Article brought to you by Intelligent Audit

When FreightWaves launched the AI Excellence in Supply Chain Awards, the goal was to cut through the noise of an industry where “AI” has become a marketing buzzword attached to nearly every press release, and instead spotlight companies using AI in truly transformative ways.

The awards are designed to recognize real deployments and measurable outcomes, not flashy pitches. Entries are judged on the strength of the AI application, how deeply it is integrated into existing workflows, and the tangible results it produces.

This year, Intelligent Audit was named one of the honorees in the AI Solution Provider category. The recognition went to DeepDetectAI, a proprietary machine learning system built by Chief Product Officer Brian Pollack to identify shipping errors and fraud buried inside millions of transportation transactions, often before they cause meaningful financial damage.

Freight and parcel invoices are among the densest, highest-volume data sets a shipper handles. A single enterprise account can generate thousands of line items each week across carriers, services, accessorials, and billing cycles. According to Intelligent Audit, that volume is exactly where costly mistakes and deliberate fraud tend to hide: a subtle deviation that is easy to miss in a spreadsheet, a changed service level that no one flagged, or a returns pattern that appears normal until it is not.

DeepDetectAI begins with history. The system ingests a shipper’s full transportation data set and uses proprietary machine learning to establish a baseline for what normal shipping activity looks like for that specific business, down to the account, service, and geography level. From there, it continuously monitors new activity for cost variations, unusual service usage, duplicate or apparently fraudulent charges, and other deviations from that baseline.

What distinguishes DeepDetectAI from a standard alerting tool is the explainability layer built on top of the detection engine. Every anomaly is paired with data showing what happened, where it occurred, and why, along with support from an Intelligent Audit analyst. The goal is to help logistics, finance, and operations teams move from reactively digging through data after the fact to proactively resolving exceptions as they appear.

Small signals, compounding consequences

Across Intelligent Audit’s DeepDetectAI case studies, small and easy-to-dismiss deviations repeatedly grew into six- or seven-figure problems when they were not identified and corrected quickly.

The pattern varied by account.

For a global eyewear company, it began with a $10,000 spike in a return service the company had never used before. That turned out to be the first sign of an organized fraud ring buying glasses, manipulating return barcodes, and eventually compromising the company’s UPS accounts to reroute product from Mexico into the U.S. By the time the scheme was fully uncovered, more than $1 million in fraudulent activity had been identified, prompting an FBI investigation.

For a national specialty retailer, the issue was a service-selection error. Teams were unknowingly booking FedEx Home Delivery instead of Ground across multiple accounts, a mistake that would have led to millions in avoidable spend before the pattern was detected.

For a global multi-brand manufacturer, the trigger was a single new “Additional Classification Fee” billed to a non-authorized brokerage account. It was flagged before it could compound into more than $200,000 in unplanned weekly spend.

In some cases, several small anomalies stacked up within one review period rather than unfolding as a single escalating issue. A high-end fashion retailer recorded three separate signals — a spike in late-payment fees, first-time use of a premium expedited service, and an incorrect international freight selection. Together, they represented $143,100 in detected issues and an estimated $2.8 million in annualized exposure if the late-fee pattern had continued unchecked.

In another case involving a different retailer, the billing appeared correct on paper even as a fraud scheme was playing out underneath it, underscoring the need for anomaly detection to look beyond whether an invoice reconciles and examine whether the underlying activity actually makes sense.

DeepDetectAI’s broader case files also include address spoofing that surfaced as an unexplained surge in residential deliveries, a vendor-impersonation scheme in which a small business moved unauthorized goods under a client’s identity, a closed-loop billing breakdown that led UPS to acknowledge unauthorized usage and return more than $1 million, an internal case of employee misuse identified through an unexpected shipping pattern at a low-volume distribution center, and an international return scheme linked to organized fraud that was stopped before it could produce an estimated $500,000 in annual losses.

“What makes DeepDetectAI’s entry stand out is how often the dollar figures involved keep escalating the longer an issue goes undetected,” said Adam Wingfield, FreightWaves’ Editorial Director. “A lot of cases don’t look urgent at first. A $10,000 return spike, a single new fee code, or an incorrect service level might not read as crises on paper. The point of DeepDetectAI is to notice the things that don’t look like a crisis yet,” he said.

Built on nearly three decades of freight audit expertise

Intelligent Audit has operated as a freight audit and payment company since 1996. Today, it counts growing e-commerce brands and at least 20% of Fortune 50 companies among its customers, and audited more than 2.1 billion shipments last year alone.

That history is central to DeepDetectAI. The system’s machine learning baseline is only as strong as the expertise behind it, and Intelligent Audit’s scale gives it a broad, high-volume view of what normal and abnormal shipping activity look like across industries. In a field where invoice details can hide both operational errors and abuse, that combination of domain knowledge and transaction volume matters because it helps separate one-off noise from patterns that deserve a closer look.

Turning hidden data into an early warning system

Supply chains generate far more transportation data than any team can manually review, and that volume is a major reason costly errors and fraud often go undetected until they have already affected budgets or service levels. For shippers, the challenge is not only seeing the data but narrowing it to the few exceptions that actually require action.

DeepDetectAI is designed to close that gap by continuously monitoring shipping patterns, surfacing exceptions in real time, and combining machine learning with explainable insights and analyst support so teams understand not only that something is wrong, but where it is happening, why it is happening, and what to do next.

That approach also reflects a broader shift in supply chain technology: the value of AI is increasingly being measured by whether it fits into existing review and exception-management workflows, rather than by the volume of alerts it generates. DeepDetectAI’s entry did not depend on a single dramatic catch. It was supported by a running case file of fraud schemes, vendor impersonations, billing breakdowns, and internal errors, each tied to a specific dollar figure and a documented resolution. That is the kind of specificity the AI Excellence in Supply Chain Awards were created to recognize.

Congratulations to Intelligent Audit on a well-earned honoree spot, and on demonstrating what real-time detection looks like in a part of the supply chain built almost entirely on trust.

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