Can AI Make Trucking Insurance More Accurate, or Just Faster?

Artificial intelligence offers insurers new ways to automate document review, extract information from applications, and analyze historical records. For trucking companies, these capabilities could help reduce administrative delays that complicate the process of obtaining coverage.

That efficiency matters. Insurance delays can prevent carriers from putting trucks into service, accepting new contracts, or continuing operations as planned.

But faster decisions aren’t necessarily better decisions.

In trucking insurance, the challenge has never been a complete lack of data. Insurers can consider inspection histories, safety records, crash reports, operating authorities, and compliance documents. The challenge is determining what those records actually reveal about a business and what they leave out.

If AI is going to improve trucking insurance, it needs to do more than accelerate underwriting. It needs to help insurers develop a more accurate understanding of risk.

A Safety Record Doesn’t Tell the Whole Story

Before entering the insurance industry, I worked directly with trucking companies on safety and compliance, helping carriers establish operating authority, organize driver qualification files, maintain records, and navigate regulatory requirements. That experience reinforced an important lesson: compliance records are useful, but they don’t necessarily reflect how a fleet operates day to day.

Consider two trucking companies with similar fleet sizes, operating histories, and inspection records. One might follow a consistent preventive maintenance schedule, invest in driver training, and address potential safety concerns before they become violations. Another might take a more reactive approach, addressing problems only when they arise. On paper, these companies could appear similar despite having different approaches to managing risk.

The reverse can also happen. A carrier may have a violation or crash on its record without that event fully explaining its current safety practices or the circumstances surrounding the incident.

The Federal Motor Carrier Safety Administration’s (FMCSA) Safety Measurement System uses roadside inspection, crash, and investigation data to help identify carriers that may warrant safety interventions. While valuable for regulatory oversight, these records are not intended to provide a complete assessment of a carrier’s overall safety condition or insurability.

FMCSA has also documented limitations in Safety Measurement System data, including questions about data sufficiency and how reliably the information can support different uses.

AI can analyze these records quickly, but speed alone doesn’t resolve their limitations.

The Risks of Automating Incomplete Information

There are clear opportunities to use AI to reduce administrative work in commercial insurance. Automated systems could help identify missing application fields, compare information across documents, organize safety records, and flag discrepancies for review.

These improvements could be particularly valuable in trucking, where gathering documentation often involves multiple parties and systems.

The risk emerges when automated assessments are treated as definitive conclusions. For example, an AI system might flag a carrier because of an unfavorable safety indicator. The system may correctly identify the record, but it might not account for subsequent operational improvements or additional information that changes how the incident should be interpreted.

FMCSA’s Crash Preventability Determination Program illustrates why context matters. The program allows carriers to request reviews of certain eligible crashes. When FMCSA determines that a crash was not preventable, it excludes that event from the calculation of the Crash Indicator category in its Safety Measurement System, although the crash remains listed.

The program recognizes that the circumstances surrounding an incident matter, not simply the existence of a crash record.

This distinction becomes especially important when evaluating smaller fleets. Large carriers may have dedicated safety personnel and integrated reporting systems, while smaller businesses may maintain records across spreadsheets, emails, and outside service providers.

A missing maintenance document, for instance, might indicate that maintenance wasn’t performed. It could also mean the record exists somewhere the system cannot access. Those situations shouldn’t automatically produce the same assessment.

The National Institute of Standards and Technology’s AI Risk Management Framework offers voluntary guidance for evaluating the reliability, transparency, and trustworthiness of AI systems.

For trucking insurers, these principles provide a useful reference for determining whether the information supporting an automated assessment is current, sufficiently complete, and appropriate for its intended use.

AI Needs to Recognize When More Information Is Necessary

One of the most useful capabilities an AI system could have is identifying when a decision requires additional context.

Rather than treating every application as a straightforward approval or rejection, an effective system should distinguish between situations where information is sufficient and those that require clarification.

Consider a carrier whose operating profile has recently changed. An automated system might detect the change, but additional questions could reveal whether the company expanded into different freight categories, added equipment, or introduced new safety procedures.

Each of those factors may influence how its risk should be evaluated.

This is also where explainability becomes important. If an AI system recommends a particular outcome, the people responsible for the decision should understand which information influenced it and whether the conclusion is reasonable.

NIST’s guidance on trustworthy AI identifies explainability, transparency, reliability, and accountability as important characteristics of trustworthy systems. It also recognizes the role of human judgment in evaluating AI performance.

For trucking insurance, the goal shouldn’t be to eliminate questions from underwriting. It should be to identify the right questions earlier in the process.

Human Expertise Still Matters

AI adoption is sometimes framed as a choice between automation and human decision-making. In practice, the more useful approach is determining which tasks benefit from technology and which require professional judgment.

AI can help organize information, identify patterns, and reduce repetitive administrative work. Human expertise becomes particularly valuable when records are ambiguous or a carrier’s circumstances don’t fit neatly into standardized categories.

Someone familiar with trucking operations may recognize why a particular safety issue matters, how different operating environments affect exposure, or why recent operational changes warrant closer examination.

That expertise shouldn’t be treated as an obstacle to automation. It can make automated assessments more useful.

There is also an important accountability consideration.

In December 2023, the National Association of Insurance Commissioners adopted a model bulletin on insurers’ use of artificial intelligence, outlining regulatory expectations related to governance, risk management, and compliance with applicable insurance laws.

The bulletin’s regulatory effect depends on adoption and implementation by individual states, but it reflects growing attention to how insurers oversee AI-supported decisions.

Human judgment isn’t infallible, either. People can make inconsistent decisions or overlook relevant information. The opportunity is to combine the consistency and processing capabilities of AI with the operational knowledge needed to interpret complex situations.

Measuring Accuracy, Not Just Efficiency

If insurers want to know whether AI is improving underwriting, they need to evaluate more than turnaround time.

Processing an application in minutes rather than days would be an operational achievement. It wouldn’t necessarily demonstrate that the resulting assessment was more accurate.

A more meaningful evaluation would examine whether automated recommendations remain reliable across different types of carriers, how often assessments require correction, and whether the system appropriately identifies missing or inconsistent information.

Smaller fleets deserve particular attention. If a model performs well for carriers with extensive digital records but struggles with businesses whose documentation is less standardized, its effectiveness may be uneven.

Testing should reflect the diversity of the businesses being evaluated.

That evaluation also needs to continue after deployment. Changes in regulations, operating practices, and available information can affect whether a system remains reliable over time.

Insurers should also be prepared to revisit how their systems weigh different types of information. A model that consistently places too much emphasis on one safety indicator, for example, could overlook other factors relevant to a carrier’s operations.

The objective should be continuous improvement, not simply faster processing.

Better Risk Understanding Is the Real Opportunity

AI has the potential to make trucking insurance more efficient, reduce administrative delays, and help insurers identify information that might otherwise be overlooked.

But its greatest value may come from helping the industry make more informed decisions.

A fleet is more than its inspection history, and a safety record is more than a collection of violations. Understanding risk requires considering how a business operates, how it responds to problems, and whether the available information reflects its current circumstances.

The most useful AI systems won’t necessarily be those that produce the quickest answers. They’ll be the ones that help insurers recognize when the available information supports a decision and when further evaluation is needed.

Ultimately, the question isn’t whether AI can make trucking insurance faster. It’s whether it can make the industry’s decisions more accurate.

That’s the standard against which its success should be measured.