How Telematics Can Support Accurate Insurance Pricing

Through various telematics devices, companies can collect data about how drivers behave behind the wheel. The data helps to understand how often and how harshly they break or accelerate, how fast they drive and how this speed compares with other road users. Putting all this together into a single score helps to measure how well an individual drives and it is this information that the insurer can use to provide additional discounts or other incentives to drivers who perform well. In our interview, Dr. Sam Chapman, Chief Innovation Officer and Co-Founder of a leading Telematics Organization explains how telematics data can support accurate insurance pricing and how it can benefit drivers and the environment.

How does “accurate” insurance differ from traditional insurance?

Traditional insurance has historically utilized a wide range of proxies to best estimate risk for individuals based on partial or indirect information. These factors used by insurers are proxies of true risk indicators, established when better data was not available.

Typically these include: vehicle make or model, credit history, claims history, home postcode, age, profession and – until recently – gender (the EU gender directive has already restricted the use of this unfair proxy risk factor on 21st December 2012, other unfair factors will likely follow).

Accurate insurance, instead of estimating risk from unfair proxies, aims to determine the true underlying risk being insured to more accurately price insurance from measuring the insured activity itself. In the case of motor insurance this includes a close understanding of how individuals actually drive, and is monitored by various movement measuring devices. Typically, this approach is known as telematics – the technology of sending, receiving and storing information via telecommunication devices.

Telematics often looks into the variables of an individual’s driving habits such as braking, acceleration, speed and mobile use, to analyze and understand driving behavior from which scores can be produced for insurers.

What will vehicle insurance (pricing) be based on in the future? What are the considered risk factors?

Pricing of motor (and other) insurance is gravitating – as it always has – towards the best known indicators of risk. Currently, the best indicators of driving risk originate from precise information about how people actually drive, for example; shifting from traditional insurance towards accurate insurance. This is of course not highly surprising as the activity being insured is now directly examined thus increased insight into actual driving logically helps prediction for real world risk of driving.

The understandings of driving behavior in telematics insurance are currently distilled due to vast amounts of data into proven behavioral scores or activity events for each individual journey – each of which can be used both by an insurer to help set pricing, but also by the driver to help understand, and minimize more risky behaviors. These are based upon extensive evidence including proven correlations to claims outcomes and also help to give educative feedback to individuals to challenge risky behavior.

As a relatively young science, models of driver understanding are still constantly improving. This is particularly so as they take on better quality data and new analytics. Currently, accurate driver behavior is understood by using either, a smartphone, a black box, an OBD (On board Diagnostic) device or OEM (original equipment manufacturer – in car electronics) to track mobility (typically GPS and accelerometer).

Future risk factors are likely to evolve beyond these systems however to include advanced mobility data such as radar and LIDAR (Light Detection And Ranging) style data to come up with a more complete understanding of driver risk incorporating surrounding driver interactions and proximities.

What can be measured and what can’t (for example, sudden lane changes)? Can it be fair?

Ultimately telematics is currently measured from raw sensors either embedded in a vehicle, added aftermarket or utilized from within personal sensors carried with the driver i.e. smartphones. In all such approaches the raw sensors of GPS and accelerometers are subject to a small degree of variability meaning they must be analyzed to build a complete and accurate picture of each vehicle in motion. The better the understanding of accurate motion the better the capability of the system.

What different types of accurate motor insurance are there? Which one do you think will prevail?

Regardless of the data gathered Usage Based Insurance (UBI) comes in many flavours. For example:

• TBYB – Try Before You Buy, where driver understanding is decoupled from a policy, but is used before a policy application to attract a favourable price based from sample driving.

• PAYD – Pay As You Drive, where policy costs are based upon usage levels, whereby usage is tracked to understand driving mileage or driving minutes to give a dynamic pricing based upon usage levels matching the insurers risk exposure.

• PWhenYD – Pay When You Drive, where policy costs prevent or encourage use in safer times and curtail long late night driving when risk is greatest. These are often sold as Curfew policies.

• PWhereYD – Pay Where You Drive, virtual geographical boundaries (geofence’s) can be used to constrain driving geographically whereby policy risk is minimized by encouraging driving within limited regions. This can also operate directly depending upon accumulating locational risk as drivers traverse roads of differing risk levels.

• PHYD – Pay How You Drive, this aims to directly understand how an individual drives rating according to precise behaviour of the driver.

