AI vs. Fraud: The Machine Learning Advantage in Catching What Humans Can’t

“When you look at the numbers…$103 billion in fraudulent returns in 2024 and $35 billion in annual cargo theft…there’s a lot to protect yourself against. The smallest irregularity can snowball into a very big problem.”

— Megan Bishop, VP of Client Success (taken from "AI in Action" - watch now)
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Fraud in logistics isn’t always dramatic. While a hijacked ship or stolen truckload make headlines, it’s not the everyday case. More often, it’s something like unauthorized usage of a lane or address spoofing. Things that might start small at first and then quietly grow into multimillion-dollar losses.

That’s why the future of fraud prevention isn’t manual auditing; it’s machine learning. Intelligent Audit’s AI-Powered Anomaly Detection uncovers the microscopic irregularities that even the sharpest analysts would never notice.

The Hidden Cost of “Small” Anomalies

“At a macro level, you’d never see it,” explained Hannah Testani. “This customer ships a billion dollars a year. A $2,000 irregularity wouldn’t move any KPIs — but that’s exactly where AI shines.”‍

You might be thinking, why do I care about one $2,000 irregularity? Because more often than not, left unchecked, that $2,000 anomaly turns into an unexplainable million-dollar loss before  anyone takes note. When you can act early, you can recover faster and prevent unnecessary leaks. 

Machine-learning algorithms review every single shipment individually. Traditional audits focus on invoice-level exceptions based on preconfigured rules, but AI-Powered AI-Powered AI-Powered Anomaly Detection operates in real time — examining every charge, location, and route as it happens. It acts as an extra layer of protection against everything from a manually miskeyed weight to a buggy TMS API connection to a sophisticated return fraud scheme. 

From Pattern to Prediction

The way machine learning detects fraud mirrors how your credit-card company flags suspicious charges.

“You’ve never told your bank to alert you when someone spends $0.99 at a Walmart in Dallas,” Hannah said. “It learns what you normally do, and when something doesn’t fit that pattern, it tells you.”

When Intelligent Audit onboards a shipper, its algorithms learn historical behaviors — such as average costs, origins, destinations, and carrier relationships. They then test those learned patterns against new data in real time.

If a shipment doesn’t behave the way the model expects, it’s instantly flagged as anomalous. Without you ever having to configure a single ‘rule.’

Fraud in Focus: What Machine Learning Finds

1. External Fraud: Hijacked Accounts

An uptick in shipments from Mexico triggered alerts for one shipper. A bad actor had obtained the company’s UPS account and began testing small, high-value returns to U.S. residential addresses.

“They start small to see if anyone notices,” Megan explained. “When no one does, it takes off like a rocket.”

Result: $500K in prevented losses.

2. Operational Errors — Invisible to Audits

Another client’s freight invoices showed legitimate charges that had been routed from the wrong location. The difference? A mile and a half — enough to trigger an “excess mileage” fee that passed every traditional audit rule.

Result: Recovered $25K.

3. Internal Misuse — The Human Factor

Even good employees can make bad decisions. One anomaly flagged a small but suspicious Priority Mail shipment leaving a distribution center. Investigation revealed a personal shipment billed to the company account — a $3,000 problem that could have multiplied quickly if not squashed. 

Result: $360K in prevented losses.

Beyond Fraud: Fixing What Humans Miss

Machine learning doesn’t just uncover bad actors; it exposes weak links.

“It’s catching technical glitches — systems not talking to each other — and bad behavior that isn’t malicious,” Hannah said. “It’s just something humans could never spot in time.”

From duplicate data flows to rate-mapping errors, AI-Powered Anomaly Detection continuously improves audit accuracy. Every time the model encounters a false positive or a confirmed issue, it retrains itself to become more precise.

Speed and Scale: Real-Time Intelligence

“We manage over 2 billion shipments a year, and this all happens in real time,” Hannah emphasized. “There’s no value in finding fraud a month after it happened.”‍

Traditional audit cycles often review data weekly or monthly, by which point losses have already accumulated. Machine-learning systems act instantly, delivering alerts while shipments are still moving — giving companies the chance to intervene, not just analyze.

Why Humans and Machines Work Better Together

Machine learning doesn’t replace human judgment; it amplifies it. It handles the volume and pattern recognition that no team of analysts could ever match, while humans bring context and strategic decision-making.

“Fraud will happen — the question is when and how big,” Megan said. “AI gives you the chance to stop it before it escalates.”

Final Word

Fraud detection used to mean waiting for something to go wrong and then trying to find out why. Now, it means knowing the moment something feels off, even before it shows up on a report.

Machine learning gives your logistics operations a sixth sense, seeing patterns, identifying risks, and protecting profits in real time.

“It’s not rules-based,” Testani said. “It’s intelligence. It’s always learning, always watching — and that’s how it keeps you ahead of fraud.”

Watch the full “AI in Action” webinar recording or keep exploring AI at IA.

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