Practical AI for financial controls
AI has not replaced judgement in revenue assurance. It has removed the hours of preparation that used to sit in front of the judgement.
I did not come to Generative AI as a technologist. I came to it with a queue of monthly validation work that consumed days of skilled attention on tasks that were structured, repetitive and unforgiving of fatigue. That framing turned out to matter, because it decides what you build.
The workflows that have worked for me share a shape. The AI never decides whether a control passed. It gathers, structures and cross-checks — reading source extracts, raw MIS, SOPs and knowledge bases — and then presents the exceptions with the evidence attached. A human decides. That boundary is what makes the output auditable.
A four-agent EPRS validation workflow built on this principle removed around twenty-four hours of manual effort from monthly and quarterly audit cycles. A daily revenue validation workflow that checks dunning, processing fees, billing and reconciliation saves about six hours per run. CAS invoice validation, which extracts financial data from PDFs, images, Markdown and CSV and reconciles it against source records, saves roughly six hours a cycle. Executive reporting across eighteen product and KPI data sets saves about four days of preparation.
None of those numbers came from a clever model. They came from writing the SOP down properly. The single biggest predictor of whether an AI workflow works in a controls environment is whether the underlying process was ever documented precisely enough for a careful new joiner to follow. If it was not, the model will invent the gaps.
Retrieval matters more than model choice. Pointing a workflow at the actual knowledge base — structured data, folders, SOPs, prior exception history — via RAG and MCP integrations does more for accuracy than upgrading the model. So does forcing structured, JSON-shaped output: it makes results comparable run to run, and comparable results can be reconciled.
The honest limitations: these workflows are brittle when source formats change without warning, they need a fallback path when a source is unavailable, and they must log what they read. Treat an AI workflow like any other control — versioned, documented, evidenced and reviewed.
What I would tell anyone in finance starting out with this: pick the task you dread most in the close, document it until it is boring, and automate only the preparation. The judgement stays yours, and it gets better because you arrive at it rested.