Risk Use case: AI fraud prevention that flags anomalous transactions before they reach approval

This article is part of our series on practical AI use cases in finance, where we explore high-impact applications across core financial workflows. 

The uncomfortable truth about most approval workflows is that they were designed for a different volume of work. A manager clearing two hundred invoices a week gives each one a few seconds. The control still fires, the audit trail looks clean, and the board assumes the company is protected. What’s actually being verified is simply that the invoice has a PO number and sits under a threshold.

Traditional ERP checks and three-way matching make this worse, not better, because they create false comfort. Rules catch what someone predicted in advance: a missing field, a breached limit, or a duplicate reference. They don’t catch an invoice that appears perfect but strange, like a familiar supplier with a new bank account, a round amount just under the approval limit, or a first-ever purchase on a dormant cost center. Anyone who knows the thresholds can stay under them.

The fallback is manual spot-checking after payment, which means sampling a fraction of transactions, months late, when recovery is unlikely. The exposure is real: in the 2026 AFP Payments Fraud and Control Survey, 76% of US organizations reported attempted or actual payments fraud in 2025, while only 17% use AI to fight it.

AI anomaly detection replaces predicted rules with a learned sense of what normal looks like in your ledger. Embedding proactive AI risk management within your accounts payable process fundamentally changes how transactions are cleared.
Here is how this proactive triage works in practice:

1. Learning the baseline: The model trains on 12 to 24 months of AP, general ledger, and vendor master history. It maps what’s typical per supplier, account, amount, timing, and user.
2. Scoring at the gate: Each new transaction gets a risk score as it enters the approval queue, before payment rather than after.
3. Triage with a reason: Low-risk items flow through untouched. The exceptional ones are routed to a reviewer with the explanation attached: changed bank details, unusual amount, first invoice from this vendor. Clear, audit-ready context replaces opaque AI scores.
4. Learning from the reviewer: Every disposition (fraud, error, legitimate) feeds back into the model, so precision improves with use.

The approver’s role changes fundamentally. Instead of rubber-stamping two hundred items, they investigate five that actually deserve it. The four eyes start seeing again.

Beyond catching individual errors, deploying AI in your approval workflow delivers immediate strategic value to the CFO and the board: 

• Restoring substance to an existing control: You’re not adding bureaucracy; you’re making a failing control effective again. That’s a message an audit committee understands immediately.
• Replacing sampling with full coverage: Screening 100% of transactions continuously is a structural upgrade to your assurance position. This level of continuous auditing and audit automation proves the true value of AI in audit by turning reactive sampling into proactive oversight.
• Protecting people as much as money: By embracing AI for internal audit, you equip your approving managers with context, not just an accept button. They do judgment work instead of mindless clicking. And because every flag comes with concrete evidence, employees face fewer vague suspicions when anomalies do occur, not more. 

However, be realistic about what you’ll find: most flags turn out to be duplicates, coding errors, and contract leakage. That recovered value typically carries the business case on its own. Fraud prevention is the tail risk it also covers.

Not every organization needs to start with AI. Duplicate-payment checks, threshold rules, and approval routing via Power Automate, paired with outlier reports in Power BI, will cover known and stable risk patterns using tools you already license. That is often the right first move.

AI earns its place when the pattern is unknown or adaptive, volumes are high, or you operate multiple entities. Incorporating AI for compliance ensures that these complex, high-volume environments remain tightly controlled.

The market splits into three categories:

• detection suites embedded in your ERP (SAP Business Integrity Screening, Oracle Fusion Cloud Risk Management)
• platform-independent analytics used by audit teams (MindBridge)
• pre-payment gatekeepers for specific spend flows (AppZen)

Which category best fits your internal control architecture depends on your ERP landscape and where you want the control point to sit. This is a strategic decision that comes well before any vendor demo.

Launching a successful AI fraud prevention pilot in accounts payable takes 4 to 8 weeks, but long-term value depends on operational discipline rather than algorithm complexity.

Key prerequisites for implementation

• 12 to 24 months of transaction history: The anomaly detection model requires a sufficient historical corpus across AP, general ledger, and vendor master tables to establish a reliable baseline of normal behavior.
• Usable vendor master data: Achieving data readiness for AI does not require a flawless historical ERP cleanup, but master records must be structured enough to reliably map typical transactional patterns.
• Named alert ownership: Every flagged anomaly must route to a designated reviewer with explicit accountability and defined SLA response times.

• Built-in explainability: System outputs must meet AI explainability and AI transparency standards to comply with GDPR, the EU AI Act, and internal audit requirements. "The model said so" is not an acceptable justification for flagging an employee or supplier. If a tool can’t show its reasoning, it doesn’t belong in this process.

   

When NOT to start

Pause your AI implementation if your vendor master data is completely unmanaged or if the organization lacks the personnel to review flagged transactions. An alert without an owner is just noise with a timestamp. An unmonitored queue does nothing to reduce financial risk, while unnecessarily exposing the business under evolving AI regulatory compliance standards.

Implementation pitfalls to avoid

• Setting alert thresholds too high: Overly sensitive models trigger a flood of false positives. Effective model risk management tunes for signal quality and time-to-disposition rather than total flag volume.
• Deploying a black-box model: Algorithms that lack AI observability cannot explain why a transaction was flagged. This destroys credibility with approvers and fails internal compliance reviews.
• Treating deployment as a pure IT project: Software alone cannot enforce risk controls. Without updated internal control frameworks and a deliberate effort to build AI maturity in finance, approvers lack the confidence and governance needed to act on alerts.

Deploying AI for fraud prevention is fundamentally an exercise in building organizational capability and AI readiness in finance, not just software installation. While technology vendors focus on selling tools, TriFinance acts as your advisory partner to bridge the gap between technical detection, internal control frameworks, and human review workflows. 

We help you design a governed operating model around anomaly detection. We anchor risk flags into your internal control framework, establish clear disposition workflows with named owners, and build the AI governance framework required to meet evolving AI regulatory compliance standards.

Looking to apply AI across other financial processes? Explore our full collection of AI use cases in finance, covering Purchase-to-Pay (P2P), Order-to-Cash (O2C), Record-to-Report (R2R), FP&A, Cash Flow, and Risk & Compliance.