Lots of news about Artificial Intelligence in the press Lots of news about Artificial Intelligence in the press. Some of the better known stories include: Deep Blue – Chess playing computer developed by IBM. In 1996 this won it’s first game against a chess champion under real time rules IBM Watson – A question answering computer system capable of answering questions posed in natural language. Watson was specifically developed to answer questions on the quiz Jeopardy and, in 2011, the Watson computer system competed on Jeopardy against former winners claiming the first place prize of $1 million. Alpha Go – designed by Google Deep Mind – In 2016 Alpha Go defeated Lee Se-dol ,the world champion of “Go”, 3-0 in a 5 match series, with 2 draws. Go is considered much more complex than Chess with over 200 possible moves at one time compared to 20 in Chess. Cortina (Mircosoft) & Siri (Apple) are examples of intelligent voice assistants which can set reminders, recognise natural voice without the requirement for keyboard input, and answer questions using information from the internet. Facebook’s chat bots were reported to have created their own language when speaking between each other and hence shut down in August 2017! But how can we use this technology?

In 2015 there was a major breakthrough in Artificial Intelligence development – AI had surpassed human accuracy in visual recognition tasks. The error rate for recognising images from a large database called ImageNet, with several million photographs of common, obscure, or strange images, fell from higher than 30% in 2010 to about 4% in 2016 for the best systems. Source: https://hbr.org/cover-story/2017/07/the-business-of-artificial-intelligence See puppy/cupcake example. Can determine how many puppies/cupcakes in the picture instantly. How long does it take you? Facebook can recognise friends faces and this technology is also being used within developments in self driving cars. So where do we perform visual tasks... Desk based engineering. Reviewing photos of damaged vehicles to perform a number of tasks: Audit reviews, damage assessment, determining a total loss, valuing a vehicle.

Video from Tractable to insert to explain who they are and how their technology works – needs to be cut to include 0:00 -2:27 and 3:01-3.40.

How are we using Tractable How are we using Tractable? Augmenting human engineers with AI technology to change the way we audit and understand performance of our Solution Centres (Approved Network). Our Solution Centres all have delegated authority to pay themselves with 10% of payments randomly selected for our in house engineers to audit. Due to the random selection of the audit we may not be as effective at understanding the performance of our Solution Centres as we could be and it is not possible to randomly review over 50,000 cases. Tractable has the ability to review all of the cases our Solution Centres are completing in real time, reviewing photos and repairer estimates to make a decision on if the most effective repair method has taken place. Tractable then only pushes cases to our engineers if there is a disagreement with the operation of the Solution Centre; only the meaningful cases that add value. If the engineer agrees with the AI, they will then discuss the cases with the repairer. Through this we have seen the following results: 10% of repairs have confirmed unnecessary replacements from over 7,000 cases. Potential savings of just under £50,000 have been identified. We now have more data to work with identified repairers to improve their performance. Already seeing average repair costs reduce for these Solution Centres. AI Agreement rate with our engineers is 94%, matching human performance.

We have only just started our journey into discovering how AI can improve the way we operate and have expanded our knowledge through the practical use. AI will play an important role in the future of car repairs and the future of claims handling. In the near future we will see immediate decisions on total loss; real time decisions on if a cosmetic or mechanical repair is required; auto authorisation of repairs and more accurate reserving. AI will also help us with a digital claims journey. If we are able to capture photos of an accident and damage at the scene, shortly Tractable will be able to generate an estimate from the photos alone. This may be able to remove some of the human touches within the claims journey as well as to speed up the process. This technology may also be used in other product lines in the future, ability to assess buildings damage from photos or generate an estimate for contents claims. As we learn more through practical use we will understand where this technology works well and where it doesn’t, we’re just scratching the surface.