How Intelligent Audit Turned Three Decades of Freight Expertise Into an AI Early Warning System

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.

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.

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. 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.

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. 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 annua