AI-assisted month-end close

Setup covers a close checklist built around your existing chart of accounts and reporting calendar, tracked by task status and by named account owner so nothing sits unassigned. Anomaly detection gets trained on your own historical closes, typically the trailing twelve to twenty-four months where available, and tuned to flag balances, ratios or transactions that break your specific pattern rather than a generic threshold.

For every account flagged as material, a first-draft flux commentary gets generated explaining the change from the prior period. A month-over-month exception log rolls flagged items into a short, ranked list a controller can review in minutes. The exact scope, including how many accounts get anomaly monitoring and how flux commentary gets routed for edit, is confirmed in writing before setup starts.

We start by pulling your trailing close history to establish what normal actually looks like for each material account, since a threshold set from a single unusual quarter will flag the wrong things all year. The checklist gets built next, mapping every close task to an owner and a due point in the close calendar.

Once the close runs live, anomaly detection scores each account against its own historical pattern as data comes in, flagging anything outside the tuned range onto the exception log instead of requiring someone to eyeball every account cold. Flagged items get a draft flux explanation generated automatically; a person on the close team reviews and edits that draft before it counts as final. A controller reviews the full exception log and signs off on the close, and thresholds get revisited after each of the first several closes based on what the model got right and what it missed.

This fits businesses closing more than roughly fifty active general ledger accounts a month where the close currently runs long because nobody has a fast way to tell which accounts actually need attention: a SaaS company with deferred revenue and multiple cost centers, a manufacturer with inventory and job-cost accounts, or any business reporting to a board or a lender on a fixed monthly or quarterly deadline.

It is a poor fit for a very small business with a handful of stable accounts and a close that already finishes in a day or two, where the setup overhead outweighs the time it would save. We flag that during scoping rather than selling automation the close does not need yet.

The most common issue we find is a close checklist that lives in someone's head or an outdated document, so tasks get missed not because anyone is careless but because there is no single tracked source of what is done and what is not. We also regularly find variance review that happens the same shallow way every month regardless of whether an account actually moved, wasting review time on stable accounts while a genuinely unusual balance gets the same five-minute glance.

A third common gap is flux commentary written from memory weeks after the numbers actually moved, so the explanation is thinner than it should be. Generating a first draft while the data is fresh, even one a person then corrects, produces a more accurate explanation than reconstructing the story from scratch later.

The close checklist and anomaly detection connect to QuickBooks Online, NetSuite or Sage Intacct depending on what your business already runs, pulling account balances and transaction detail directly rather than requiring a manual export. FloQast handles checklist tracking and close-task status for businesses that want a dedicated close-management platform on top of their ERP.

Power BI sits on top for businesses that want the exception log and flux commentary surfaced in a dashboard a controller or the business owner can check without opening the accounting system directly. We do not require replacing your existing ERP or reporting tool; the close layer is built to sit on top of what you already run.

AI-assisted month-end close setup is a project-based fee separate from ongoing monthly bookkeeping, priced by account count and how much historical data is available to train anomaly detection against. It is quoted after a scoping call once we have seen your chart of accounts and close calendar.

Ongoing monthly close and reporting work that this setup improves is priced on the published rate card at /us/pricing. FloQast or Power BI subscription costs, where used, are separate from our fee and billed directly by the vendor.

Quality is tracked two ways: how many flagged items turn out to be real issues worth a controller's attention versus normal noise, and whether the close finishes by the agreed date more consistently than before automation. Both get reported after each of the first several closes.

A flagged item that turns out to be a false alarm is not treated as a failure on its own; a pattern of false alarms on the same account is treated as a signal the threshold for that account needs retuning, and that adjustment gets made and logged rather than silently ignored.

Anomaly detection narrows what gets reviewed; it does not replace the review. Every flagged item still gets checked by a person, and a controller or senior reviewer signs off on the full close before any report leaves the building. Draft flux commentary is explicitly labeled as a draft internally until a person has edited it, so nobody downstream mistakes a model's first guess for a reviewed explanation.

The exception log and sign-off record are kept as part of the close file, giving you a documented trail of what was flagged, what was checked and who approved the period, useful both for internal governance and for anyone preparing audit-ready books later.