AI for finance

AI for finance

Finance is a function where the cycle time is dominated by the assembly of data from many systems, the reconciliation across those systems, and the review of the assembled output. AI can compress each of these, but every number that goes into a report must be traceable to a source, and the function must continue to own the signed output.

How to think about this

Three operational zones carry most of the economic case:

• The close and reconciliation. The monthly close is dominated by data assembly and reconciliation across entities, currencies and systems. An agent that reads from the source systems, performs the routine reconciliations, and surfaces the exceptions for human review compresses the close from days to hours.
• Regulatory and management reporting. The reports that go to the board, the regulator, the auditors. An agent that assembles the report, cites the source of every number, and produces a draft for human review reduces the assembly time and improves the auditability.
• Variance analysis and FP&A. The continuous analysis of actuals against plan, with the explanations that go to the CFO. An agent that monitors the variances, investigates the non-routine ones, and prepares the explanations reduces the time between the close and the insight.

What this looks like in production

The agent integrates with the finance stack and works within the existing controls. Every number in every report is traceable to a source system and a timestamp. The agent’s outputs are drafts; the finance team signs off on the final report.

The variance analysis is done during the close, not after. The agent monitors the variances as the data lands, investigates the non-routine ones, and prepares the explanations. The finance team reviews the explanations and adds the judgement.

The reconciliation is performed by the agent, with the exceptions surfaced for human review. The agent does not silently write off reconciliation breaks. The exceptions are visible, classified and tracked.

When NOT to use this approach

• If the finance data quality does not support reliable assembly. The agent is only as good as the data it reads.
• If the finance team cannot commit to reviewing agent outputs before they are used. The agent is the drafter; the human is the signer.
• If the regulatory environment requires human-only preparation of certain reports. The agent should not be deployed in those report types.

What good looks like

• Monthly close compressed from days to hours, with the same or better accuracy.
• Reconciliation cycles shortened, with the exceptions visible and tracked.
• Regulatory reports assembled faster, with the source of every number traceable and the report signed off by the responsible officer.
• Variance analysis during the close, not after, with the explanations prepared by the agent and reviewed by the human.