There is also the risk of bias unfairness. AI models can unintentionally learn and produce biases presented in the training data, leading to unfair outcomes. As a result, a continuous monitoring for bias is essential, alongside a commitment for transparency and fairness in their AI applications.
A key question for regulators will be the extent to which their focus is on the internal use of AI by an insurer, as opposed to concentrating on the company’s actual outputs generated by AI.
With the main focus of regulators to date having been on the outputs (for instance, whether premiums are fair and non-discriminatory), the hope shared by many insurers is that this approach will persist.
A further problem arises with transparency.
All model users, stakeholders and regulators ideally require their models to be transparent. But this is not possible with GenAI, which is typically based around neural networks with a hundred or more labyrinthine layers, each containing thousands of ‘nodes’ (in effect, robotic neurons).
So how can we learn to cope without transparency?
Alternative criteria will need to be defined to allow use while retaining confidence in that use.
The AI takeover – redefining insurance
All too often, the insurance industry approaches risk from a one-sided perspective, only seeing the negative side. While this is a natural human instinct and typical of chief risk officers concerned with everything that could possibly go wrong, real-world risks tend to be two-tailed.
That is to say, insurers also need to think about the commercial risks of being slow to harness the powers that GenAI offers and being left behind.
Looking ahead, the insurance industry is likely to accelerate the pace at which AI and human expertise are integrated.
Insurers that invest in the necessary resources and capabilities to ensure the benefits of AI are effectively harnessed, while being mindful of its limitations and potential challenges, will be best equipped to thrive in this new era of insurance innovation.
GenAI will be profoundly transformative and far more so than analytics and machine learning were predicted to be 10 years ago.
Until very recently, industry leaders were sceptical as to how such tools could safely add value to their business.
Given the record speed at which these tools are evolving, coupled with an increasing awareness of the technology’s scope and transformative potential, we should be flipping the default question from ‘show me how GenAI can help in this part of the value chain’ to ‘explain to me why you’re not using GenAI here’.
