AI for Payment Times Reporting Compliance
A Report That Can Now Be Used Against You
The Payment Times Reporting Scheme has existed since 2020, but for most of its life it was a disclosure obligation with modest teeth. Large businesses reported how quickly they paid their small business suppliers, the data went onto a public register, and the market was left to make of it what it would. The reforms enacted through the Payment Times Reporting Amendment Act 2024, which apply to reporting periods commencing on or after 1 July 2024, changed the character of the scheme entirely. The scheme has stopped being a mirror and become a lever the government can pull.
The single most important change is the slow payer mechanism. Under the reformed scheme administered by the Payment Times Reporting Regulator, the slowest 20 per cent of small business payers in each industry division can be directed by the Minister to publicly disclose that they are a slow small business payer. That disclosure can be required on your own website, in your financial statements, and in other business documentation, which puts it in front of the people you sell to. For a business that sells to other businesses, or that tenders for work where reputation matters, being labelled a slow payer of small suppliers in your own published materials costs you commercially.
The trigger has a specific shape worth understanding. A ministerial direction can apply where an entity has been classified as a slow small business payer for two consecutive reporting cycles, or where it was a slow payer in one cycle and failed to submit a report in the cycle before or after. Slow payment and poor reporting compound, and a business that does both is exactly the profile the mechanism is built to catch.
There is an upside instrument as well. The Fast Small Business Payer List recognises businesses that consistently pay their small business suppliers in 20 days or less, and publishes them on the public Payment Times Reports Register. The scheme now rewards leaders and exposes laggards, and the data that decides which list you land on comes straight out of your own accounts payable system.
This guide is written for the finance leader, financial controller or accounts payable manager at a business that crosses the reporting threshold. It explains where AI-assisted data work genuinely reduces the risk of an inaccurate report or a slow-payer classification, and where the problem is a cash and policy decision that no software can make for you.
What the 2024 reforms mean in practice
- Reporting is assessed against a $100 million consolidated revenue threshold, on a consolidated group basis aligned to accounting standards.
- The slowest 20 per cent of payers in each industry division can be directed to publicly declare themselves a slow small business payer.
- Direction risk rises after two consecutive slow cycles, or a slow cycle plus a missed report.
- Paying small business suppliers in 20 days or less can earn a place on the Fast Small Business Payer List.
Why This Is a Data Problem Before It Is a Payment Problem
The instinctive reaction to slow-payer risk is "we need to pay faster," and sometimes that is exactly right. Often, though, both the exposure and the reporting error trace back to data that cannot be trusted. Before you can improve your payment times, you have to measure them correctly, and measuring them correctly across a consolidated group is harder than it sounds.
Start with the threshold change. The reformed scheme uses a $100 million consolidated revenue test and consolidated group reporting aligned with accounting standards. That means a reporting entity often has to pull payment data from multiple controlled entities, each potentially on a different accounting system, with different supplier master data and different ways of recording when an invoice was received and when it was paid. Reconciling that into a single, defensible dataset is a material data-engineering task, and it is one that many groups currently do with heroic manual effort each reporting period.
Then there is the small business identification problem, which is unique to this scheme. You only report on payments to small business suppliers, and determining which of your thousands of suppliers meet that definition is not something you can eyeball. The Regulator provides a Small Business Identification Tool for exactly this reason. Matching your supplier master data against that determination, keeping it current as suppliers change, and doing it consistently across group entities is precisely the kind of high-volume, rules-based matching where manual processes drift and automated matching holds up.
Finally, the metric itself is sensitive to data quality. Payment time is measured from a defined start point to the day payment is made. If your systems record invoice receipt dates inconsistently, or if invoices sit in an approval queue without a clean timestamp, your reported payment times can be wrong in either direction. An inaccurate report is not a minor issue under the reformed scheme. False or misleading reports sit squarely within the Regulator's stated compliance and enforcement priorities for 2025-26, alongside late reporting and failure to comply with directions.
