The False FNOL Problem: Why High-Resolution Data Is the Answer

First Notification of Loss (FNOL) is the moment that sets everything else in motion for an insurer. The faster and more accurately a genuine crash is detected and reported, the sooner the claims process can begin — and the more control the insurer has over cost, fraud prevention, and customer experience.

Yet for as long as telematics-based FNOL has existed, it has been plagued by a stubborn twin problem: false positives that flood claims teams with non-events, and false negatives that allow genuine crashes to slip through unnoticed.

Both are costly. Both erode trust in the technology. And both are increasingly solvable.

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The False Positive Problem

A false positive occurs when a telematics device triggers a crash alert for an event that was not actually a collision. The classic culprits are harsh braking, speed bumps, potholes, kerb strikes, mounting pavements, and even heavy items being dropped onto the vehicle floor.

For an insurer running a large telematics portfolio, even a modest false-positive rate generates a significant volume of non-event alerts. Each one consumes operational time — someone has to review it, contact the policyholder, determine whether a claim should be opened, and close it out. At scale, this becomes a serious cost centre. Worse, it risks alert fatigue, where genuine FNOL events are treated with less urgency because teams are desensitised by noise.

The root cause is straightforward: lower-resolution accelerometers struggle to distinguish between a sharp deceleration event and a genuine impact. The signature shapes of these events look similar when sampled at low frequency. The nuance that separates them is lost.

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The False Negative Problem

More dangerous still is the false negative — a genuine crash that the device fails to identify as such. These often occur in specific scenarios: low-speed urban collisions, side impacts where the deceleration vector is not aligned with the primary sensor axis, or multi-event collisions where the initial impact is moderate and the more severe impact follows.

False negatives mean the insurer does not know a crash has occurred until the policyholder reports it manually — sometimes days later. This delays intervention, increases claims costs through lost early-action opportunities, and opens the door to fraud, as the gap between event and report is exactly the window in which claims can be exaggerated or fabricated.

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Why Resolution Matters

The common thread behind both false positives and false negatives is data resolution. A device that samples acceleration at a low rate captures a simplified version of the crash event. The detail that distinguishes a pothole from a rear-end collision, or a harsh brake from a low-speed impact, lives in the fine structure of the sensor data — the microsecond-level changes in acceleration, rotation, and vibration.

Higher-resolution crash data, like that captured by the latest generation of telematics hardware such as the Teltonika FT platform, changes the picture fundamentally. With significantly higher sampling rates across both accelerometer and gyroscope sensors, these devices capture the full shape of the collision event rather than a simplified approximation.

This means our crash detection algorithms can work with genuinely differentiable event signatures. A harsh brake produces a different data shape to a rear impact. A pothole produces a different shape to a side collision. The patterns are there — but only if the hardware is capable of recording them.

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The Role of Algorithm Refinement

High-resolution data is the foundation, but it is not sufficient on its own. The detection algorithms must be trained and tuned to leverage that resolution effectively. At Axon, our ongoing programme of real-world crash testing — including our long-standing participation in events such as the ITAI Crash Test & Research Day at Upper Heyford Airfield — provides the ground-truth data needed to refine these models against genuine collision dynamics.

The combination of better hardware and better-tuned algorithms delivers measurable improvements:

• ›Fewer false positives, reducing operational burden and alert fatigue on claims teams.
• ›Fewer false negatives, ensuring genuine crashes are captured at the moment they happen.
• ›Faster trigger response, shortening the gap between event and FNOL.
• ›Richer event context, providing claims handlers and investigators with more detail to work with from the outset.

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The Business Case

For insurers, the value of solving the false FNOL problem is both direct and compounding. Lower false-positive rates reduce operational cost per policy. Lower false-negative rates reduce leakage from late-reported and contested claims. Faster, more accurate FNOL improves the customer experience at what is often a stressful moment for the policyholder — and positions the insurer as responsive and in control.

Crucially, the improvement is not theoretical. The latest hardware platforms are delivering the resolution needed to make this real today. The question for insurers is not whether high-resolution crash data will improve their FNOL performance, but how quickly they can adopt devices that capture it.

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Looking Ahead

As telematics hardware continues to advance, the false FNOL problem will become increasingly marginal. But getting there requires more than just better sensors — it requires a platform partner that understands how to turn high-resolution data into accurate, actionable crash intelligence.

That is what Axon does. If you would like to understand how our crash detection capabilities could improve FNOL accuracy across your portfolio, we would be glad to talk.

Get in touch with the Axon team →

— The Axon Telematics Team