Driving Behaviour Through Telematics
3 August, 2017
Posted by Bianca Huttner
This is the third blog in our series revealing the top 5 applications of Machine Learning in insurance. In our last blog we looked at the ways that fraud mitigation and loss prevention is being reduced through the application of this new technology. In this blog, we will discuss the use of telematics and AI and Machine Learning to determine driving behaviour.
Telematics represents a growing and valuable way to quantify driver risk for automobile insurers. Rather than making pricing decisions based on driver characteristics and vehicle type, telematics allows insurers to measure a driver’s behaviour. This allows for the burden of increased liabilities to be allocated accordingly while providing savings to infrequent and safer drivers.
Creating fairer insurance
For auto insurers, AI and Machine Learning represents an opportunity for carriers to take into account hundreds, if not thousands, of factors involved in calculating the potential risk of individual customers.
Data being produced at this granular level by hundreds of thousands of customers who have consented to sharing their data requires advanced Machine Learning in order to produce useful insights. When it comes to claims, patterns in driver behaviours can be combined and analysed alongside a range of other information, including local road and weather conditions, speed limits and geo-location information, for example, in order create an accurate and holistic picture.
This allows for incidents to be assessed on a personal, case-by-case basis, improving accuracy and reducing claims processing times over traditional methods.
Reducing insurance premiums
Car insurance powered by Machine Learning has the ability to provide feedback loops about driving behaviours that can benefit the drivers themselves. For example, this data can be sent directly to the holder of the insurance policy, or indirectly to parents of teen drivers, in order to transparently show what they can do to reduce their insurance premiums in the medium to long term.
This Machine Learning approach to enabling customers to improve their own driving habits will reduce claims overall, which in turn will result in savings in claims processing costs and settlements.
In broader terms, Machine Learning helps insurers identify which variables are highly significant to driver’s risk levels and which are not. For instance, many insurance companies treat large or powerful vehicles as intrinsically high-risk factors, yet scenarios may not be as such in actuality.
Fringe benefits of Machine Learning
Telematics technology also has the tangential benefit of reducing theft claims, as it acts as a tracking device in equipped vehicles.
AI and Machine Learning is enabling telematics to invigorate product developments such as personalised maintenance plans, discounted policy premiums and superior roadside assistance packages for drivers with a lower risk profile.