In 2024, the auto liability loss and loss adjustment expense ratio hit 87.6, the highest in 11 years. And the situation was not getting better throughout 2025.
As a result, 2025 will be another year without underwriting profit, as forecasted by CCCISand Insurance Information Institute.
Considering the above, loss cost optimization needs to remain a crucial part of every commercial auto strategy.
In our previous articles, we discussed how early accident data and data-enabled claims processes can improve claim outcomes. Today, we’ll discuss how data can protect you from fraud.
Commercial auto fraud is widespread
Fraud remains a significant recurring loss driver with high cost. In commercial fleet insurance, we are up against:
Underwriting/premium fraud: Underpriced coverage, especially in composite-rated policies.
Ghost vehicles: Trucks operating for months without being reported, then surfaced at claim time.
Wrong exposure: Submission information misrepresented exposure and prior experience.
Transactional fraud: The claim itself is manipulated.
Staged accidents: Planned crashes designed to trigger payouts.
Inflated bills and exaggerated injuries: Claims that don't match the actual impact.
How bad?
According to GoFleet, a single fraudulent commercial auto claim can result in $30,000 - 100,000 settlements. Defense expenses alone for a disputed commercial claim often reach $15,000–$40,000, regardless of the outcome.
Why are commercial auto insurance so exposed?
Fraudsters tend to favor staged accidents involving trucks over accidents involving private passenger vehicles because the mandatory insurance limits are much higher for commercial vehicles.
Plus, in commercial auto insurance, policies often cover “Any auto,” while vehicle lists are reconciled only during audits. Composite rating offers operational flexibility, but it also creates blind spots. This makes it difficult to prove whether a truck was legitimately insured.
A common scenario:
A truck involved in a loss was never reported in prior audits but is claimed as a “recent addition.” Without historical asset and telematics data, coverage disputes become complex and expensive.
Manual validation often fails
Fraud detection is embedded across these steps, but it is constrained by fragmented data. Each function validates information in isolation. Very little data is cross-checked early, and most verification happens after reserves are set.
FNOL: Policyholder submits incident details (date, location, vehicles, and drivers).
Coverage analysis & verification: Insurer confirms if the policy is active and the vehicle & driver match the terms. For a composite-rated policy, there is no record of vehicles that are insured.
Investigation & liability determination: The information relies on the information submitted by the policyholder and possible police reports, which can take weeks.
Damage evaluation: The claims team gathers narratives, photos, and video evidence. Analytics require a custom process as data information is usually not standardized.
Medical & casualty evaluation: Assessment of reported injuries, treatment costs, and liability exposure.
Subrogation & risk transfer: The insurer identifies third parties who may be legally responsible for the loss.
Fraud detection is a critical part of these steps, but it depends on the quality of data that can be used. Monitoring fleets and using multiple data sources on an ongoing basis significantly enhances the chances to flag fraud early. The chances of missing flags are elevated unless information can be systematically integrated.
How early plausibility checks change the game
In trucking claims, fraud detection during investigation relies on plausibility checks – validations that help identify whether the reported facts align with objective data.
Draivn’s EZ Claims is designed to provide you with validated data, specifically to support plausibility checks, fraud detection, and rapid triage decisions. Here’s how it works:
Accident monitoring for enrolled fleets
If a fleet already uses Draivn, EZ Claims monitors fleet telematics, video, and contextual data to detect potential accidents. When detected:
EZ Claims performs plausibility checks to minimize false positives and verify vehicles against ELD and asset lists.
Validated data is delivered to claim workflows.
Insurers contact fleets early to understand severity, triage risks, and set reserves based on validated data
Overview report at the FNOL
If a fleet is not on Draivn, claim teams can initiate data collection at the First Notice of Loss:
A fleet files the claim, and an insurer onboards the fleet on Draivn.
EZ Claims collects accident-related data, normally within a day.
The data is compiled into a comprehensive accident overview and sent to the claims team.
All this doesn’t replace investigations, but prevents unnecessary ones.
Beyond objective data
Telematics and video provide relevant evidence, cutting off fraudsters who exploit commercial vehicles for payouts and reducing legal threats. EZ Claims empowers your claim and legal teams with such proof. EZ Claims makes this evidence available early, when it matters most.
Learn more about the solution or contact us at draivn.com for an EZ Claims demo.