Where Slow-Payer Risk Actually Comes From
| Metric | Assumed Cause | Common Real Cause | Improvement |
|---|---|---|---|
| Late payments | Holding cash | Approval bottlenecks | Fixable |
| Wrong payment times | Slow AP team | Bad receipt timestamps | Data issue |
| Missed suppliers | Oversight | Unmatched small business data | Matching gap |
| Group inconsistency | Policy difference | Disconnected ledgers | Consolidation gap |
| Reporting errors | Carelessness | Manual reconciliation | Process risk |
The reframing matters because it changes what you fix first. If half of your apparent slow-payer risk is actually measurement error and unmatched supplier data, then cleaning the data is both cheaper and faster than a blanket policy of paying everyone early, and it gives you an accurate picture to make real payment decisions from.
Where AI Genuinely Helps, and Where It Cannot
The honest boundary is the same one that applies to every compliance-adjacent automation. AI cannot decide to pay a supplier earlier, because that is a cash flow and working capital decision that belongs to your finance leadership and depends on factors no model can weigh for you. It cannot make the legal determination of your reporting obligations, and it cannot substitute for a human signing off on a report that carries real consequences if it is wrong. What it can do is make the data underneath all of those decisions accurate, current and consistent, which is the part that currently consumes the most time and produces the most risk.
Payment Times Data Pipeline
Take the stages one at a time.
Consolidation. Bringing accounts payable data together from multiple entities and systems into one consistent dataset is repetitive, rules-driven work, and it is where reporting time currently disappears. The same consolidation discipline we describe for multi-entity financial consolidation applies directly here, because the payment times problem is a consolidation problem wearing a compliance hat.
Identification. Matching your supplier master against the small business determination is high-volume classification, and keeping it current as suppliers are added and change is ongoing. This connects naturally to how you handle supplier and vendor onboarding: if supplier data is captured cleanly at the front door, the reporting classification downstream is far more reliable.
Measurement. Computing payment time correctly depends on clean start and end timestamps. AI-assisted reconciliation can flag invoices with missing or inconsistent receipt dates, surface the ones stuck in approval limbo, and give you a payment-time distribution you can actually trust. This is a close cousin of the reconciliation automation that bookkeeping teams already use to keep ledgers honest.
Monitoring. The slow-payer mechanism punishes drift over consecutive cycles, so the signal worth watching is the trend between reports. A monitoring setup that recalculates your position against the 20-day fast-payer line and your industry's slow-payer band on a regular cadence gives you time to act before a bad cycle becomes two.
Reporting. Producing the actual submission from clean, consolidated data, with the numbers traceable back to source, turns reporting from a fortnight of spreadsheet work into a review-and-sign exercise. The drafting and assembly are automatable; the sign-off stays human, because the accountability does.
What remains outside the pipeline is every genuine decision. Whether to change payment terms, how to sequence payments when cash is tight, and how to respond if you are heading toward the slow-payer band are all leadership calls. The automation feeds those calls accurate information and leaves the calls where they belong.
Reading Your Own Numbers Before the Regulator Does
The slow-payer mechanism has a feature that rewards businesses who watch their own data closely: it operates by industry division, and it punishes drift over consecutive cycles rather than a single bad period. Both of those characteristics are exploitable in the good sense, if you understand them.
The industry-division framing makes your position relative. You are measured against the other reporting businesses in your division, and the slowest 20 per cent in that comparison group carries the direction risk, so there is no fixed benchmark set in Canberra to clear. That has a practical implication: a payment time that would be perfectly safe in one industry might land you in the bottom fifth of another where fast payment is the norm. Knowing where the band actually sits for your division, and how close you are to it, is far more useful than a generic internal target of paying within some round number of days. Your own historical data, tracked over time, is what tells you whether you are drifting toward that band or away from it.
The consecutive-cycles design is the other lever. Because a ministerial direction attaches after two consecutive slow cycles, or a slow cycle plus a missing report, a single poor period is a warning rather than a sentence. That warning is only useful if you can see it. A business that reconstructs its payment times once, at reporting time, learns it had a bad cycle only after the cycle has closed and cannot be changed. A business that monitors continuously sees the deterioration mid-cycle, while there is still time to clear an approval backlog or reprioritise payments to small suppliers, and can avoid the second consecutive classification that actually triggers the consequence. The difference between those two postures comes down to data and monitoring.
