underwriting rules. Flag outliers, feed training data back into the model.
EU AI Act + SR 11-7 evidence bundles
Pre-built controls for Annex III. Model risk documentation generated from runtime traces, not a consultant's Word doc.
Frameworks we auto-map for this industry
FAQs for this industry
Frequently Asked Questions
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