MyDrive: The One-Second-Data Doctrine

MyDrive: The One-Second-Data Doctrine

The reduction in the base price of in-car monitoring technology has made telematics a practical option for the car insurance industry.

Additionally, recent legislative decisions like the European Court of Justice’s gender ruling in March suggest that car insurance companies will lose the ability to price differentially when it comes to gender and potentially age.

As such, car insurers need a different means by which to assess risk and price insurance premiums accordingly. Through the analysis of behavioural data driven by insurance telematics, this has now become entirely possible.

Early arrivals in the insurance telematics space have opted for low volume transmission of data in order to minimize data collection for insurers. However, sampling every 30 seconds and during exceptional events is too low a rate to enable a true picture of the driver’s behaviour to be established.

The resulting “pay how you drive” propositions are somewhat better than the current proxies that car insurers are using to price risk. But going down the 30-second-logging, exception-based route will never lead to true understanding of individual driver behaviour.

One-second-data logging

It is possible to measure driving behaviour at a much more granular level. With more frequent data logging, the amount of data collected is significantly larger than that gathered by using the exception-based approach, but if done correctly, the increase in data will not swamp IT resources.

This results in a much more accurate view of driving behaviour, and therefore risk. The car insurance company can provide a more detailed assessment of the risk presented, and drivers receive the information required to understand how they drive and how to work on improvements as necessary.

Furthermore, a more frequent measurement allows the company to see more discrete behaviours, some of which might be missed by 30-second logging.

One-second-data logging should become the new industry standard. One-second logging allows insurers to move away from the old proxies that have traditionally contributed to drivers being treated as averages, with all of the inherent weakness of that approach.

Instead, drivers can be treated as individuals and insurers can therefore truly understand the risk presented by each and every one of their drivers.

Contextualising driver behaviour

Knowledge of driving behaviour is of little value when it cannot be related to the location of individuals and the type of roads on which they’re driving.

Indeed, the value of driving behaviour can only be truly derived when it is contextualised to the underlying road network.

Using GPS data allows car insurers to analyse what type of roads their drivers are most likely to spend time on, and thus assess the associated risk they present.

A driver who spends the majority of time driving on motorways, for instance, is in fact around six times less likely to have an accident than a driver spending the same amount of time on open rural roads.

On the other hand, a driver who consistently spends time on the road after 11pm is approximately three times more likely to have a fatal accident than a driver who simply uses his or her car to commute in daylight hours.

However, without the ability to relate this wealth of information back to the actual driving behaviour that has been recorded, car insurers can’t assess the level of risk presented to them.

It is thus critical that driving behaviour is always contextualised in relation to the road network, and specifically, the time of driving and the location of the car. This allows the insurer to understand exactly what hazards like roundabouts, junctions, and bends the driver is negotiating and with what level of competence.

Only by the utilisation of GPS data and cross-referencing to the map can this be done effectively.

Combining capabilities

Rather than rush into making hasty investment into early arrivals in the marketplace, car insurance companies would be wise to carefully consider what they are aiming to achieve from deploying insurance telematics technology.

One would expect that their goal is to understand at the most discrete level possible exactly the risk they are contracting to insure.

Early adopters of the technology are frantically trying to minimize the amount of data that has to be collected to make an accurate assessment of the actual risk presented, but 30-second data logging will never lead to true understanding of individual driver behaviour.

If car insurance companies do wish to drill down into the data to such a level that they understand the risk presented by individual drivers, they must pull together a number of capabilities.

These include behavioural data as derived by a minimum of one-second logging, geographical data as determined by GPS data, and psychological expertise to assess why drivers behave in the manner that they do on the road.

Until all of these criteria have been brought together and analysed as a complete picture, car insurance companies will not possibly be able to understand the risk presented by individual drivers.

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