This is also why reporting accuracy and payment behaviour are not separable problems. Because a missed or late report can combine with a single slow cycle to trigger a direction, sloppy reporting amplifies payment risk. A business with clean, automated reporting removes an entire category of self-inflicted exposure: it always reports on time, it always reports accurately, and it therefore only ever faces the slow-payer test on its genuine payment performance, which it can see coming and manage. Getting the data right does double duty, protecting you on both the reporting limb and the payment limb of the mechanism at once.
Governance and the Trap of the Wrong Tool
A word of caution that applies to any finance-data automation. Supplier payment data, banking details and internal cash positions are commercially sensitive, and feeding them into a general-purpose AI service that may retain or train on your inputs is a real risk. The analysis should run in an environment you control, with clear rules about what the system can access and where the data lives, and outputs should flow to the people who need them without exposing the underlying records more widely than necessary. We set out this control model in our guide to AI agent governance and data access, and it applies with full force to financial data.
Where Should You Start?
If you cannot confidently answer how your payment-time numbers are calculated and where the source data comes from, that uncertainty is itself the risk. A structured data readiness assessment will tell you whether your systems can support accurate reporting today or whether the foundations need work first.
The Fast Payer List Is an Asset, Not Just an Escape
Most of the conversation about the reformed scheme is defensive, focused on staying out of the slowest 20 per cent. That is the right first priority, but it undersells the other side of the mechanism. The Fast Small Business Payer List, published on the public register, recognises businesses that consistently pay their small business suppliers in 20 days or less. For a business that wants to be a supplier of choice, or that competes for work where small business supplier relationships matter, that recognition is a genuine reputational asset, and it is one your competitors either have or do not.
Landing on that list takes actually paying fast, consistently, and having the data to prove it across a full reporting period. A policy that says you pay fast counts for nothing on the register. That is squarely a process-and-data question. If your approval workflows are clean, your invoice receipt dates are captured properly, and you can see your rolling payment-time distribution against the 20-day line, then reaching and holding the fast-payer standard becomes a manageable operational goal rather than a hope. If your data is a mess, you cannot even tell whether you already qualify.
There is a strategic point here worth naming. The same data capability that keeps you out of the slow-payer band is the capability that lets you pursue the fast-payer recognition. You build it once and it works in both directions, protecting you from the downside and opening the upside. Framing the investment purely as compliance cost misses half of what it buys.
A Realistic Rollout
Treat this as capability you stand up once and then run every cycle. The order matters: get the data trustworthy before you make payment-policy decisions from it, because decisions made from bad data are worse than no decisions at all.
Building Payment Times Reporting Capability
The business case is a blend of avoided risk and reclaimed time. The reporting hours are real and worth quantifying for your own environment, but the larger value is not landing in the slowest 20 per cent because of measurement error, protecting your reputation as a reliable payer of small suppliers, and being able to stand behind a report you know is accurate.
What Accurate Payment Times Capability Protects
Two closing points. First, faster and more accurate payment is good for your small business suppliers, and the scheme is designed to make that visible. Improving your data is the first honest step toward improving your actual payment behaviour, and healthier supplier relationships tend to follow, which connects to broader work on cash flow forecasting and working capital. Second, this guide is an operating model, not legal advice. The precise thresholds, definitions and obligations under the Payment Times Reporting Scheme are set by the Regulator and can change, so confirm your position directly with the Payment Times Reporting Regulator and, where the stakes warrant, with your advisers.
The reformed scheme has turned payment times from a quiet disclosure into a public, consequential number. The businesses that come out ahead are the ones who can measure their own payment behaviour accurately, see trouble coming, and prove the number they report is the number that is true. Cash reserves matter less here than visibility does.
Related Reading
- AI-assisted multi-entity financial consolidation for CFOs
- AI for supplier and vendor onboarding automation
- Automated compliance reporting for Australian businesses
- AI Xero reconciliation for bookkeeping teams
- AI cash flow forecasting for growing businesses
- AI agent governance, data access and privacy
Solve8 helps Australian finance teams turn scattered accounts payable data into accurate, monitored payment times reporting that stands up to the reformed scheme. Book a consultation to talk through your reporting setup.