How to use AI in accounting and financial processes

Using AI in accounting and financial processes requires more control than creativity. The goal is to reduce reading, sorting and manual searching without allowing a probabilistic output to alter records or move money outside of company rules.

Reading and classifying documents

AI extracts fields from notes, receipts, contracts and receipts, relates documents and separates differences. Fields such as CNPJ, value, tax and maturity must undergo deterministic validations. When confidence is low or the rule fails, the item goes for review.

Conciliation and closing

Templates help suggest matches, explain variations, and summarize issues. The final release remains subject to approval and segregation of duties. A good copilot shows the origin of the numbers and the path used to arrive at the recommendation.

Accounts payable, receivable and billing

AI can prioritize titles, prepare messages and summarize trading history. Payment, bank changes, discounts and write-offs require authentication, limits and double approval depending on the risk. Never allow instructions received via email to replace financial controls.

Planning and analysis

In FP&A, AI accelerates questions about budget, realized and scenarios. It can generate initial explanations, but it must not invent causality. Connect each statement to the source data and keep scenario assumptions visible.

Control architecture

Separate four layers: extraction, validation, recommendation and execution. Give each one specific permissions. Record input, model version, sources, output, approval and action taken. NIST highlights documentation, responsibilities, and ongoing monitoring as elements of AI governance.

Metrics to track

Measure time per document, correct extraction rate, discrepancies found, rework, closing time, delay and cost per transaction. Add controls: human corrections, exceptions, unauthorized access and incidents. Economy without reliability is false efficiency.

Start with a high-volume, low-autonomy process like grading and assisted conferencing. Only proceed to execution after proving quality, permissions and reversibility. See also how to protect business data in AI applications and backoffice automation.