For each of the above base forms of how telematics insurance can be administered and presented to a user where each approach has particular strengths and weaknesses, for example PWhenYD is proving to be generally less popular in younger drivers as it constrains end users and at times creates risk by encouraging drivers to speed to try and meet cut off times.

The truth that is emerging however, is that not just one approach will prevail. Instead, the good aspects from each approach are slowly being combined to obtain optimised solutions. Nevertheless, the best solution will differ from one country to another, insurers preference or demographic to match the needs of the insurer and the end drivers. Understanding the best approach is another science in itself.

Why do we need a different vehicle insurance model?

Traditional insurance is inherently unfair. Using proxies means drivers are often unfairly rated, for example paying for a demographic bias, or the prior claims of neighbors rather than actually related to their personal good driving. Accurate insurance models are more predictive in that they relate good driving to pricing. Flexible and tailored solutions maybe the future route to success.

How is the environment affected (for example, social benefits) by accurate insurance pricing models?

Telematics not only gives advantages to drivers with cheaper policies, discounts, added value services, but also to insurers with improved risk understanding and better drivers. However wider society also benefits from telematics, for example drivers are educated to drive differently using actual driving data to feedback minor changes.

These small changes encourage smoother driving and decrease aggressive accelerations that reduce risk, but also reduce pollution by optimizing fuel usage. What is more, anonymous data with user consent can be used to understand road pollution, improve traffic management and help to power smart cities of the future.

What are the most important advantages of an accurate insurance pricing?

Drivers policy prices without accurate insurance pricing have to factor into pricing underlying risks from potential bad drivers and fraudulent ‘cash for crash’ criminals. Without accurate insurance, data insurers find it hard to disambiguate risky policyholders from traditional rating factors alone.

Telematics offers motor insurance a measure of how individuals drive, thus, acting as a direct means to understand an individual’s risk as opposed to using discriminatory socio-demographic data. For individuals this means a fair price rather than paying for the errors of others with a similar car or postcode. As well as this the closer observation allows end users to also improve their driving and become safer road users.

What are the biggest challenges on the way to implement them?

Data size and complexity are a huge challenge compared to traditional insurance approaches. To understand accurate risk, it is essential to understand vehicle usage in a fine degree of sub-second detail, this alone presents a huge challenge resulting in 100’s of millions of miles of journey data in sub second precision. To conduct accurate and meaningful analytics with a proven correlation to claims is a very complex task.

Do you think telematics supported Usage Based Insurance (UBI) will benefit insurers, drivers and the environment? If yes or no, tell us your reasons for the choice in the comment section.


Please note that this article expresses the opinions of the author and does not reflect the views of Move Forward.

How Telematics Can Improve Road Safety and More Cost-Effective Driving

Telematics and vehicle monitoring has long been associated with solutions to improve road safety and also cost-effective driving. In our interview, Dr. Sam Chapman, Innovation Leader and Co-Founder of a leading Telematics Organization explores these aspects and explains the advantages telematics can bring to both safety and cost-effective driving.

Which aspects of telematics are utilized when it comes to road safety and cost-effective driving? (Is it the same approach?)

Understanding risk and cost-effective driving are complex problems and not merely a matter of monitoring variables such as speed, accelerations and braking. Cost-effective driving and the associated risk is influenced by all aspects of a driver, the vehicle and the environmental conditions it operates in. Lowering risk and lowering fuel costs are also linked to a degree, but they are far from the same thing, sometimes these particular goals even compete.

Each goal of driving improvements is explained in more detail:

Risk Reduction: this has been studied from across the worlds of accident prevention, safety education, road design and actuarially by insurance firms. Looking at this huge body of work, it is clearly recognized that risk on the road is influenced by a long list of interacting influencing factors.

For example, black cars at night are reported to be approximately 47 percent more likely to have a crash than white, silver or gold colored cars. However this and many other risk factors are often proxies for other areas of driving behavior, for example, black cars are more commonly selected by younger or less experienced drivers, in daylight hours black cars have on average 12 percent more reported incidents.

Is the underlying risk the color of the car? The time of day? Or, on average who, typically, drives a color vehicle? The truth is that the underlying cause of risk is often a confusing subject, but the best means to understand risk today incorporates advanced analysis of real-time driver telemetry. Combining data with widespread risk, crash, driver, geographical, claims and other data.

The task is a complex science, but monitoring speed, acceleration and braking alone is clearly not a good substitute to understand real risk at all.

