Keeping up with AI and machine learning trends will require a data platform that can evolve alongside providers’ needs.
Insurance has been slow to adopt artificial intelligence – which is surprising for an industry that’s come to rely on advanced analytics. But as a long-established sector with huge regulatory burdens, technological changes have to be managed carefully.
Things are now changing, though. Experts are predicting a dramatic impact from AI in insurance over the next decade – as you can see in McKinsey’s comprehensive look at the potential insurance landscape of 2030.
In our last insurance- blog, we explored some of the tech trends that are influencing the trajectory of the industry. From continuing customer disruption to changing business models and blockchain adoption, there are going to be significant, growing data demands on insurers. Factor in AI, and there’s a real shakeup happening within insurance technology.
This will all have a major impact on how insurance providers plan for the long-term management and evolution of their data analytics platform.
Digital transformations require advancing technologies
In the past couple of years, data science and AI has started to make progress in insurance – but according to Deloitte, 40% of all organisations that haven’t yet invested in AI don’t actually know what they could use it for. Further Deloitte research predicts that as much as a third of insurance will come from completely new propositions by 2024.
Take Ant Financial’s Ding Sun Bao tool, for example. It’s an app where drivers can submit images of their car’s damage after an accident, to submit a claim. Using visual AI processing and machine learning capabilities, the tool can assess the photo and return a response to a claim in as little as six seconds, dramatically below the human claim adjuster average of almost seven minutes. For the insurer, that’s freeing up its people for more complex tasks and claims.
Advancing technologies require evolvable platforms
One pressing issue is that few firms today are thinking in a long-term strategic way about their AI platforms. Instead, they’re focusing on experimental, quick-win projects.
That kind of short-term thinking will ultimately keep insurers from making the most of AI, because building a data platform for AI is not a single, discrete project . For insurers to see continued success in their digital estate, they’ll need a platform that can evolve, adapt and provide long-term foundations for ongoing strategies.
Between the influx of data – which will continue to expand as new sources are added to the ecosystem – and the integration of AI, demand for advanced skills will grow. Insurers will need to hire data scientists to develop and optimise machine learning models, and coding specialists to build, maintain and evolve their platforms.
Many insurers won’t already have this level of capability in-house, and this kind of personnel investment can run to eyewatering figures, especially if insurers want to work on the leading edge of AI. Finding all the required skills will also take a long time, eroding the insurer’s ability to innovate and disrupt.
For a more cost-effective, supported approach, insurers can look instead to experienced partners – third parties that already have the deep technical knowledge and industry experience they need to help guide an evolving platform. Whether it’s short-term support with getting a project up and running and teaching existing personnel, or an ongoing strategic partnership, it can be vital to a successful, adaptable platform.
Learn more about data’s role in the future of insurance
As a Microsoft Gold Partner, we understand the challenges and opportunities of combining AI, data and intelligent analytics platforms for a more intelligent approach to business.
Our whitepaper, Data: The Key to Transformation for UK Insurers, explores the UK insurance market in its current state, discusses where it’s headed next, and digs deeper into the importance of a modern, ready-to evolve data analytics platform.
You can also talk to one of our data experts today, if you’d like to start thinking more practically about your own platform, and how to get started with a flexible, long-term vision for your organisation.