AI Accounting Software for Finance Teams
Put an intelligence layer over the accounting you already run. Workisy codes transactions automatically, flags anomalies before they reach the close, assists reconciliation with ranked match suggestions, and forecasts cash — with a written rationale and audit trail behind every decision.
Features
Powerful Capabilities, Built for Scale
Every tool you need to run a world-class operation, from day one to enterprise scale.
Automated Transaction Coding
The model learns how your team has historically coded vendors, cost centers, and expense types, then proposes the correct account for each new transaction. Confidence scores decide what posts straight through and what waits for a reviewer.
Anomaly Detection
Continuous scanning flags duplicate payments, out-of-pattern amounts, unusual vendor activity, and postings that break historical trend lines. Issues surface within hours of entry instead of during the close review.
AI-Assisted Reconciliation
Bank lines are matched to ledger entries using amount, date proximity, counterparty text, and prior matching behavior, including many-to-one and split settlements. Unmatched items arrive in a ranked exception queue with suggested pairings attached.
Close Acceleration
A live close checklist tracks every task, owner, and dependency, and predicts which items are likely to run late based on current progress. Accruals and recurring journals are drafted automatically from prior-period patterns.
Cash-Flow Forecasting
Forecasts combine open payables, receivable aging, payment-behavior history, and recurring commitments to project cash position by week. Scenario sliders show the effect of delayed collections or pulled-forward vendor payments.
Audit-Ready Explanations
Every AI suggestion carries a written rationale, the source documents used, and the historical entries it was compared against. Reviewers see why a decision was proposed instead of accepting an unexplained output.
Document-to-Entry Capture
Invoices, receipts, and statements are read on arrival and converted into draft entries with vendor, tax treatment, and line-level detail already populated. Extracted values stay linked to the source image for one-click verification.
Variance Narratives
Actual-versus-budget and period-over-period movements are explained in plain language, naming the accounts and transactions driving each swing. Controllers start review meetings with the analysis already written.
How It Works
Up and Running in Three Simple Steps
Connect Your Ledger and Banks
Link your chart of accounts, bank and card feeds, and document sources. The AI layer reads your existing history to learn your coding conventions before it proposes anything.
Set Confidence Thresholds
Decide which decisions post automatically, which need a reviewer, and which always escalate — by account, amount band, vendor, or entity. Thresholds are adjustable at any time.
Review Exceptions and Close
Your team works a short exception queue instead of the full transaction volume, approves the drafted journals, and closes the period with the audit trail already assembled.
A closer look at Workisy AI accounting
An intelligence layer on top of the accounting you already run
Accounting software has been good at storing transactions for decades. What it has rarely done is take on the repetitive judgment around those transactions — deciding which account a charge belongs to, spotting the payment that went out twice, or noticing that a vendor's monthly total quietly doubled. Workisy treats that judgment as the automatable part and leaves the record-keeping to the modules built for it.
That division of labor is deliberate. Your postings, approval chains, and statements continue to live in general ledger and accounts payable, while the AI layer reads across them to predict, detect, and explain. Turning it on does not migrate your data or rewrite your chart of accounts.
Where machine learning changes the month-end economics
The close is expensive because volume forces linear effort: more transactions mean more coding, more matching, and more chasing. Automated coding and AI-assisted matching break that link by handling the predictable majority and presenting only a ranked exception queue. Teams that adopt this pattern typically shift from processing everything to reviewing what genuinely needs a decision, which is what compresses the calendar rather than simply adding overtime.
Bank matching is usually the clearest starting point because the patterns are dense and the feedback loop is immediate — our guide to banking reconciliation automation covers the mechanics, and accounts payable automation explains how invoice intake feeds the same models. Both connect to banking reconciliation in the platform.
Forward-looking finance, not just faster history
Once transactions are coded quickly and reliably, the same data becomes usable for prediction. Cash-flow forecasts draw on open payables, receivable aging, and observed payment behavior instead of a spreadsheet built once a quarter, and variance narratives arrive with the numbers rather than days after them. Controllers spend the saved time on the questions the board actually asks.
Reporting and dashboards remain the responsibility of financial reporting, and the wider automation stack is described across the AI solutions suite. To see the AI layer applied to your own ledger extract, book a working session with our finance team.
Results That Speak for Themselves
Measurable Impact on Your Business
Transactions Auto-Coded
Shorter Close Cycle
Cash Position Visibility
FAQ
Frequently Asked Questions
Ready to See It in Action?
Book a personalized demo and discover how Workisy can transform your operations in weeks, not months.