Greater Than

Take away the models and raw position data is all that remains. Machine learning built on deep learning layers decomposes driver behavior into thousands of variables to find patterns the company states were previously invisible. It produces individualized predictions of accident probability and emissions per trip in real time. The models are protected by seven patents and have been trained continuously on driving data since 2004, yielding more than seven billion individual driver profiles. Crash probability scoring and dynamic premium calculation both depend on them.

Scores are produced automatically and continuously, and the stated use in fleets keeps a person in the loop, with risk managers identifying the highest risk drivers so that training, coaching or other intervention can follow. On the insurance side the position is different, since the platform enables dynamic premiums reflecting the actual risk of the individual driver in real time, which is pricing that adjusts without a human deciding each time. Nothing published describes a confidence indication on a score, a threshold above which a person reviews before a premium moves, or what recourse exists inside the system when a score appears wrong.

Model quality is engaged by attacking the industry standard rather than defending the company's own. Its published position is that harsh braking and acceleration events, the signals telematics has ranked drivers on for two decades, showed no relevant relationship to crashes in research the author conducted personally. That is a falsifiable claim about a competitor methodology, put in writing.

Behind it sit seven patents, 22 years of continuous training data, and selection into a European Union funded research program, which is external technical scrutiny. The weak point is the headline figure. An accuracy of 99.98 percent to factual risk and accident probability is quoted through third parties with no methodology, denominator or validation source attached, and a number that precise invites the question of what exactly was measured.

A global insurer and a major vehicle manufacturer's mobility arm are both named as clients, and a Nordic telematics group launched an entire insurance broker brand on these scores, covering more than 200,000 connected vehicles in one market, with its chief executive describing risk segmentation and pricing built directly on the models.

A leading policy administration platform partnered to distribute the crash probability and climate impact scores to its own insurer customers, whose executive is quoted on the arrangement. Training scale is exceptional and specific: billions of real world trips from more than 100 countries, equivalent to over 855,000 person years of driving. The company is publicly listed, 22 years old, and its technology was selected for a European Union funded research programme.

Pooling is the stated foundation of the product. Models are trained on billions of trips across more than 100 countries, and every new customer's driving contributes to a common pool of over seven billion driver profiles. The company discloses this as the source of predictive strength rather than concealing it, and it means one insurer's book improves the scores sold to its direct competitors. Nothing states whether a customer can decline to contribute, what is retained when a contract ends, or how the data of a fleet's drivers is separated from the insurers who might later price them.

The payload is granular personal behavioral data, and no data protection agreement, retention schedule, subprocessor list or consent description is public. Continuous position and driving data is captured second by second and assembled into a persistent individual profile. The platform holds where a person went, when, how fast and how carefully, accumulated over years. European operation imposes a statutory floor.

Nothing published states how long a driver profile persists, whether it follows a person between insurers or fleet employers, or what a driver is told when their employer enrolls the vehicle they drive.

Nothing public names an attestation, certification, trust center or enumerated framework. A global insurer and a major policy administration platform have both completed vendor assessment on this company, so the assurance exists privately. The platform holds continuous location and behavioral records on individual drivers across many jurisdictions. Publishing that control set would address the question every prospective insurer and works council will raise.

No supervisor, statute or instrument is named. The omission carries weight. Motor insurance pricing is among the more prescriptively regulated activities in European financial services, with restrictions on permitted rating factors and on differentiation between customers. The EU AI Act now treats risk assessment and pricing in insurance as a category requiring specific governance. A platform whose entire purpose is individualized premium differentiation across those markets identifies none of it.

The company makes a substantive fairness argument. Conventional motor risk ranking rests on lagging indicators, license violations and crash history, which price a driver on a past they cannot change. The company states its own research found no relevant relationship between the conventional event signals and actual crashes.

Pricing current behavior instead replaces history with something a driver controls, and the broker brand built on these scores markets exactly that as customer beneficial segmentation. The unaddressed exposure is that behavior is not free of circumstance either.

Night shift workers, urban drivers, long commuters and those in older vehicles drive in measurably riskier conditions for economic reasons, so behavioral scoring can reproduce income patterns without any protected characteristic appearing as a criterion. Replacing lagging indicators with behavioral ones moves the fairness problem rather than resolving it, and no analysis across driver populations is published.

The person being scored sees their own score. Drivers receive their risk profile and targeted guidance on avoiding crashes. One deployment sends regular performance reports directly to the customer, and the loyalty product rewards improvement. The subject of the model is told what it concluded and given a route to change it, which turns an opaque rating into a feedback loop. What is missing is challenge rather than visibility. No commercial guarantee or indemnity is published, and nothing describes how a driver disputes a score they believe reflects the conditions they drive in rather than how they drive.

The data chain is short, and that is a strength. Driving data is collected first hand through the company's own device, application or connected vehicle feeds rather than licensed from brokers. The models are its own and patented, so no external provider sits between the raw signal and the score. A named public cloud is disclosed as infrastructure for at least one product. Nothing further is published. No model provider is identified, there is no subprocessor list, and no statement says what mapping, weather or vehicle data supplements the driving signal.

Distribution reaches insurers through the systems they already run rather than requiring them to adopt another. A partnership with a major policy administration platform places the crash probability and climate impact scores inside that platform for its insurer customers to consume directly, which is the shortest path into underwriting and pricing workflows. One product is published on a leading cloud marketplace for procurement and deployment.

Data ingestion is equally flexible, spanning an on board diagnostics device, a smartphone application, and existing connected vehicle feeds, and one offering is stated to require no technology integration at all to be applied across an existing customer base, with driver onboarding handled by text message.

A named public cloud is identified as the platform behind at least one product. No region selection, residency commitment or private deployment option is published. The question is material given operation across more than 100 countries and the nature of the payload. Continuous location data about identified drivers is treated as sensitive in several of the markets served, and its processing location would ordinarily need stating.

Greater Than publishes no pricing, packaging or basis of charge. One product is listed on a major cloud marketplace, which implies a defined commercial unit exists, yet no rate appears there or elsewhere. Nothing indicates whether charge falls per scored driver, per vehicle, per trip or as an enterprise license. That matters because buyers range from a single fleet to a global insurer scoring an entire motor book.

The same scores serve four buyer types: motor insurers, fleet operators, mobility and car sharing providers, and vehicle manufacturers. A broker brand has also been built on the platform, which shows it supports both the underwriting and distribution ends of the chain. Training data is drawn from more than 100 countries and the scoring is described as geography independent, with named deployments in the Nordics, Australia and with a global insurer. The limit is line of business. This is motor risk exclusively, and everything the company does depends on a vehicle being driven.