Cost-effective driving: has been taught to drivers, particularly those in commercial fleets, as well as incorporated into various manufacturers’ vehicle and dashboard optimizations to promote improved fuel efficiency.

Ultimately, cost-effective driving is a factor involving a number of variables such as driver behavior, the vehicle being driven, the location, traffic interactions, signal and junction interactions, speed controls and many more. These are just a few of the factors to look into, however elements such as gear optimization, lower acceleration and steady long approach braking have been identified as conducive to better fuel economy.

Telematics aids in understanding both risk and fuel costs, but it must be clear that the most cost effective driving styles can potentially introduce increased safety risks. Also, we must note that at the same time the safest driving possible may not be the most fuel efficient. However, in general lower aggressive acceleration and increased road awareness is good for both risk reduction and fuel cost reduction.

Imagine, for example, a car approaching a junction where the signals are soon to change from green to red. A low risk approach would be to brake and prepare to stop as the light timing is such that it is better to wait for green than to rush into a light switching to red.

A fuel efficient approach for the same scenario encourages drivers to adjust speed less thus cruising through the lights to make minimal adjustments to speed, maximizing fuel efficiency. However, parts of the road network require speed changes to minimize risk, for example, corners, junctions, signals, lane merging, overtaking, and roundabouts and so on. In this case, highly fuel conscious drivers can optimize to the point whereby fuel usage lowers at the same time as risk levels increase.

Telematics provides means to detect and optimize these driving behavior aspects to further understand drivers, helping direct drivers to desirable road-user behaviors.

How will the advantages show in everyday life? (For example, lower accident rates)

Telematics solutions already demonstrate clear benefits to reduce fuel costs and have been widely adopted by large fleets to optimize expenditure of fuel. Telematics technology is also encouraged by insurance firms in wider private insurance markets due to the safety differentials of drivers, which select and use the technology, compared to those that do not. This differential is enough for insurance companies to offer discounts on top of the costs to provide additional telematics monitoring capabilities.

Throughout everyday life, this technology can be used to influence safety education – altering drivers’ behavior to make roads safer for all. In the event of an accident involving a telematics enable vehicle, emergency services can be summoned based upon transmitted data before humans contact the authorities. This capability leads to faster emergency response times, minimizing the ‘golden hour’ effect, which leads to a high percentage of traffic accident fatalities in those not receiving prompt medical treatment.

In what situations is real-time insurance pricing beneficial (For example, accidents, insurance claim etc.)?

Real-time data to support insurance pricing offers more immediate rewards for drivers encouraging safer driving in general. When it comes to claims, handling the ability of an insurer to manage claims is sped up as an evidence base is presented to help understand a crash with less need to rely on driver testimony from competing sides. This, in turn, leads to fairer claims and a minimization of ‘cash-for-crash’ schemes, which threaten road users’ safety.

How is safe driving behavior detected with telematics?

As well as providing a precise understanding of behavior of a vehicle, telematics is essential to correlate these to real risks to determine the root cause of risky behavior and not penalize drivers unfairly. Simply stating that driving or accelerating fast indicates risk, is not acceptable to pin-point risky behavior, as this often depends upon the wider context of a location, situation and time.

For example, pulling out across a lane of oncoming traffic is often required to accelerate fast in busy environments, but this in itself is not unsafe. However, driving slowly in such a setting can be unsafe. What telematics enables is determination of normal and a typical behavior in given situations, which can accurately be correlated to prior crash data.

This science is a big data science and actuarial challenge, but the result is very powerful and provides strong indicators towards risk likelihood for individual drivers.

How is the driver learning about better behavior patterns? And how are desired behavior patterns promoted (For example, gamification)?

Drivers are influenced by the world around them all the time, by factors such as friends, family, distractions, education, news, etc. What is often harder to understand is the means to encourage drivers towards a behavioral improvement.

For instance, advanced driver courses in young drivers often lead to increased chances of ultimate crash involvement not less. Although safety education can be helpful it can have unexpected impacts on drivers safety – any messages passed to drivers must be carefully balanced to deliver a positive effect in the right driver, thinking about the age and circumstances of the driver.

Gamification can address certain demographics to lead to overall improvements. However, in others this can lead to a ‘race to the bottom’ to get to the worst score or fastest travel time – it is these capabilities that can lead to unwanted behavior and can have detrimental effects across drivers. What is essential in feedback back to drivers is the delivery of an approach that monitors the impact of education and provides the desired effect in drivers, resulting in real safety changes.

