Learning the data, automatically

Andrew Lee, head of Market Intelligence and Analysis
at Octo Telematics

Anyone that uses Amazon or Netflix will be familiar with their suggested algorithms. These learn from the products and films that customers regularly view and purchase to understand their buying or watching habits and provide them with products that correspond to their interests.

Google Adwords also combs search histories to provide the most relevant websites and products to tailor consumers’ browsing habits. While these algorithms are not yet perfected, they are early examples of machine learning and the growing ‘algorithm economy’.

Now, due to the increased efficiency and understanding of data management and easy access storage, particularly in the cloud, machine learning is becoming increasingly sophisticated and prevalent in a range of industries, including car insurance and telematics.

At its core, telematics is simply a way to transmit data on how a car is being driven. However, there is an increasing understanding that the data gathered can be used far more widely than just reporting on driver behaviour. Collecting information on collisions can not only help with fraud detection, but also forms the basis of trend analysis, says Andrew Lee, head of Market Intelligence and Analysis at Octo Telematics.

For example, if there’s a road which has a high volume of accidents, examining how these crashes occur can help understand why this area is so dangerous, and therefore help prevent accidents.

With 65% of businesses using telematics devices and around 750,000 telematics insurance policies in the UK, there is a huge amount of data being gathered. The more data available, the more we can understand. With smartphones, wearables, sensors on cars and the telematics boxes themselves, all connected through the Internet of Things, there is a huge amount of information coming at once, meaning that it could be very hard to sift through and analyse.

Machine learning could be the answer. If systems can ‘learn’ what the important data is and use automated and evolving processes to analyse it, there can theoretically be no limit to the amount of information that can be received and put to good use.

Better storage facilities also mean that there would be no need to discard information that is not of use now, but may be in the future. Even more importantly, when that information becomes useful, an AI that is teaching itself all the time will realise when to retrieve it and incorporate it into calculations.

The benefits are obvious to insurers. They are already looking at telematics data to detect fraud and build up databases of collision data for use in pricing risk. With the ability to analyse even more data points for each accident, they can also look at the type of car and how passengers are affected by crashes.

It also becomes possible to more fully understand the full range of forces at work and be able to build a better picture of each incident. No longer relying solely on evidence from one or two data points, insurers looking to more accurately assess and price risk can take advantage of a far greater number of data points to create precise trend data. Of course, this is of huge benefit to the drivers that are providing the data.

As they provide more information to their insurers, they can create a better, two-way relationship and benefit from both the trend data that is collected, as well as receive policy calculations based on their own, unique driver DNA. This collaboration between drivers and insurers can only lead to even better User-based Insurance (“UBI”) policies. These policies will be fine-tuned to the needs of an individual, rather than a demographic or age group.

However, this information can also be of value to car manufacturers. If drivers are regularly receiving the same type of injury, designers can use the analysis to make their vehicles even safer. If one type of accident is constantly resulting in a broken arm, for example, designers will be able to strengthen restraints or increase cushioning to avoid this.

Incorporating learnings will become doubly important in the design and ongoing development of autonomous cars. As well as safety functions to protect occupants, driverless cars will need to be constantly updating their awareness of their environment and understand not only the conditions, but the driver and incident history of the area they are travelling through. The vehicles will then be able to match their speed and approach to the road appropriately.

In extreme cases, we may even see legal issues arising from the data available and a company’s failure to analyse it properly. If a driver is in an accident and the data shows that they have been driving poorly for some time without flags being raised, stern questions may be asked regarding the purpose of collecting the data and why steps were not taken.

This is already in place for fleet owners subject to the Corporate Manslaughter and Corporate Homicide Act of 2007. Under the Act ‘aggravating factors’ such as deliberate concealment of an illegal nature of activity could mean that employers face huge fines. Insurance companies covering private vehicles could, potentially, find themselves in a similar position. Making sure there are no grounds for accusation will be key.

In any data-driven industry, there has always been a balance between having enough information and having so much that it becomes unwieldy and impossible to see the wood for the trees.

Through applying smart, learning algorithms and systems that can autonomously decide which information to store and which to analyse immediately, it is possible to sort through the volumes of information gathered and continue to build up a wide database of information and trends that will not only generate real value now, but also in the future.

The author of this blog is Andrew Lee, head of Market Intelligence and Analysis at Octo Telematics

Comment on this article below or via Twitter: @IoTNow_ OR @jcIoTnow

FEATURED IoT STORIES

9 IoT applications that will change everything

Posted on: September 1, 2021

Whether you are a future-minded CEO, tech-driven CEO or IT leader, you’ve come across the term IoT before. It’s often used alongside superlatives regarding how it will revolutionize the way you work, play, and live. But is it just another buzzword, or is it the as-promised technological holy grail? The truth is that Internet of

Read more

Which IoT Platform 2021? IoT Now Enterprise Buyers’ Guide

Posted on: August 30, 2021

There are several different parts in a complete IoT solution, all of which must work together to get the result needed, write IoT Now Enterprise Buyers’ Guide – Which IoT Platform 2021? authors Robin Duke-Woolley, the CEO and Bill Ingle, a senior analyst, at Beecham Research. Figure 1 shows these parts and, although not all

Read more

CAT-M1 vs NB-IoT – examining the real differences

Posted on: June 21, 2021

As industry players look to provide the next generation of IoT connectivity, two different standards have emerged under release 13 of 3GPP – CAT-M1 and NB-IoT.

Read more

IoT and home automation: What does the future hold?

Posted on: June 10, 2020

Once a dream, iot home automation is slowly but steadily becoming a part of daily lives around the world. In fact, it is believed that the global market for smart home automation will reach $40 billion by 2020.

Read more
RECENT ARTICLES

IoT set to overtake cloud computing as primary Industry 4.0 technology, Inmarsat research reveals

Posted on: October 14, 2021

New research by Inmarsat, the provider of global mobile satellite communications, reveals that investment in the Internet of Things (IoT) is set to overtake cloud computing, next generation security, big data analytics and other digital transformation technologies in the near future.

Read more

IDTechEx looks at the setbacks and explores how to move forward

Posted on: October 14, 2021

Bill Gates backed a Belmont smart city in the Arizona desert little has happened beyond a land purchase. Authorities demand that the Colorado river’s diminishing water supply is unharmed. Arizona suffers historic water shortage. The Southwest and much of the West is suffering from an intense 22-year drought, resulting in increasingly low water levels, dry

Read more