I recently had the pleasure of attending the second annual Insurance Data Science Conference in Zurich, Switzerland and it may not come as a surprise to you to learn that AI and ML in insurance topped the list of discussion items. But what may surprise you is the context within which they were discussed: autonomous cars. Yes, the reality of autonomous vehicles – AVs – and even connected and autonomous AVs – CAVs – is upon us and the implications for the insurance industry are significant. Even trucks are getting a look-in thanks to Tesla and Daimler’s foray into semi-autonomous big rigs. In short, the technology is ready, but what effect will the uptake of these autonomous vehicles have on insurers?

Insurers are having to ask – and find answers to – some serious questions, particularly if the government’s target to allow CAVs onto UK roads by 2021 is to be realised. Resolving questions such as how risk should be priced, or deciding upon best practice when it comes to managing claims, are crucial to the industry, but will also help address uncertainty amongst consumers, who are equally apprehensive at the prospect of CAVs taking to the roads in the next five to 10 years.

Technology has forever altered the transportation landscape, and so has the way in which we look at insurance for transportation. As a data scientist, the question I would then ask would be; it possible that data science can help to answer some of these questions? In my view, from a business perspective it certainly can, on the assumption that insurers regard data as a strategic asset that can inform better decision-making which requires four steps:

1. Identifying the decisions that have the biggest business impact, in order to create an analytics framework
2. Ensuring that the internal and external data being collected and stored is good quality to support those decisions
3. Provision of expertise to identify ways to improve these and run analytics regularly
4. Creating the right technology infrastructure to support data and analytics

These steps create the right foundation for the application of data science, which really comes to life in the right hands, such as AI engineers, data analysts and research specialists who are being hired in increasing numbers by insurers as they look to understand safety and financial-risk implications around CAVs, and seek to develop products around them. The US-based company is a great example of this – it had 74 job openings related to AI engineers and data scientists from almost 1000 open listings in the US between September and December 2018.

It’s an interesting time to be in the insurance sector, albeit uncertain as the impact of CAVs is not yet that clear. The best way for insurers to meet the challenge head on is to put data at the heart of the business, instilling the kind of data-driven culture that will help insurers stay agile and relevant, whether it be products and services for self-driving cars or the soon-to-be ‘old-fashioned’ people-driven kind!