Insurance Data: The Challenge of Successful AI Adoption
The global growth in the volume of data, coupled with huge advances in the capabilities of AI and machine learning, is having a game-changing effect on the insurance industry. Why, then, are so many insurers failing to leverage new technology to tap into the potential of their data?
We speak to Selim Cavanagh, Director of Insurance at Mind Foundry, who details things insurers can do to overcome barriers to AI adoption, ultimately resulting in reducing OpEx costs, better product offerings, and customer experiences.
What are the obstacles preventing insurers from effectively leveraging AI?
There are several obstacles preventing insurers using AI and machine learning to get the most out of their customers’ data.
Among these is the pressure to reduce operational costs without negatively impacting their products and services. AI can help here. According to Accenture, “tools like AI and automation have the potential to deliver a 30-40% cost reduction”. But the compartmentalised nature of many large insurance organisations means they’re unable to take a procedural approach to AI adoption.
Furthermore, many lack a strategic approach, meaning that any AI systems they have adopted are being siloed across different departments and business lines, acting more as individual R&D projects than strategic initiatives to drive growth and deliver ROI.
Are there any surrounding issues creating barriers to adoption?
Understandably, the current macroeconomic climate is also affecting the industry. The cost of second-hand cars, parts and labour have soared, for example, slashing the already small margins in motor insurance and leading to increased claims costs. The effects of these changes are being felt by customers, too, in the form of increased premiums as insurers look to balance their books.
Perhaps the most significant obstacle, however, concerns the data itself. The way it is collected, processed, stored, and used is subject to specific – and increasingly stringent - rules and regulations. Maintaining regulatory compliance when using AI to extract value from data is hugely challenging.
What’s the first thing insurers must do to overcome these barriers?
The first step to addressing these obstacles is to take the correct approach from the start.
Some insurers mistakenly think that because they have access to huge volumes of data, they’ll see immediate rewards simply by adopting a form of AI. This, though, results in a wealth of off-the-shelf solutions that bring limited benefits and that can represent risks around data protection and compliance. Others hire consultants to build custom solutions, but these can be costly and hard to maintain post-deployment. Essentially, starting with the wrong approach to AI adoption can lead to the wrong results.
Instead, a “problem first” approach is necessary. Establishing which departments are failing to get the most out of their data, where the problem is most severe, and how quickly a solution can be devised and integrated is the first step in finding the right AI solution. This can be done in collaboration with operators who will bring the necessary experience and AI expertise, helping to identify areas where AI and machine learning have the potential to bring the most value.
How important is it to find the ’right’ AI solution?
Having identified where the problems lie, it’s vital to build and implement the right AI solution. It’s essential that any solution is customised to suit the nuances of the problem and the data it’s dealing with, as well as being compliant with all relevant regulations. This requires an AI partner that not only understands these issues but has the resources to resolve them.
Likewise, in an industry where new data is constantly being generated and incorporated, it’s important to consider model performance post-deployment. Machine Learning models can fail due to a variety of factors, and when this happens, you can’t afford to find out long after the fact. The damage may already have been done. This means that systems for model governance that give real-time feedback on model performance have quickly become integral to the success of AI adoption. Continuously feeding data back into the original models will help the AI to improve over time and not become an unexplainable ‘black box’.
What are some of the potentials that implementing AI will unlock for insurers?
Driving real business value requires a comprehensive portfolio of customised models performing a variety of functions, each of which have the capacity to build and integrate new ones when the need arises.
Technological capabilities that allow for the building, deployment, management, and monitoring of a portfolio of models in a unified environment can go a long way to unlocking the full potential of insurance data with AI.
Adopting AI is more complex than buying and integrating software. It requires a strategic, nuanced and collaborative approach – even a large insurer won’t possess all the necessary data, technology, and skills. It’s important, therefore, for insurers to find partners who understand their data and their problems and can marry these to their AI capabilities and expertise, and, by doing so, maximise the value of the vast wealth of data they hold.
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