By Luis Lopez, AI transportation consultant, CEO of Go Hub.io Holdings Corp and subsidiaries, and host of the Freight Guru Podcast
Freight fraud used to mean a stolen trailer in a dark truck stop. Today most of it starts with a login, an email address, or a phone call. Someone impersonates a legitimate carrier, books a load, and either hands it to another carrier without permission or simply disappears with it. The paperwork looks fine right up until the freight does not deliver.
As an AI transportation consultant, the first thing I look at when a broker or shipper asks about fraud tools is not the software. It is the booking process the software is supposed to protect. AI can score risk faster than any person, but it only protects you if somebody is required to look at the score before the load is tendered.
Why freight fraud is a pattern problem
A single fraudulent booking rarely looks wrong in isolation. The carrier has active authority. The insurance certificate is on file. The dispatcher sounds professional. What gives the scheme away is the pattern around the booking: a phone number that changed last week, an email domain that is one character off, a truck that has never run this lane, an authority that sat dormant for a long time and suddenly became very busy.
That is exactly the kind of problem machine learning handles well. People are good at judging one conversation. Software is good at comparing one event against thousands of others and noticing that it does not fit.
What AI vetting tools actually look at
Products differ, but most AI-based carrier vetting and fraud detection tools draw on the same categories of signals:
- Authority and registration history. Age of the authority, recent changes to contact details or company officers, and gaps in activity, drawn from public FMCSA registration data.
- Contact and identity consistency. Whether the phone number, email address and domain used on this booking match what is on file with the regulator and what the carrier has used before.
- Insurance verification. Whether the certificate matches what the insurer or agent confirms, rather than what a PDF says.
- Inspection and equipment footprint. Whether the carrier has roadside inspections, and whether its size and equipment are plausible for the load it is asking for.
- Behavior on the load board or portal. Unusual login locations, new devices, rapid bidding on high-value freight, or interest in lanes the carrier has never run.
- Network intelligence. Reports from other brokers and shippers on the same platform about the same entity, phone number or email address.
- Tracking behavior after pickup. A truck that pings from the wrong place, stops tracking, or heads away from the delivery point.
The model combines these into a risk score or a set of flags. A good tool also tells you which signals drove the score, so the person reviewing it knows what to check.
Where these tools perform well
Identity theft of a real carrier
The most common scheme is impersonation of a legitimate carrier. The fraudster uses the real company name and MC number with their own phone and email. Contact-consistency checks are strong here, because the one thing the fraudster cannot easily fake is the contact information already on record.
Recently changed or purchased authorities
An older authority that changes hands, updates all of its contact details and immediately starts chasing high-value loads is a well-known warning pattern. Software sees that sequence instantly. A busy carrier rep on a Friday afternoon usually does not.
Volume and speed
A person can carefully vet a handful of new carriers a day. A model can score every carrier on every load, every time, including carriers that were clean last month. Fraud often arrives through an account that was legitimate when it was onboarded.
Where they fall short
- False positives. New authorities, small fleets and carriers that recently moved or changed phone systems can look risky when they are perfectly honest. If the tool blocks them automatically, you lose good capacity and treat small carriers unfairly.
- Clean-looking bad actors. A patient fraudster can build a tidy record before making a move. No score catches everything.
- Compromised real accounts. If a legitimate carrier’s email is taken over, the contact details match perfectly. Only out-of-band verification, such as calling the number on file, catches this.
- Insider and process failures. If a rep can override a flag with one click to cover a load, the tool is decoration.
- Data lag. Public records and network reports are not instant. A scheme can run for days before the data catches up.
I covered the broader limits of these systems in what AI cannot do in freight. Fraud detection is a clear example: the model narrows the field, and a person makes the decision.
Building a process around the score
The tools work when the workflow gives them teeth. A practical setup looks like this:
- Score at onboarding and again at every tender. A carrier’s risk is not fixed on the day the packet is approved. See the carrier onboarding packet checklist for the baseline documents.
- Define what each risk level requires. Low risk books normally. Medium risk requires a call back to the phone number on file with the regulator. High risk requires a manager’s approval or a pass.
- Verify out of band. Never confirm identity using contact details supplied in the same email that is asking for the load.
- Tighten rules for high-value and easily resold freight. Electronics, food and beverage, and building materials deserve stricter thresholds.
- Match the truck at pickup. Give the shipper the carrier name, driver name and truck number, and have the dock confirm them before loading.
- Log overrides. Every time someone books against a flag, record who and why. Review the log weekly.
- Feed outcomes back. Report confirmed fraud and confirmed false alarms to the vendor so the model improves.
Questions to ask a vendor
- Which data sources feed the score, and how often is each refreshed?
- Does the tool explain why a carrier was flagged?
- How does it treat new authorities and very small fleets?
- Does it re-score existing carriers continuously or only at onboarding?
- Can it integrate with the TMS so a flag appears at the moment of tender?
- What happens to the data I contribute, and who else sees it?
The longer list in how to evaluate an AI vendor for a trucking or logistics company applies here as well.
The liability angle
A risk score does not transfer responsibility. If a load is tendered to an impostor, the questions afterward are about what the broker or shipper knew and what steps they took. Having a tool and ignoring its flags can look worse than not having one. The legal side is covered in double brokering and freight fraud liability.
Bottom line
AI is well suited to freight fraud because fraud is a pattern-matching problem at a scale people cannot handle manually. It is not a substitute for a phone call to a verified number, a dock that checks the truck, and a rule that nobody books against a red flag alone. Buy the tool for speed and coverage. Rely on the process for protection.
For more practical conversations on freight and technology, subscribe to the Freight Guru Podcast.
About the author: Luis Lopez is a Miami-based AI transportation consultant and logistics entrepreneur, the CEO of Go Hub.io Holdings Corp and subsidiaries, and host of the Freight Guru Podcast.
