Freight Industry Reports

Which Freight Industry Report Should You Use?

Choose the report based on the question you are trying to answer. Annual and quarterly theft analyses describe reported cargo-theft activity over different time periods. Survey-based fraud reports describe the experiences of participating respondents. Those sources can be useful together, but they should not be combined as though they measure the same population or event type.

CargoNet: 2025 Annual Supply Chain Risk Analysis

Published: January 21, 2026. Reporting period: calendar year 2025. Coverage: United States and Canada.

CargoNet’s annual analysis covers reported supply-chain crime events, confirmed cargo theft, estimated losses, commodity patterns, and geographic trends. The report distinguishes the broader supply-chain crime-event count from confirmed theft incidents, which matters when citing or comparing totals.

Use this report when your team needs a full-year view rather than a snapshot of one quarter. Treat the figures as findings from CargoNet’s reported dataset; delayed reporting and the scope of the database can affect comparisons with other sources.

Read CargoNet’s 2025 annual analysis.

CargoNet: First Quarter 2026

Published: April 23, 2026. Reporting period: January–March 2026. Coverage: United States and Canada.

CargoNet reported 767 supply-chain crime events in Q1 2026 and estimated losses of $131.58 million. Its analysis also discusses commodity shifts, geography, impersonation, and organized-crime behavior. Because this is a quarterly report, compare it with equivalent quarterly periods rather than a full-year total.

Use the Q1 report when you need early-2026 context or want to understand how CargoNet described the shift toward identity and carrier-impersonation tactics.

Read CargoNet’s Q1 2026 analysis.

CargoNet: Second Quarter 2026

Published: August 6, 2026. Reporting period: April–June 2026. Coverage: United States and Canada.

CargoNet documented 677 supply-chain theft incidents in Q2 2026, down 26% from Q2 2025, while estimated losses rose to $304.6 million from $135.7 million. The report explains that a small number of multimillion-dollar losses had a large effect on financial severity and discusses persistent compromise-based schemes such as business-email fraud and shipment misdirection.

Use this report when you need the newest CargoNet quarterly analysis included on this page. Do not interpret the higher estimated loss total as proof that every theft became more severe; the publisher specifically notes the effect of extreme high-value events.

Read CargoNet’s Q2 2026 analysis.

TIA: April 2025 State of Fraud Report

Published: April 28, 2025. Source type: TIA member survey and Watchdog incident data.

The Transportation Intermediaries Association’s public summary covers broker-reported fraud experiences, losses, prevention spending, unlawful brokerage, cargo theft, identity spoofing, and the operational burden on respondents. Among the public findings, 22% of respondents reported more than $200,000 in fraud losses over the preceding six months, 34% identified unlawful brokerage as their leading fraud tactic, and 83% reported experiencing at least three types of fraud.

Use this report to understand the experiences of participating TIA members. Do not present those percentages as a nationwide fraud rate for all brokers or all truckload shipments.

Read TIA’s April 2025 fraud report summary.

How to Compare Freight Reports Without Misreading Them

• Compare equivalent reporting periods whenever possible.
• Keep the publisher’s definition of an incident, theft, fraud tactic, or loss attached to the number.
• Distinguish reported incidents from estimated industrywide totals unless the source explicitly makes that estimate.
• Do not combine survey percentages with incident counts as though they are one dataset.
• Check whether the source covers the United States, Canada, a member survey, or another specific population.
• Use the original report link when presenting a figure to customers, leadership, insurers, or operations teams.
• Recheck the source date before calling a statistic “current.”

Turn Research Into Operational Questions

Industry research is most useful when it helps your team ask better questions. If a report discusses impersonation or fictitious pickup, review how your operation confirms the driver and unexpected carrier changes. If the report discusses shipment misdirection or cargo theft, review your cargo theft prevention, tracking, and release procedures.

For individual numbers with source dates, use the Freight Security Statistics page. For illustrative operating examples, use the CargoGuard Workflows page.

Freight Industry Report Questions, Answered

Does CargoGuard publish these reports?

No. The reports linked here are published by CargoNet and the Transportation Intermediaries Association. CargoGuard summarizes their scope and limitations so readers can find the original research more easily.

What is the newest CargoNet quarterly report on this page?

As of the October 1, 2026 source review, the newest CargoNet quarterly analysis included here is Q2 2026, published August 6, 2026.

Is this a freight forwarding market report?

No. This page focuses on cargo theft and freight fraud research. It is not a general freight-forwarding market-size, rate, capacity, or economic forecast page.

Is this the same as freight analytics and reporting software?

No. These are external industry reports. Freight analytics software analyzes your own shipment or operational data. For CargoGuard product information related to shipment history and visibility, review the real-time visibility and load status timeline pages.

Can I compare figures from different reports?

Only after checking definitions, reporting period, geography, population, and methodology. CargoNet incident counts and TIA survey percentages describe different datasets and should not be added together.

Do these reports show CargoGuard customer outcomes?

No. They are third-party industry sources. Use them for external context, then measure CargoGuard against your own baseline and observed operational results.