Telematics is transforming the way motor insurance risk is assessed and priced, but can insurers rely on the data they are receiving, asks Linden Holliday?
Traditionally, in order to price risk, car insurance companies have had little choice other than to use a combination of different proxies, such as sex, age, marital status, location and occupation. The algorithms and data models used by car insurers are hugely sophisticated and mathematically complex and, on average, result in a broadly accurate pooled risk. Surely, however, in the age of mobile phones, F1 telematics and high-speed internet communications, there is a more sophisticated means of calculating risk for individual drivers?'
Insurance telematics' is a 21st-century solution that has presented itself to car insurers as a viable option for the first time. There are now a small number of providers who are extolling the virtues of such an approach for the industry, but early adopters of this new technology need to be fully aware of the range and limitations of such products that are first to market.
What is insurance telematics?
Insurance telematics is the process by which data is collected and analysed to enable driver behaviour to be assessed, and the level of risk presented by each individual to be calculated. This allows an insurance company to assess individual risk much more accurately and, therefore, to provide a much fairer price to the proposer.
Why is it important?
The first reason is because the base price of the technology for in-car monitoring has fallen and, second, legislative changes are having an increasing effect on the industry. The European Court of Justice's gender ruling in March last year means that, from December 2012, car insurance companies will lose the ability to price differentially when it comes to sex. For example, at present, the average first-time driver insurance policy for a 17-year-old male who has just passed his test is £4,400, whereas his female counterpart can be insured for the first year at an average cost of £2,700*.
This gender-based differentiation will no longer be allowed, and there is also a possibility that car insurance companies will lose the ability to differentiate by age. As such, car insurers need a different means by which to assess risk and price insurance premiums. Thanks to the analysis of behavioural data, driven by insurance telematics, this has now become possible.
Bandwidth limitations
Early arrivals in this market are balancing the available technology and the bandwidth - the ability to transfer data from the car to a server via the telecoms network. Some providers have tried to minimise the amount of data that must be collected in order to make a prediction on driver ability.
The first suppliers to market have arrived via the existing fleet telematics networks and have, therefore, opted for low-volume transmission of data - sampling typically every 30 seconds, while also logging exceptional events. This rate of sampling is low to establish a full picture of the driver's behaviour, and the exceptions themselves are typically set without reference to hard evidence by the insurer for determining what a true exceptional driving manoeuvre is.
The currently available 'pay-how-you-drive' propositions may be better than the proxies that car insurers use to price risk. However, the 30-second logging, exception-based route will limit understanding of individual driver behaviour. It is possible to measure driving behaviour at a much more granular level.
The amount of data collected is significantly larger than that gathered by using the exception-based approach and, 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, allowing the insurer to provide a more detailed assessment of the risk presented. It also gives drivers the information required to understand how they drive - and to work on improvements as necessary. A more frequent measurement also allows the company to see many more discrete behaviours, some of which might be missed by 30-second logging.
One-second data logging
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 thus truly understand the risk presented by each person.
Three criteria are essential to achieving individual-level risk assessment:
1. Technical data collection capability. The minimum requirement to truly understand individual risk is one-second logging.
2. Technical infrastructure. How will you collect the large amounts of required data and what are you going to do with it?
3. Driver psychology and behaviour understanding. For one of the most technically complex and stressful things we each undertake daily, what is the underlying psychology?
Putting behaviour in context
Knowledge of driving behaviour may be of little value when it cannot be related to the location of the individual and the type of roads on which they are driving. Indeed, the value of driving behaviour data can be enhanced when it is seen in the context of 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 assess the associated risk.
A driver who spends most of their time driving on motorways, for instance, is around six times less likely to have an accident than a driver spending the same amount of time on open, rural roads. Likewise, 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 their 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, how is the car insurer to assess the level of risk? Driving behaviour should be examined in the context of the road network and, specifically, the time of driving and the location of the car. This allows the insurer to understand exactly what hazards - roundabouts, junctions, bends, and so on - the driver is negotiating, and with what level of competence. Only by using GPS data and cross-referencing to the map can this be done effectively.
Conclusion
With the advent of insurance telematics, car insurers are on course to treat each of their drivers as individuals. Such innovation brings great opportunity.But without a true assessment of behavioural, psychological and geographical data, analysed as a complete picture, they could be missing out on useful rating information.
*BBC News, "Insurance and pension costs hit by ECJ gender ruling," 1 March 2011
www.bbc.co.uk/news/business-12606610
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