Using AI to Speed Up the Month-End Close

The month-end close is one of the most time-consuming recurring processes in the finance function, and one where AI can genuinely help to speed things up — if it is used for the right parts of the close, with the right safeguards. AI cannot run the close autonomously or replace the judgement, control and accuracy the close requires, but it can accelerate parts of it, freeing time and easing the pressure of the close cycle. This guide gives an honest view of both where it helps and where the finance professional must remain firmly in control.

It is written for Financial Controllers, Finance Managers and close teams looking to make the close faster and less burdensome. It covers where AI can help — close checklist automation, reconciliations, accrual suggestions, journal review, flux analysis and commentary — where it cannot and must not replace the finance professional, how to use it well, the safeguards and audit trail that keep its use safe, what auditors will ask under ISA (UK) 315, and an example close timetable. It builds on the close discipline in our guide to optimising the month-end close, and on our wider guide to how a Financial Controller uses AI. AI is best understood as an accelerant layered on a well-designed close: it speeds up a good process and exposes the weaknesses of a poor one.

Where AI Can Genuinely Help in the Close

AI can genuinely help with the parts of the close that suit its capabilities. It can help with the text and narrative elements — drafting the commentary and explanations around the numbers — which are time-consuming and suit AI’s strength with text. It can help with summarising and explaining, producing summaries of results and first explanations of movements as a starting point. It can assist with first-pass analysis, drawing out patterns for the finance professional to review. And it can help with the routine, repetitive elements of the close, freeing time for judgement-intensive work. In each area the finance professional refines and verifies the output. The main applications are below.

Close checklist automation

A close checklist that lives in a static spreadsheet is easy to let drift. AI can help review the checklist for missing or mis-ordered steps for a business of your type, draft task descriptions and owner notes, and summarise status from the tracker each day so the Financial Controller sees what is late without chasing. Close management software and the workflow features in many finance systems can automate reminders and dependencies; general-purpose assistants help with the drafting and review around them. A prompt for reviewing a checklist is in our finance prompt library — paste the checklist without names or figures.

Reconciliations

Reconciliations consume a large share of the close. Matching tools, many with machine-learning matching built in, can perform first-pass matching on bank, control and intercompany accounts and present only the unmatched items, each with a suggested explanation — a timing difference, a miscoding, a possible duplicate — for the finance professional to confirm or reject. The reconciliation still needs a preparer and a reviewer, and the reviewer still signs it off. Our guide to AI for reconciliations covers the workflow and the exceptions process in detail.

Accrual suggestions

Missing accruals are a common close error. AI can help compare open purchase orders, goods received not invoiced, recurring supplier invoices from prior months and the previous period’s accrual listing, and suggest candidate accruals that appear to be missing or released early. The suggestion is a prompt for the finance professional, not an entry: the decision to accrue, the estimate and the calculation stay with the team, and the arithmetic belongs in the finance system or a spreadsheet, not a chat window. Prepayments and deferred income can be reviewed the same way.

Journal review

AI tools can review the full population of manual journals rather than a sample, flagging entries posted outside normal hours, round-sum amounts, unusual account combinations, journals posted to rarely used accounts, or journals raised and approved by the same user. The output is a list for the Financial Controller to investigate before the period is locked. This supports, rather than replaces, a properly designed approval control — see our guide to designing financial controls.

Flux analysis

Flux analysis — comparing trial balance movements against the prior month, prior year and budget — is a natural task for assistance. A spreadsheet or the reporting tool calculates the movements; AI can then help sort them against a threshold, group related movements, and draft the questions the team needs to answer for each one. Asked properly, it should flag movements it cannot explain rather than guess. The explanations come from the people who know the business, and they then feed the commentary.

Commentary and close summaries

The close ends in written output: variance commentary, a close summary for the FD and the management pack narrative. AI can produce a first draft from the figures and the team’s driver notes, which the finance professional refines and verifies. The approach is covered in our guides to AI for variance commentary and AI for management reporting narrative.

Where AI Cannot and Must Not Replace the Finance Professional

Alongside where AI helps, there are parts of the close where it cannot and must not replace the finance professional. AI cannot replace the judgement the close requires — judgements about accounting treatment, estimates, provisions, cut-off and the accuracy of the numbers — which need the finance professional’s understanding and accountability. The close involves genuine judgement, and AI assisting with the close does not change the finance professional’s ownership of those judgements.

Nor can AI replace the control and accuracy the close requires. Because AI can produce confident but wrong output, anything it contributes must be verified, and the accuracy of the close must be assured by the finance professional, not trusted to the tool. Language models are not reliable calculators, so they should not perform the arithmetic behind an accrual or a reconciliation. The close produces the numbers the business, its lenders and, in a regulated firm, the regulator rely on — see our guide to the month-end close at a regulated firm — so its accuracy cannot be compromised by uncritical reliance on AI. The finance professional remains in control of, and accountable for, the close.

How to Use AI Well for the Parts It Suits

Using AI well means employing it for the parts it suits while keeping the finance professional in control. For commentary, AI produces a first draft that the finance professional refines and verifies. For summaries and explanations, it produces a starting point to review. For first-pass analysis — flux reviews, journal screening, reconciliation matching — it generates an initial view that the finance professional examines, verifies and develops.

