Finance AI control review

Make control evidence part of the pilot.

Review ten control questions before a finance AI use case moves from experiment to operating workflow. Track what is defined, what is tested, and what still needs an owner.

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Current review

Forecast assumption monitoring

Continuously flag external or operating signals that could invalidate material planning assumptions.

0of 10 applicable controls tested
Control work required
10 not addressed0 defined0 tested
Open pilot charter

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01
Data boundary

Are permitted data sources, classifications, and prohibited inputs explicitly documented?

Evidence to inspect
Approved source inventory, classification decision, and masking or minimization rules.
Risk if missing
Sensitive or unreliable data enters the workflow without visibility.
02
Model and provider

Is the model, provider, hosting path, and retention behavior approved for this use?

Evidence to inspect
Provider assessment, approved configuration, contractual terms, and named technical owner.
Risk if missing
Finance information is processed in an unapproved environment or retained unexpectedly.
03
Source traceability

Can a reviewer trace material outputs back to the exact source data and assumptions?

Evidence to inspect
Citations, input snapshot, prompt or workflow version, and calculation lineage.
Risk if missing
Unsupported output enters a management, accounting, or operational decision.
04
Financial integrity

Are generated figures and material statements reconciled to governed finance records?

Evidence to inspect
Reconciliation procedure, tolerance thresholds, exception log, and reviewer sign-off.
Risk if missing
Generated output conflicts with the ledger, approved plan, or system of record.
05
Human authority

Is a qualified person accountable for review, approval, override, and escalation?

Evidence to inspect
RACI, approval point, override procedure, and training record.
Risk if missing
Automation makes or influences a material decision without accountable judgment.
06
Access and records

Are access, retention, change history, and review evidence controlled and auditable?

Evidence to inspect
Role-based access, activity logs, retention schedule, and version history.
Risk if missing
Unauthorized use or an incomplete audit trail weakens accountability.
07
Performance monitoring

Are quality, false negatives, exceptions, drift, and reviewer corrections monitored?

Evidence to inspect
Baseline, test set, operating thresholds, recurring review, and named metric owner.
Risk if missing
Performance degrades silently after the pilot or misses material exceptions.
08
Resilience

Can the finance process continue safely when the AI service or workflow fails?

Evidence to inspect
Manual fallback, recovery procedure, service dependency map, and continuity test.
Risk if missing
A provider or workflow failure interrupts a critical finance process.
09
Incident response

Are incorrect output, data exposure, control failure, and vendor incidents routed clearly?

Evidence to inspect
Severity definitions, escalation contacts, containment steps, and correction procedure.
Risk if missing
A material issue persists because ownership and response expectations are unclear.
10
Value and exit gate

Are benefit evidence, operating cost, scale criteria, and stop conditions agreed in advance?

Evidence to inspect
Baseline, benefit owner, total-cost view, day-90 decision record, and exit criteria.
Risk if missing
The pilot scales on enthusiasm rather than controlled, measurable value.

This review is a structured planning aid, not a certification, audit opinion, benchmark, or substitute for accounting, legal, privacy, security, technology, internal-audit, or model-risk review.