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 slapped on every press release, and instead spotlight the companies that are leveraging AI in truly revolutionary ways.

The awards recognize real deployments and measurable outcomes as opposed to flashy pitches. Entries are judged on the strength of the AI application itself, how deeply it's 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 honor goes to DeepDetectAI, a proprietary machine learning system built by renowned Chief Product Officer, Brian Pollack, to catch the kind of shipping errors and fraud deeply buried inside millions of transportation transactions, lurking undetectable until they've already done real financial damage.ย 

Freight and parcel invoices are some of the densest, highest-volume data a shipper touches. A single enterprise account can generate thousands of line items a week across carriers, services, accessorials, and billing cycles. According to Intelligent Audit, that volume is exactly where costly mistakes and deliberate fraud like to hide: a subtle deviation easy to miss in a spreadsheet, a switched service level nobody flagged, a returns pattern that looks normal until it isn't.

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

What separates DeepDetectAI from a standard alerting tool is the explainability layer sitting on top of the detection. Every anomaly comes with data showing what happened, where it occurred, and why, backed by support from an Intelligent Audit analyst. That combination is meant to move logistics, finance, and operations teams from reactively digging through data after the fact to proactively resolving exceptions as they surface.

Small Signals, Compounding Consequences

What stands out across Intelligent Audit's body of DeepDetectAI case studies is how consistently small, easy-to-dismiss deviations can compound into six- or seven-figure problems if not recognized and rectified quickly.

That pattern shows up differently depending on the account.ย 

For a global eyewear giant, it started as a $10,000 spike in a return service the company had never used before, which turned out to be the first signal 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 full scheme was uncovered, more than $1 million in fraudulent activity had been identified, triggering an FBI investigation.ย 

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

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

Some cases involve several small anomalies stacking up inside one narrow window rather than a single escalating thread. A high-end fashion retailer had three separate signals: a late-payment fee spike, first-time use of a premium expedited service, and an incorrect international freight selection, surface within one review period. Together, they represented $143,100 in detected issues and an estimated $2.8 million in annualized exposure had the late-fee pattern gone unaddressed.ย 

In a separate case involving a different retailer, the billing looked correct on paper even as a fraud scheme played out underneath it, a reminder that anomaly detection has to look past whether an invoice reconciles and toward whether the underlying activity actually makes sense.

DeepDetectAI's broader case files also include address spoofing that surfaced as an unexplained residential delivery surge, 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 caught through an unexpected shipping pattern at a low-volume distribution center, and an international return scheme tied to an organized fraud operation that was stopped short of an estimated $500,000 in annual losses.ย 

"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, and today counts growing e-commerce brands and at least 20% of Fortune 50 companies alike among its customers, auditing more than 2.1 billion shipments last year alone. That history matters for DeepDetectAI specifically: the system's machine learning baseline is only as strong as the expertise behind it, and Intelligent Audit's scale gives it a wide, high-volume vantage point on what both normal and abnormal shipping activity looks like across industries.

Turning Hidden Data Into an Early Warning System

Supply chains generate more transportation data than any team can manually review, and that volume is precisely why costly errors and fraud so often go undetected until they've already affected budgets or service levels. DeepDetectAI is built to close that gap: continuously monitoring shipping patterns, surfacing exceptions in real time, and pairing machine learning with explainable insights and analyst support so teams understand not just that something is wrong, but where it's happening, why, and what to do next.

DeepDetectAI's entry didn't lean on a single dramatic catch. It came backed by a running case file of fraud schemes, vendor impersonations, billing breakdowns, and internal errors, each with a specific dollar figure attached and a documented resolution. That level of specificity is exactly what the AI Excellence in Supply Chain Awards were designed to recognize.

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

Click here to learn more about Intelligent Audit.

The post How Intelligent Audit Turned Three Decades of Freight Expertise Into an AI Early Warning System appeared first on FreightWaves.