Closing the Small-Parcel Loophole: AI at the Border

If the past four chapters of our AI-Ready Brand Protection Guide taught us anything, it’s that counterfeiters don’t slow down. They get smaller, faster, and harder to see instead.

De-minimis rules made “counterfeit-by-mail” possible, with tiny parcels slipping through with little scrutiny. Now that those exemptions are disappearing, customs must find fakes in a flood of low-value shipments without stalling trade.

There’s simply no way humans can keep up with that volume alone.

But AI can.

In this fifth chapter of our guide, we’ll look at how AI helps customs see what low-value parcels are designed to hide, and how brands can supply the structured, machine-readable data that teaches these systems what “real” looks like.

The New Reality: Why Parcels Became the New High-Risk Channel

For years, de-minimis rules let millions of low-value parcels cross borders with barely a look. Counterfeiters treated it like a feature, breaking bulk shipments into tiny parcels and trusting most would go unnoticed.

That environment is shifting.

The EU is curbing low-value exemptions, pulling more parcels into real customs checks. In the U.S., the elimination of de-minimis for many categories forced carriers to rebuild data pipelines just to keep mail flowing.

This creates a huge operational reset. Parcels that once passed automatically now require real risk assessment, forcing customs to process far more signals, much earlier, and with a consistency no manual workflow can sustain.

And if you’ve followed this series, the pattern should feel familiar.

When platforms tighten one channel, counterfeiters move to another. Today, that channel is parcels, and without AI to absorb the volume, the enforcement gap simply shifts downstream.

The Parcel Problem: How Counterfeiters Hide in Plain Sight

Small parcels may look harmless, but they’re now the perfect hiding place for counterfeits. Instead of one container with 10,000 units, counterfeiters ship 10,000 parcels, each low-value and easy to disguise inside global mail flows.

This pushes the burden straight onto customs. Officers work from labels and routing, not deep product knowledge.

Brands hold the missing context:

SKU catalogs and legitimate packaging variations
Material and weight profiles
Security features and authentication markers
Price baselines
Authorized sellers and legitimate fulfillment routes

Without this intel, AI treats every parcel as identical. But once brands share structured data, the models can instantly spot shipments that mirror known counterfeit patterns, and clear trusted ones with confidence.

Enforcement Patterns: How Parcels Leak Into Markets

Look at recent seizures and the pattern appears quickly: the riskiest goods aren’t arriving in containers anymore.

They’re coming through postal and express channels in thousands of tiny parcels that look identical at first glance.

At U.S. international mail facilities, CBP leans on advanced electronic data and AI models to flag unusual senders, routing patterns, and mislabelled descriptions before a parcel is even opened.

Europe is seeing similar gains. Dutch Customs uses machine learning on X-ray images to highlight suspicious shapes and densities that would be nearly impossible to spot manually at scale.

China has taken the concept further.

AI-driven image analysis and targeting models run across hundreds of inspection sites, catching concealed items and repetitive sender fingerprints that tie seemingly unrelated parcels back to the same network.

Across all three regions, the trend is unmistakable. When AI and seizure intelligence reinforce each other, customs move from catching random parcels to consistently intercepting the networks behind them.

The AI Workflow: How Border Systems Detect Risk at Scale

If you’ve ever watched customs teams work, you know they’re juggling an impossible volume of parcels with limited signals. AI doesn’t replace their judgment; it just surfaces the right parcels first, based on patterns no human could stitch together at scale.

Here’s how the workflow actually comes together.

Parcel risk models

AI starts with the basics, analyzing behavioral clues that humans often miss when they’re buried under thousands of shipments. It flags sender history (repeat offenders, brand-new high-risk accounts), routing anomalies, label inconsistencies (like “gift” or mismatched descriptions), and price outliers that make no commercial sense.

From there, the system generates parcel-level risk scores, pushing suspicious shipments to the front of the queue so officers can focus on what matters.

AI on X-ray and NII images

Then comes the imaging layer. Computer vision models compare what’s declared with what’s actually inside the parcel. They spot density or shape mismatches, concealed compartments, and packaging layouts common in repeat counterfeit shipments

Instead of officers reviewing every image manually, AI pre-sorts them into low risk, needs review, or priority flag, a massive time saver.

Data fusion

The biggest lift comes from linking data that used to live in separate systems. Carrier scans, marketplace orders, routing metadata, and signals from past seizures all feed into the same analytical layer.

When those signals converge, customs can identify risky parcels much earlier in the journey, sometimes before they even enter domestic sorting.

These layers work best when the underlying data is structured and connected.

That’s where Hubstream adds value. Its investigative workflows unify marketplace intel, parcel histories, packaging fingerprints, and seizure data into a single, machine-readable picture, giving AI the context it needs to flag networks, not just individual parcels.

The Brand Playbook: How to Support Customs AI

This is a power workflow, but it still needs brands to define the baseline. If the system doesn’t know what authentic packaging, routes, or sellers look like, everything gets treated as equally risky.

Which leads to the practical question: how can brands feed the AI workflow so it works better in their favor?

Here are the highest-impact steps:

Create structured two-way channels with customs: APIs, registries, and MoUs beat shared inboxes. Flag high-risk SKUs during launches or known counterfeit waves.
Publish machine-readable product fingerprints: Packaging specs, materials, weights, variants, authenticity markers, the details AI can’t guess.
Share early-warning signals from platforms and social media: Brands see spikes weeks before customs does. Feed these into customs systems, so AI reweights risk in real time.
Provide trusted seller and channel lists: This reduces false positives. Help AI distinguish legitimate D2C shipments from unknown, high-risk senders.
Set shared alert thresholds: Define what “suspicious volume,” “route anomalies,” or “packaging outliers” look like for your product. These thresholds trigger automated responses as soon as activity surges.

When brands contribute these signals, the entire AI workflow becomes sharper. Risk scoring improves, inspection queues become more accurate, and coordinated networks become visible long before parcels enter domestic circulation.

In other words, brands aren’t just helping customs. They’re shaping a workflow that protects their products proactively rather than reactively.

Final Thoughts: The Border Is Now the Test of Brand Readiness

Counterfeiters have shifted from containers to parcels, and with de-minimis rules fading, the volume won’t slow down. Customs now face millions of small shipments that only AI can triage at scale.

But AI only works when it knows your products.

Brands that share clear, machine-readable fingerprints, like trusted routes, SKU details, and early-warning signals, give customs the context needed to spot risk and move legitimate goods faster.

In Part 6, we’ll wrap up the series with the piece everyone’s waiting for: how to build internal teams that can actually sustain this new, AI-driven way of working.