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Internet of Things

In a previous post last year, we discussed how Big Data is being increasingly used by insurance companies. As we wrote, Big Data analysis of increasingly large amounts of personal data offers the potential to better predict the risk of individual customers – e.g., identifying riskier drivers. Insurers have always tried to match each customer’s risk and price charged, charging more to riskier drivers, but criteria used have traditionally been the drivers’ age and accidents history, in addition to the car type and age. But Big Data is promoting getting more and information to help predict whether individual policyholders are likely to put in claims.
The major new source of information is enabled by the Internet of Things, linked to the emergence of platform- and ecosystem-based business models. As you know, the Internet of Things (IoT) “is a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction.” (source:
The number of such internet-linked devices is exploding, including in relevant aspects to the insurance industry. As part of Accenture’s research into the impact of IoT in the insurance industry, this consultancy firm recently produced an eye-opening report on its technology vision for this industry (available upon registration). Cars, equipment, fitness wearables and homes are increasingly woven into the IoT, providing insurers vast amounts of real-time information about the lives and assets they insure. After embedding real-time analytics into business processes, insurers may not only optimise the premium charged to individual customers, but they can also help individuals and organization to better manage and reduce their risks.

Continuing our focus on driving insurance, with connected cars insurers may be able to monitor driving habits – and hence risks – of individual drivers, and use that information to set a price which more closely reflects the actual risk of each particular case. High speeds, many abrupt stops and dangerous curves may be collected and reported, and increase the price to be charged. This goes well beyond the current Pay-as-you-go and usage-based insurance models (which are already quite more sophisticated than the traditional insurance models, which ignore these variables), but it will also develop these models. For example, travelled distance is already used in usage-based insurance; but even though it is an extremely basic variable, its periodic collection without automated processes is time consuming, costly and late. Moreover, self-driving cars fundamentally reshape driving risk and its sources, and will require insurers to adapt to this new reality.

As Accenture reports, “some insurers are even exploring solutions that blend image recognition or video analytics with deep learning and pattern recognition in order to, for example, assess vehicle damage after an accident or even adjudicate claims. The footage could be sourced from an on-board camera on a vehicle or from public cameras. And natural language processing and text analytics, coupled with machine learning, can help in detecting fraud when processing claims.” So, reporting claims regarding accidents that did not happen may well become an impossible fraud.

One final example, from the same Accenture report, regarding health insurance. “A US start-up called Beam Technologies created a smart toothbrush and app two years ago to help people track their brushing habits. In August 2015, it launched a new dental insurance product leveraging technology and data from the toothbrush. It hopes to reduce the cost of dental care and incentivize customers to take better care of their teeth”.

This is an amazing possibility brought by the IoT – but of course, so long for privacy, even in the most private place of the house…