This article is by Selim Cavanagh, Director of Insurance, Mind Foundry, and it looks at how AI pricing is becoming reality, plus how regulators will react to those pricing models.
The insurance industry is incredibly competitive as insurers jostle over the sheer numbers of customers, competitors and small margins. Pricing is like a microcosm of the insurance industry, with every insurer seeking the attention of time-poor and cost-conscious customers seeking fairer quotes.
Against this backdrop, artificial intelligence (AI) has become an essential part of any insurer’s pricing tool-kit. But just as any tool must be used properly to do its job, so must AI, and the best way to do that is effective governance to balance its value against unseen future damages.
Setting the scene
Before AI burst onto the scene, most insurance premiums were set using a fixed-commission-plus model that determined a price by assessing risk and adding an extra percentage to pay the broker, cover the distribution costs, and make a profit. While this worked for a long time, as soon as comparison sites became popular and competition increased, insurers needed to not only be able to predict the best price for their customers, but also anticipate market factors like the price their competitors were offering. In short, insurers needed to become more savvy.
Then larger datasets, cheaper processing power and new techniques allowed insurers to build more complex technical rating models.
AI is at the heart of these new approaches, and its role in insurance has grown and evolved. Linear models (GLMs) have given way to gradient boosting (GBMs), models have been chained together in increasingly complex pipelines, and once explainable, transparent models have become increasingly opaque, inscrutable, black-box ones. This has coincided with the looming spectre of tightening regulation and the subsequent need for responsible AI governance in the UK.
What is AI governance?
AI governance helps validate the aspects of AI that are going well. It’s a set of principles or processes that ensure AI is developed and deployed responsibly and ethically and is aligned with current regulations. AI governance is fundamentally about assessing the full implications of AI in operation and the potential impact of their AI on end-users, customers, and the organisation as a whole, both at the point the AI model gets deployed and on an ongoing basis.
AI governance is also an approach that ensures models are kept up to date and with an aim of maintaining the return on investment from the original build business case and then ensuring the model doesn’t drift into non-compliance over time
AI governance requires the involvement of multiple stakeholders who must ensure that an AI system adheres to their specific policy of interest – whether that be their pricing approach, compliance interpretation or data science correctness.
As AI becomes increasingly ingrained into our every day, governance is essential for any insurer looking to deploy AI models safely and responsibly.
Governance is about to become a reality
Over the last year, 73% of adults in the UK used a financial comparison website to pit insurers against each other to scope out the best prices for them. It’s a tricky game that requires pricing models that extract every ounce of insight from the customer’s data to generate profit whilst factoring in the quotes from their competitors. Yet generating the winning price is only the first step.
Explaining how and why the model came to that conclusion, is equally important. This is where model governance comes into play. Insurers need to consider the impact of their models on their customers and whether these adhere to stringent regulatory requirements.
In the UK, this is of paramount importance, considering the Consumer Duty legislation will come into effect on the 31st of July this year. The Financial Conduct Authority is looking to cover “larger fixed firms with a dedicated FCA supervision team, who primarily operate in retail financial markets”.
The new rules will make it a legal requirement for insurers to provide evidence that their pricing models are making recommendations fairly based on unbiased data and unprotected customer characteristics. On an individual model level, insurers need to ensure that their pricing models have sufficient levels of explainability to communicate their outputs to internal and external teams, customers, and regulators. It’s not just about offering cheaper insurance because even offering lower premiums can lead a customer to question whether they were getting fair value in the first place.
The cost of not complying
This year, we can expect the FCA to act faster and address more ongoing risks associated with AI, so insurers must be prepared. Insurers who are ahead of the game in AI governance will position themselves as offering fairer prices to their customers because they can communicate the reasoning behind every pricing decision. In insurance pricing, where enough marginal degrees of optimisation can lead to entire market dominance, being ahead of the curve in governance terms can be the dealbreaker, ensuring profitability and compliance as your AI scales.
As the Information Commissioner’s Office notes, “If the rules are broken, organisations risk formal action, including mandatory audits, orders to cease processing personal data, and fines.” If one model operates in a way that violates these rules, your entire business is exposed to these potential consequences. In 2022, the FCA handed Santander UK Plc a fine of £107 million after they repeatedly “failed to properly oversee and manage its anti-money-laundering systems”. This was one of 26 fines handed out by the FCA in 2022, the first year of their three-year strategy to become more innovative, assertive, and adaptive.
It is now or never
Some insurers have already woken up to the fact that AI governance is not going away. AI regulations around the world are no longer just ideas and proposals. Changes affecting how every organisation builds, deploys, and uses AI are already in motion. Governance is the protection every insurer will need to scale their AI in a way that can deliver significant value to customers and their business.
As we’ve seen with regulation time and time again, the time for talking about AI governance in insurance is over, and the time for action has arrived.
Be the first to comment