In each case the pattern is the same: AI produces a first pass, and the finance professional refines, verifies and takes ownership of the result. Using AI well also means interacting with it effectively — clear prompts, the right context, explicit constraints such as “use only the figures provided” and “flag anything you cannot explain”, and working iteratively. Our guide to using AI inside Excel for finance covers keeping the calculations in the spreadsheet, where they can be checked.

The Safeguards That Keep Its Use Safe

Using AI safely in the close rests on safeguards. The foundational safeguard is verification: checking anything AI contributes — particularly numbers, analysis and factual content — because AI can be confidently wrong and the close must be accurate. AI’s errors must not flow through unverified into numbers people rely on.

Related safeguards include keeping judgement, control and accountability with the finance professional; using AI only for the parts of the close it suits; attending to data security so sensitive financial data is not exposed; and maintaining the existing close controls rather than letting AI’s involvement weaken them. Management accounts and ledger data belong only in enterprise tools the business has approved, as set out in our guide to AI data security in finance, and payroll or other personal data needs particular care under the ICO’s guidance on AI and data protection. In a regulated firm, the heightened accuracy the close requires makes these safeguards especially important; the FCA applies its existing rules, including the Senior Managers and Certification Regime, to AI rather than a separate regime.

Controls and audit trail

Every figure in the close must be traceable to its source, and AI-assisted work must preserve that trail. In practice:

• Approved tools, written rules. Which tools may be used and which data may go into them, set out in the team’s AI usage policy.
• Review built into the timetable. Each AI-assisted task has a named reviewer and a slot in the close plan — our guide to human-in-the-loop AI controls shows how.
• Records for material outputs. The tool used, the prompt or a summary of it, the source data and who reviewed and approved the output, kept with the close working papers.
• No AI postings without approval. Suggested accruals or journals go through the same approval workflow as any other entry, with segregation of duties intact.
• Change control. Changes to matching rules, thresholds or automated steps are approved and recorded, like any other change to a key control.

What auditors will ask: ISA (UK) 315

External auditors have a direct interest in how AI is used in the close. Under ISA (UK) 315 (Revised July 2020), published by the Financial Reporting Council, the auditor must understand the entity’s IT environment, the IT applications relevant to financial reporting and the risks arising from the use of IT, including the general IT controls around them. An AI tool that matches reconciliations, screens journals or suggests accruals is part of that picture. Auditors also test journal entries as part of their response to the risk of management override, so a documented AI-assisted journal review is something they may ask to see.

Expect questions such as: which AI tools are used in the close; which outputs feed reported figures; who reviews them and how that review is evidenced; how access and changes to automated rules are controlled; and how data security has been assessed. A team that can answer from its policy and its records makes the audit easier. Our guides to what auditors ask about AI and AI for audit preparation go further, and the ICAEW’s artificial intelligence resources are a useful reference.

How to Approach Using AI in the Close Sensibly

Approach AI in the close by identifying the parts where it genuinely helps — commentary, summaries, first-pass analysis and routine elements — and using it there, while keeping the judgement-intensive work and accuracy assurance with the finance professional. Apply the safeguards, particularly verification, and treat AI as an assistant that speeds up parts of the close, not a replacement for the finance professional’s control of it.

Start appropriately: begin with the clearly suitable parts, such as commentary drafting, learn what genuinely helps, and expand as the team gains confidence, rather than relying on AI before understanding its value and limits. A sensible first step is to run one AI-assisted task alongside the manual process for a single close and compare the results. Maintain the close’s quality throughout, so AI speeds it up without degrading it. The safe-use principles in where AI helps and where it is dangerous apply to every step.

An example AI-assisted close timetable

The table below is illustrative; working days and tasks will differ by business, systems and reporting deadlines.

The Cumulative Time Saving Across the Close

The time savings from AI accumulate across the close, and this is where the value becomes significant. Any single use — drafting a piece of commentary, summarising results, taking a first pass at a flux review — saves a modest amount of time on its own. But the close involves many such tasks, and using AI across them adds up to a meaningful reduction in the overall effort and pressure of the close.

A team looking to speed up the close with AI should therefore consider the full range of suitable tasks, rather than one isolated use, with the safeguards applied throughout. How much time is saved depends on the starting process, the systems and the volume of review the outputs need, so measure it in your own close rather than relying on general claims. Where the close is late because the process or the team is the problem, AI will not fix it on its own; that is often the point to bring in an interim Financial Controller to redesign the close first.

Frequently Asked Questions

Can AI run the month-end close automatically?

No. AI and automation can take on matching, screening, drafting and status tracking, but the judgements, approvals and accuracy assurance stay with the finance team. Every AI-assisted output that affects the numbers needs review by a competent person.

Which part of the close should we try AI on first?

Commentary drafting is usually the lowest-risk starting point, because errors are caught in the normal review. First-pass reconciliation on a low-risk control account or a flux review are sensible next steps.

Will auditors accept an AI-assisted close?

Generally yes, provided the audit trail is intact: you can show which tool did what, the inputs and outputs, and who reviewed the result. Under ISA (UK) 315 they will want to understand the IT environment, so expect questions.

Can AI post journals or accruals?

It can suggest them, but any entry should go through the same approval workflow as a manually prepared journal, with segregation of duties maintained. The finance professional decides, estimates and approves.

What should we look for when hiring someone to run an AI-assisted close?

Strong close ownership, reconciliation discipline and controls experience first, then the ability to use AI tools within a written policy and evidence review for auditors. Our Financial Controller recruitment team can help, with permanent shortlists typically within one to two weeks.