AI based fraud detection learns what normal looks like in your payments, invoices, logins and supplier records, then flags what does not fit: a changed bank account, an invoice outside the usual pattern, a login from an unexpected place. For Australian organisations, the biggest exposure is invoice and payment redirection fraud, and the best defence combines AI flags with verification steps people follow.
Key takeaways
- Australians lost $2.03 billion to scams in 2024, according to the National Anti-Scam Centre's Targeting Scams report.
- Payment redirection and business email compromise target accounts payable directly, often with convincing, AI-generated emails and invoices.
- AI detects patterns rules miss, such as near-identical supplier details, unusual amounts and timing, and approval workflows being bypassed.
- Courts have held payers responsible for verifying bank details before paying, so process controls matter as much as technology.
- Start with bank protections, call-back verification for bank detail changes and segregation of duties, then add AI monitoring.
How big is the fraud problem for Australian organisations?
The National Anti-Scam Centre reported that Australians lost $2.03 billion to scams in 2024, down 25.9% on 2023 but still enormous. Business losses concentrate in accounts payable: false invoices, supplier bank detail changes and compromised email threads.
The Australian Signals Directorate's Cyber Threat Report 2022-23 recorded more than 2,000 business email compromise reports to ReportCyber, with losses of almost $80 million and an average loss of around $39,000 per confirmed case.
Manual verification that worked a decade ago struggles now. Fraudsters use AI to write convincing emails, mimic supplier invoices and, in some cases, clone voices for follow-up calls.
How does AI based fraud detection work?
Pattern recognition at scale
Traditional fraud controls use rules: "flag any payment over $10,000" or "alert on a new bank account". Rules are easy to understand and easy for fraudsters to work around.
AI systems learn what normal looks like for your organisation and flag anomalies:
Rule-based: flags every payment over $10,000, including the legitimate quarterly payment you have made to the same supplier for five years.
AI-based: learns that quarterly payments to ABC Supplies are normal, and flags a smaller payment to a new account whose details are similar but not identical to ABC Supplies.
Which models are used?
- Random forests and gradient boosting (such as XGBoost): strong on structured transaction data.
- Autoencoders: learn to reconstruct normal transactions and flag those that reconstruct poorly.
- Sequence models: detect gradual patterns such as staged account takeover.
- Language models: read emails and invoices for signs of impersonation and urgency tactics.
You do not need the technical detail. What matters is what the system can detect and how alerts reach people who act on them.
What can AI fraud detection catch?
Invoice and payment redirection fraud
The biggest risk for most organisations. AI can detect:
- supplier bank details that change, or differ by a digit from the record
- invoice amounts that deviate from history
- lookalike email domains (accounts@supplier.com.au vs accounts@suppIier.com.au)
- invoices arriving outside normal billing cycles
- duplicate invoices with small changes
Account takeover and business email compromise
- logins at unusual times, places or devices
- sudden changes in email patterns or forwarding rules
- requests that bypass normal approvals
- urgency and secrecy language typical of social engineering
Transaction anomalies
- payments split to stay under approval thresholds
- unusual round-number transactions
- sudden changes in payment frequency
- geographic inconsistencies
- Invoice arrivesCaptured from email or portal
- AI checksSupplier, bank details, amount, timing
- Risk scoredLow, medium or high
- Human verificationCall-back on known number for high risk
- Payment approvedLogged with full audit trail
Why do false positives matter so much?
Rule-based systems often generate far more alerts than teams can review, and most turn out to be legitimate. The result is alert fatigue: staff stop investigating, and real fraud slips through.
Well-tuned AI reduces noise by understanding context, so alerts are fewer and more meaningful. That is the real business case: catching the right fraud while legitimate payments keep flowing. Track your false positive rate from day one. If the team ignores alerts, the system is not working.
What does Australian regulation require?
The Scams Prevention Framework
The Scams Prevention Framework Act 2025 places obligations on designated sectors, starting with banks, telecommunications providers and digital platforms, to prevent, detect, report and respond to scams, with significant penalties for breaches. If you operate in a designated sector, scam detection becomes a legal obligation. Our guide to the Scams Prevention Framework covers it in detail.
Payer responsibility
In Mobius Group Pty Ltd v Inoteq Pty Ltd (2024), a business that paid a fraudulent invoice into a scammer's account, after its supplier's email was compromised, was still required to pay the supplier. The lesson: verify bank details through an independent channel before paying.
AML/CTF obligations
Reporting entities under the AML/CTF Act must monitor transactions and report suspicious matters to AUSTRAC. AI used for this must produce explainable alerts with full audit trails. You need to justify an alert to a regulator, not just raise it. From 1 July 2026, the regime extends to more professions, including lawyers, accountants and real estate agents providing designated services.
