Use cases / Finance and operations
AI Finance
Faster close and clearer forecasts, with people accountable for the numbers.
01
Problem
Finance teams reconcile by hand across systems and explain variances late. Automation that can post entries unattended is a control risk.
02
AI capability
Match transactions, explain variances, draft journals and commentary, and flag anomalies for review.
03
Data
The agent works from the context you connect, not from the open internet by default.
- General ledger (read)
- Sub-ledgers and bank feeds
- Invoices and contracts
- Budgets and forecasts
04
Agent
Finance Agent
Prepares reconciliations, variance explanations and draft entries for accountants.
05
Workflow
- 1. Pull transactions
- 2. Match and reconcile
- 3. Flag exceptions
- 4. Draft entries and commentary
- 5. Accountant review
- 6. Post by a person
06
Governance
Governance runs inside the agent at runtime: policy changes what it can actually do.
Can
- Read ledgers and sub-ledgers
- Reconcile and explain variances
- Draft journal entries
- Flag anomalies
Cannot
- Post journals
- Make or release payments
- Change chart of accounts
- Approve its own entries
Requires approval
- Any journal posting
- Adjustments above threshold
- Access to payroll data
Records
Agent identity, Data accessed, Model used, Output, Tools called, Policy applied, Decision, Approval, Action, Outcome.
Suggested starting autonomy: L2 Approve
Start at L2 so accountants approve every entry; consider L3 for low-value reconciliation matches.
07
Outcome
What you measure, so the agent earns more autonomy on evidence:
- Days to close
- Unreconciled items at period end
- Review corrections per batch
- Anomalies detected before posting
Questions
Can the agent move money?
No. Payments and postings are restricted actions and need a person.
Is there an audit trail for entries?
Each draft records the data read, model used, policy applied and the reviewer who approved it.