In summary, telematics is a very powerful tool to monitor and understand driver behavior, but to interpret and influence driver behavior requires an expertise and experience that cannot be undertaken lightly.

Do you think telematics could help improve driver behavior and road safety? Share your opinions in the comment section.


Please note that this article expresses the opinions of the author and does not reflect the views of Move Forward.

How Telematics Data Can Help Monitoring Motor Vehicle Pollution

Road transportation is one of the biggest sources of pollution. Telematics data can help build up a detailed view of how emissions change throughout the day and over longer periods and this could assist government agencies and planners in understanding and tackling pollution. In our interview, Dr. Sam Chapman, Innovation Leader and Co-Founder of a leading Telematics Organization explains how telematics data provide actionable insight into the pollution that is generated as a result of vehicle use and driver behavior and how this data can be used to address the real causes of road pollution.

How are telematics used to monitor motor vehicle pollution? Does this apply to single vehicles or whole fleets of vehicles?

Traditionally, pollution has been understood in two ways: 1), by direct measurement of calibrated sensors at fixed observations points in urban centers and 2), by mathematical modelling of pollution levels between known observation points to estimate wider pollution exposure levels.

These approaches, however, measure exposure and not direct emissions from any source and so often prove inaccurate near variable emission sources and hard to control from a gap in understanding each emission source.

Modeling attempts to address this by tweaking predicted levels when in proximity to roads and industrial emission sources, although these approaches are known to be wildly wrong compared to actual measurements. In an ideal world, it would be possible to monitor with high grade sensors wherever is required, but this is not possible given the cost and number of sensors required. Instead, in-vehicle telematics now offers a new means to better understand pollution from motor vehicles far better than ever before.

Telematics has long been used in specialized fleets to monitor and control risk, but thanks to device-agnostic data capture and analytics carried out by expert telematics firms, it is now becoming much more widespread across general motor insurance provision and is more frequently enabled by inbuilt OEM (Original Equipment Manufacturer) technology which leads to blanketing entire regions with mobility sensor devices.

What exactly is monitored? Which data is collected?

Clearly pollution is generated by the burning of fuel within a vehicle, but each vehicle utilizes fuel at differing rates depending upon its individual activity, driving behavior, fuel type, operating temperature, state of repair, prior mileage and make and model of each vehicle. Despite these complexities, however, it is still possible to predict from sub second mobility data the emission vehicles emit within each environment to a high degree of accuracy.

One vehicle alone however does not provide accurate understanding, but combining mass telematics data can provide a new way to understand pollution, providing emission data at a fine-grained location utilizing mass telematics data.

What can be achieved with the collected data?

Telematics data, when analyzed by a team of professionals, can paint a clear picture of pollution emissions over time; linked to the road network in a fine degree of detail based upon how drivers traverse the location and the times they do it. This big data approach illuminates localized pollution hotspots and can find issues in traffic management or infrastructure that can potentially be altered to address resultant health concerns impacting everyone.

Can telematics also help to reduce motor vehicle pollution? In what ways?

Telematics increases the capability to understand vehicle pollution and this same understanding can provide means to address pollution on two separate fronts: One, is informing traffic planners and related authorities to design in changes to road management and networks to minimize causes of vehicle pollution. Two, involves encouraging end-drivers via feedback and education to change behavior where it causes undue impact upon the environment. For example, informing drivers of “smooth driving” techniques to minimize accelerations and braking not only reduces pollution, but also each driver’s fuel bill.

What are the biggest risks and restrictions in monitoring vehicle pollution with telematics? (For example when it comes to data safety)?

To process telematics data to better understand pollution does not require any understanding of the driver and also no understanding of exact journey endpoints as the combination of aggregated anonymized data gives the value when it comes to understanding vehicle pollution – we need no specifics with regards the driver.

With that in mind, the biggest risks of using telematics data for air emission understanding are with regards the accuracy of predicted information, as telematics vehicles do not cover all vehicles on the road. However, the telematics sample when compared to traditional modeled approaches still offers a performance improvement and very fine grained views that make it possible to address the real causes of road pollution.

Do you think telematics data could positively impact the environment? Share your opinions in the comment section.


Please note that this article expresses the opinions of the author and does not reflect the views of Move Forward.