AI policy and controls for accounting

The policy names which finance tasks may be automated and which require a person by default, task by task rather than as a blanket statement. Human-in-the-loop checkpoints are defined for every automated process currently in scope, stating exactly where review sits and what happens to anything flagged. Data-handling rules cover what each AI tool can access, whether that data trains a model shared with other customers of the vendor, and where it is stored. An audit trail standard ties every automated action back to what triggered it, confirming what your current tools already log and closing any gap. A review cadence is set so the policy keeps pace with new tools rather than describing a setup that has since changed.

We start by inventorying every AI-assisted tool currently touching your finance processes, bank categorization rules, invoice extraction, draft commentary generation, and mapping exactly what each one does and where human review already happens informally. That informal reality gets written down as a formal checkpoint, tightened where the current review is inconsistent. Data-handling terms for each vendor get reviewed directly against their own documentation, not assumed. An audit trail standard is drafted against what your tools currently log, with gaps flagged for either a settings change or a documented manual workaround. The finished policy is reviewed with your team before it is finalized, and a review cadence is agreed before the engagement closes.

Finance teams that already use AI tools on their books and need a written policy a controller, lender or investor can actually review are the clearest fit, especially ahead of a financing round, an audit, or a due diligence process where the question is coming regardless. Teams about to adopt their first AI automation tool benefit from writing the policy before rollout rather than after, since retrofitting controls onto a tool already in production is harder than building them in from day one. Fintech and payments businesses, and any company handling investor or lender reporting regularly, tend to need this earlier than others because the question comes up sooner.

The most common problem is an AI tool in daily use with no written policy at all, just a set of settings someone configured once and nobody has documented since. The second is a policy that exists but says something vague like humans review everything, without naming the specific checkpoint or what happens when review fails, which does not survive a real diligence question. The third is data-handling terms nobody actually read when the tool was adopted, discovered only when a lender's checklist asks directly whether client data trains a shared model. The fourth is a policy written once and never revisited, describing a setup from a year ago while three new tools have since been added without any review.

The policy is written against whatever AI-assisted tools actually touch your finance processes today, most commonly bank categorization inside QuickBooks Online or Xero, invoice or receipt extraction tools, and NetSuite-based reporting automation where that applies. Vanta or a similar compliance platform is used where you already track controls for a SOC or similar framework, so the AI policy sits alongside existing control documentation rather than as a separate, disconnected artifact. The specific tools in scope are named directly in the policy, not described generically.

The policy engagement is priced as a fixed-fee project scoped to the number of AI-assisted tools and processes in scope, since a business running one categorization tool needs less review than one running several tools across bookkeeping, AP and reporting. It is priced separately from ongoing bookkeeping, AP or controller-level services, though it is often scoped alongside them for clients already engaging us on those. Current published rates for ongoing accounting services are on the pricing page; the policy engagement itself is quoted directly once the tool inventory is known.

The real test is whether the policy survives contact with an actual outside reader: a lender's underwriter, an investor's finance lead, or your own external accountant asking how a specific automated posting got approved. A policy that only holds up when nobody asks a follow-up question has not done its job. We check this by tracing a sample transaction through the documented checkpoint and audit trail before the engagement closes, confirming the policy describes what actually happens, not an idealized version of it.

A policy written for the tools in place today is only as good as the review process that keeps it current. The review cadence set at the end of the engagement triggers at minimum whenever a new AI tool is added to a finance process, since that is the moment the existing policy is most likely to miss something the new tool actually does. Some clients fold this review into an existing quarterly close or controls check, others treat it as its own standalone trigger tied to procurement decisions rather than a calendar date.