Reporting
The ACCC's National Anti-Scam Centre, launched in July 2023, coordinates scam disruption. Reporting to Scamwatch and ReportCyber helps protect other organisations.
What works in practice?
1. AI in accounts payable
The highest-value step for most organisations is adding fraud checks to invoice processing:
- cross-check payee details against the supplier master file
- flag unusual invoice patterns automatically
- keep the vendor master file clean, with changes verified and logged
- learn your organisation's patterns over time
Expect several weeks of configuration and enough historical data, typically months of transactions, to establish baselines. Our guide to automating invoice processing covers how fraud checks fit into the workflow.
2. Bank protections
Australian banks have expanded protections, including payee name checking and intelligence sharing through the Australian Financial Crimes Exchange. Ask your bank which features are available and switch them on. Many organisations have protections they have never enabled.
3. Layered verification
AI works best inside a layered process:
- AI flags suspicious transactions
- automated checks run
- low-risk transactions proceed
- medium-risk transactions get a quick human review
- high-risk transactions require independent verification
What did the AI flag?
- Supplier bank details changedCall-back on a known number before any payment
- Amount or timing outside normal patternQuick review by AP lead
- Lookalike email domain or urgent requestHold, verify with supplier, alert IT security
- Split payments under approval thresholdEscalate to finance manager
What does not work?
Tools without process change. A platform that produces alerts nobody acts on is expensive theatre.
Over-reliance on AI. Sophisticated social engineering through legitimate channels can still get through. Human judgement stays essential for high-value decisions.
One-size-fits-all. Detection tuned for retail e-commerce generates noise for a B2B manufacturer with few, high-value transactions. Configure for your industry.
How are fraudsters using AI?
The same capabilities that power detection are used by attackers:
- AI-written phishing and BEC emails without the spelling mistakes staff were trained to spot
- realistic fake invoices that pass visual inspection
- voice cloning for follow-up calls impersonating executives or suppliers
Visual inspection of invoices is no longer enough. Verification must go beyond the document:
- match transactions against bank and supplier records
- verify by calling known numbers, never numbers on the invoice or email
- require two people to approve any bank detail change
What should you look for in fraud detection experts or solutions?
Whether you engage fraud detection experts or buy a platform, look for:
- Process first: they map how money leaves your organisation before recommending tools.
- Integration: they connect to your ERP or accounting system, bank feeds and email security.
- Explainability: every alert shows why it was raised.
- False positive management: they measure and tune alert quality.
- Data handling: transaction data stays in Australia, inside an environment you control.
- Human approval: high-risk payments always require a person.
What return should you expect?
Return depends on current exposure and process maturity. It is strongest where invoice volumes are high, supplier lists are long, payments are frequent, there have been previous incidents or near misses, or false positives are already consuming staff time.
- Fraudulent payments stopped before releaseDirect loss avoided
- Fewer false alerts to investigateStaff time back
- Cleaner supplier master fileFewer errors
- Evidence for auditors and insurersAudit trail
Much of the benefit often comes from reduced false positives and a cleaner, better-controlled payment process, not only from prevented fraud.
How do you get started with AI fraud detection?
- Weeks 1-2AssessmentReview incidents and near misses, map every payment path, identify high-risk points
- Weeks 3-4Quick winsEnable bank protections, call-back verification, segregation of duties review
- Months 2-3TechnologyCheck existing ERP features, pilot AI checks on real data
- Month 4+ImproveTrack false positives, update training, report incidents
- Quantify exposure: review the last two years for incidents, near misses and false positives.
- Map payment processes: document every way money leaves the organisation.
- Fix the basics: bank protections, independent verification for new or changed bank details, and clear approval limits.
- Check existing tools: your ERP or accounting system may have fraud features you are not using.
- Pilot AI checks: test on real historical data before committing.
- Keep improving: monitor alert quality, refresh staff training and report incidents.
How Solve8 can help
Our consultants map how money leaves your organisation, tighten the controls, and build AI checks into your invoice and payment workflows, connected to your ERP and running on your infrastructure in Australia, with a person approving every high-risk payment. See our process automation services and financial services work, or talk to us.
Related reading: AI vendor onboarding, contract review AI, Privacy Act compliance for AI.
Sources: National Anti-Scam Centre, Targeting Scams Report 2024; Australian Signals Directorate, Cyber Threat Report 2022-23; Scams Prevention Framework Act 2025 (Cth); Mobius Group Pty Ltd v Inoteq Pty Ltd (2024).
