AI in corporate finance: what to automate and what not to hand to a model

Three baskets: automated, never delegated, human-checked

Three examples from deployments

1. 1A registry-filing parser – over a dozen companies with one methodXML e-filings have one structure but three variants (small, other, micro entity) and different notes. A script loads the balance sheet, P&L and cash flows, reconciles them and computes fourteen ratios in five modules with a rating. No language model here – pure code, because the structure is known. Effect: a comparative analysis of a dozen companies in an hour instead of a week; same ratios, same method, zero retyping.
2. 2A ledger controller for an accounting office – a hundred rules instead of an eyeThe accounting-system export, the chart of accounts and the accounting policy as inputs; around a hundred rules (VAT and deadlines, counterparties against the official register, e-invoicing, central-bank rates, accounts that should never carry a balance) and several hundred tests. The language model appears in one place: for an unclear posting description it proposes a classification the accountant approves. Effect: errors visible before month-end, the accountant works on exceptions, a dashboard for the office owner shows which company failed what today.
3. 3A management report from the model – one number in three documentsA script reads the Excel model (management P&L, allocation, liquidity), builds an HTML dashboard, a PDF report and a presentation – in Polish and French for an owner abroad. Closing checks (segments sum to the P&L, the balance sheet balances) stop publication when something does not match. The language model writes a first draft of the “what changed” commentary; I correct and sign it. Effect: report closing from a week to an hour, no more three versions of the same margin.

Why the model must not compute

A language model predicts the next piece of text. For “2 × 3” it will predict “6”, having seen it a million times; for “7.96 + 14.75 discounted at 11.3 %” it will predict something that looks like a result – and be wrong by a few percent in a way you cannot see. In a valuation a few percent is hundreds of thousands. That is why in my tools the model never returns a number that goes into a report: it returns a classification, a proposal or text, and code with a test does the computing. That is not caution – it is the difference between a tool and a risk.

A checklist before deployment

1. Which single report eats the most hours? Start there – not with an “AI strategy”.
2. Does data enter through one gate with validation and a log? If not, the gate comes first.
3. Where are personal data and salaries – and can the model reach them? It must not.
4. Which controls can be written as a rule with a test? All of those go to code, not to the model.
5. Who signs the result? If the answer is “nobody, it is automated”, the deployment is not ready.
6. Is there a register of pitfalls and refresh instructions? The tool must outlive its author.

Service: finance automation and AI