The Data Conundrum: Why Freight Visibility Alone Cannot Prevent Cargo Theft

Cyber-enabled freight fraud is a persistent, worsening threat across the United States, causing serious financial and reputational damage for shippers, brokers, and carriers. Despite a decline in the number of thefts in 2026, Q2 losses of $304.6 million were nearly double Q2 2025 losses, due largely to increasingly sophisticated strategic fraud schemes that exploit supply chain vulnerabilities and target high-value loads.

Bad actors are attacking online systems and digital freight platforms at an alarming rate, surreptitiously inserting themselves into everyday shipment and financial workflows. Misleading transportation teams by manipulating data, they are taking advantage of fragmented systems, inconsistent verification practices, and the pressure to transport goods quickly.

For instance, strategic fraud tactics include data spoofing (e.g., manipulating GPS data to mask truck location and conceal unauthorized route changes); double brokering (i.e., a brokered load is passed off to another carrier without the shipper’s knowledge); and identity theft (e.g., buying Department of Transportation (DOT) and Motor Carrier (MC) numbers outside of legitimate sales channels to impersonate a reputable carrier).

The data conundrum

Although transportation companies collect vast amounts of data, from electronic logging devices (ELDs), mobile applications, Federal Motor Carrier Safety Administration (FMCSA), transportation management systems (TMS), carrier profiles, geofences, insurance records, bills of lading (BOLs) and proof-of-delivery (POD), relying on the sheer volume of freight and visibility data is not the impenetrable fraud prevention strategy that supply chain leaders want it to be.

Undoubtedly, real-time visibility and shipment-related data is integral to reducing the risk of cargo theft. However, data is vulnerable to calculated manipulation. As tech-savvy criminal networks take advantage of data security weaknesses to misrepresent carrier identities, location signals, and shipment documents, distribution-focused businesses must shift their focus from simply amassing data to evaluating the provenance, consistency, and corroboration of their data.

Consider a load that appears to be moving normally along its designated route. The carrier credentials looked legitimate during pre-tender checks. The tracking application reports consistent location pings and the load is marked “on time.” In reality, the carrier identity was cloned, the truck location was spoofed, or the shipment was a victim of double brokering and was transferred to an unknown location without the shipper’s or broker’s knowledge. In these scenarios, the real-time visibility data and carrier and load information were readily available but not corroborated to ensure authenticity and validity. Integrated carrier vetting and freight visibility technology can help close this gap by cross-checking identity, asset, location, and behavioral signals rather than treating each data point as independent proof.

Why individual data points are weak evidence

In today’s treacherous cargo theft landscape, false data that looks credible is the fox in the henhouse. All too often, data that appears legitimate is compromised. For instance:

·        A valid DOT or MC number may have been cloned or acquired illegally by a bad actor.

·        Regular tracking pings may disguise mode switching or a stationary load.

·        An insurance certificate may be altered or out-of-date.

·        A familiar email address from a carrier may have been compromised.

·        A GPS signal may be spoofed to create the appearance that freight is moving as planned when it has already been diverted.

·        A delivery image may not have been captured at the reported location.

·        An “arrived” event may reflect a geofence trigger rather than a verified handoff.

To mitigate fraud risk, the central question for transportation leaders has shifted from “Do we have data?” to “Who or what produced this data and how else can we confirm it?” Individual data points cannot stand on their own; the power to mitigate risk lies in corroboration and secondary data verification practices.

Corroboration secures trust across the freight journey

Data verification at each stage of the shipment journey is integral to protecting freight. Forward-thinking shippers and brokers are using purpose-built technology that leverages machine learning, advanced algorithms, and real-time analytics to uncover anomalies in data. For example, by spotting unusual tracking patterns, FMCSA profile edits, identity mismatches, or VOIP-based driver phones that could be linked to fraudulent entities, businesses can take additional steps to verify data, especially for higher-risk loads.

Pre-tender vetting: To prevent bad actors from entering the network, transportation companies should verify FMCSA authority data, insurance, ownership, identity credentials, and lane and performance history. They should pay special attention to potential red flags, such as any recent changes.

At pickup: Transportation teams should confirm that the driver, vehicle, VIN, and physical location correspond with the carrier that accepted the load. Notably, a single valid credential should not outweigh inconsistencies elsewhere.

In transit: Shippers and brokers need to determine whether the shipment is behaving as expected. Are the location signals credible? Do movement patterns align with what the broker should be seeing, or are there anomalies in location (e.g., sudden jumps, implausible travel times, prolonged gaps, unexplained route deviations) that suggest spoofing, emulation, or other attempts to conceal what is really happening? With technology that continuously compares the source and integrity of location signals, teams can better distinguish legitimate exceptions from potential spoofing or unauthorized tracking activity.

At delivery: Proactive teams can alleviate risk by matching POD data, timestamps, image metadata, and location information with the expected destination and shipment record. While not every load needs additional checks, data anomalies require stronger corroboration. Technology can also confirm whether delivery evidence was captured at the reported place and time, adding another layer of validation before a shipment is closed.

Best practices to build trust in transportation data

AI-enabled carrier onboarding and risk monitoring technologies take care of the heavy lifting of analyzing large volumes of identity, asset, location, and behavioral data but the human element is critical for investigating anomalies and making risk decisions. To reduce the risk of falling prey to digital freight fraud schemes, companies should establish best practices for data verification, creating a shared process across the transportation team (e.g., dispatchers, customer service, carrier managers, fraud teams) for identifying and responding to anomalies.

Best practices should define:

·        Which inconsistencies trigger an alert

·        Which loads require additional verification

·        Who is responsible for investigating each type of anomaly

·        When a load should be paused or escalated

·        How confirmed incidents can improve future risk models

Transportation leaders may also benefit from establishing “data trust” metrics to measure the efficacy of fraud mitigation strategies and to refine verification processes and authentication rules moving forward. Data integrity KPIs can complement common transport performance metrics, such as on-time delivery rate or revenue per mile.

For insights into freight visibility trustworthiness, transportation leaders can monitor KPIs such as the percentage of loads verified through two or more independent data sources, percentage of PODs validated against location and timestamp data, and the rate of carrier identity, contact, or authority changes before tender.

Similarly, to assess the reliability of their fraud protection model, shippers and brokers may choose to track metrics such as the percentage of exceptions requiring manual verification, median time from anomaly detection to resolution, and “confirmed fraud” rate vs. “false-positive alert” rate.

Looking ahead

As the transportation industry continues to modernize, becoming more reliant on digital onboarding, visibility, and payment solutions, the risk of freight fraud will persist. And as fraud schemes grow in sophistication and frequency, the need for robust data validation processes will only increase.

Moving forward, competitive advantage will depend less on the volume of logistics and visibility data and more on establishing a verifiable chain of data alongside the physical chain of custody. Transportation companies that gain trust in their data through AI-driven analysis, proactive investigation of anomalies, and validation of key data will be better able to thwart costly freight fraud schemes to protect loads, margins, and their reputation.