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The rise of AI in insurance – A double-edged sword

Insurance companies have relied on data since insurance has existed. Insurers today are using big data from a myriad of sources to underwrite more accurately, assess risk, and create incentives for risk reduction. Advances in data capture and storage are making more consumer insights available than ever before. From telematics that tracks driving behavior to social media that creates a digital footprint that could offer unprecedented insights, new data sources are now capable of producing highly individualized profiles of customer risk.

Insurers are increasingly using artificial intelligence (AI) and machine learning to manage low-complexity manual workflows, dramatically increasing operational efficiency. The ability to more accurately predict losses and the behavior of their customers is also driving the rise of AI-powered insurance. Some insurers say it also gives them more opportunities to influence behavior and even prevent claims from occurring.

Yet, is there a risk that this new way of doing things will create injustices and even undermine the risk pooling model that is fundamental to the industry, making it impossible for some people to cover themselves? After all, AI is not technology agnostic and therefore can be used in ways that reinforce the biases of its creators. Therefore, insurers need to be especially sensitive to ensure they develop and use AI and machine learning ethically and manage their customer data with watertight controls.

How ethical is AI?

AI has become an integral part of daily operations in most industries and can be credited with condensing vast amounts of data into something more usable. But as companies come under greater public scrutiny of how algorithms influence business behavior, the question of how to ethically apply machine learning and AI is a priority for companies. insurance leaders.

It’s important to remember that AI doesn’t really reason; algorithms have no ethics; they are just algorithms. So, instead of asking how ethical a company’s AI is, we should be asking how ethical is considered by the people who design the AI, feed it data, and implement it. use to make decisions.

For privacy issues, organizations are required to comply with GDPR regulations, the European legal framework on how to process personal data. Currently, however, nothing similar is in place to address the myriad of ethical challenges presented by this rapid pace of AI innovation. The EU AI law, first proposed in 2021 and expected to be passed in 2024, is considered the world’s first international AI regulation. Thus, although various legislative texts are in preparation, gray areas remain, companies having to rely on high-level directives which could leave a significant margin of interpretation. Therefore, for now at least, the responsibility lies primarily with businesses, organizations and society to ensure that AI is used ethically. Insurers will need to think about their entire data ecosystem to achieve comprehensive AI governance, including the insurtech vendors they partner with.

The value of applying a clear ethical framework should be seen as an essential element for successful adoption and value extraction.

As machine learning continues to drive significant additional value across insurance, the value of applying a clear ethical framework should be seen as a critical element for successful adoption and value extraction. In addition to transparency, key elements of WTW’s own ethical framework, for example, include accountability and fairness – understanding, measuring and mitigating bias – models and systems in how they operate. in practice, in addition to how they are built, and technical excellence to ensure models and systems are reliable and secure, ensuring privacy and security by design.


While insurers were already on the path to digital and innovative products before COVID-19, the pandemic has certainly accelerated some of these transformations. In addition to more recent factors of growing uncertainty in global markets and high inflation, changing customer demands have put enormous pressure on the industry to transform rapidly.

To meet customer expectations for speed and convenience, with products and services tailored to their needs, and experiences equivalent to those elsewhere in life and online, insurers need to innovate faster, ‘AI increasingly becoming a must-have component and function. to increase their risk management activities. The increasing use of AI in making decisions that affect our daily lives will also require an explainable level of transparency for employees and customers.

Given the immense volumes and diversity of data sources, the real value of AI and machine learning is best harnessed when making intelligent decisions at scale without human intervention. Yet this capability once achieved gives rise to the perception of a “black box” where most business personnel do not fully understand why or how a certain action was taken by the predictive model. This is because as companies leverage data more and the types of analyzes and models they build become more complex, a model becomes harder to understand. This, in turn, is driving a growing demand for the “explainability” of models and an easier way to access and understand models, including from a regulator’s perspective.

The question of how to ethically apply machine learning and AI is a priority for insurance leaders.

Transparent AI can help organizations explain the individual decisions of their AI models to employees and customers. With the GDPR decision recently coming into effect, there is also regulatory pressure to give customers insight into how their data is being used. If a bank uses an AI model to assess whether a customer can get a loan and the loan is declined, the customer will want to know why that decision was made. This means the bank must have a deep understanding of how their AI model arrives at a decision and be able to explain it in plain language.

Realizing the potential of AI

Opportunities for more sophisticated pricing and immediate P&L impact have never been better. The pursuit of pricing sophistication can enable transformative shifts toward advanced analytics, automation, new data sources, and the ability to respond quickly to changing market environments.

External data can help insurers better understand the risks they underwrite. With a complete picture of the driver and vehicle, auto insurers can better assess risk and detect fraud. By feeding external data into analytical models, they can more accurately quote and attract desirable risk profiles at the right price. Investing in AI can also enable an insurer to further improve the customer experience throughout the policy lifecycle – from streamlining at quote time to faster claims processing.

The demand for transparent and accountable AI is of course part of a broader debate on business ethics. What are an insurer’s core values, how do they relate to their technology and data capabilities, and what governance frameworks and processes do they have in place to follow them? Ultimately, for AI to have the most impact, it must have the public’s trust.

Transparent AI can help organizations explain the individual decisions of their AI models to employees and customers.

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