The rise of artificial intelligence in insurance – a double-edged sword

Insurance companies have relied on data for the life of insurance. Today’s insurers use big data from countless sources to insure more accurately, rate risk and create incentives to reduce risk. Advances in data capture and storage allow more information about consumers than ever before. From information technologies that track driving behavior to social media that creates a digital footprint that can deliver unprecedented insights, new data sources are now able to produce highly individualized customer risk profiles.

Insurers are increasingly using artificial intelligence (AI) and machine learning to manage low-complexity manual workflows, dramatically increasing operational efficiencies. Also behind the advent of AI-powered insurance is the ability to predict greater losses in accuracy and the behavior of its customers. Some insurers say it also gives them more opportunity to influence behavior and even prevent claims from occurring.

However, is there a risk that this new way of doing things could actually create injustice and even undermine the risk pooling model that is so central to the industry, making it impossible for some people to find cover? After all, AI is not a neutral technology and therefore it can be used in ways that reinforce the biases of creators. As a result, insurance companies must be particularly sensitive to ensuring that artificial intelligence and machine learning are ethically developed and used, and their customers’ data is managed with tight controls.

How ethical is artificial intelligence?

Artificial intelligence has become an integral part of daily operations in most industries, and it can be credited with condensing massive amounts of data into something more usable. But with companies coming under more public scrutiny to see how algorithms affect corporate behaviour, the question of how to apply machine learning and artificial intelligence ethically is a top priority for insurance leaders.

It is important to remember that AI is not a real cause; Algorithms have no morals; They are just algorithms. So instead of asking how ethical AI is in a company, we should ask to what extent ethics are taken into account by the people who design AI, feed it data and put it into the decision-making process.

For privacy issues, organizations are required to comply with the GDPR, the European legal framework for how they handle personal data. However, at the moment, there is nothing comparable to dealing with the set of ethical challenges presented by such a rapid rate of AI innovation. The European Union Artificial Intelligence Act, first proposed in 2021 and expected to be passed in 2024, is understood to be the world’s first international regulation of artificial intelligence. Therefore, despite the preparation of many legislations, there are still gray areas where companies have to rely on high-level guidelines that can leave plenty of room for interpretation. Therefore, for the time being at least, the responsibility lies primarily with companies, organizations and society to ensure that AI is used ethically. Insurers will need to consider the entire data ecosystem to achieve end-to-end AI governance, including the internal insurance vendors they share.

The value of implementing a clear ethical framework should be considered an essential component of successful adoption and value extraction.

As machine learning continues to generate significant additional value across insurance, the value of implementing a clear ethical framework should be considered an essential component of successful adoption and value extraction. In addition to transparency, key components of the WTW’s ethical framework include, for example, accountability and fairness – understanding, measuring, and mitigating bias – of models and systems in how they work in practice, as well as how they are built, and technical excellence to ensure that models and systems are reliable and secure that provide privacy Safety by design.


While insurance companies were already on a digital journey and innovating products before COVID-19, the pandemic has certainly hastened to track some of these shifts. Combined with more recent factors of increasing uncertainty in global markets and rising inflation, evolving customer demands have been placing tremendous pressure on the industry to transform rapidly.

In order to respond to customers’ expectations for speed and convenience, with products and services tailored to them, and experiences equivalent to those found elsewhere in life and online, insurance companies must innovate faster as AI technology becomes an increasingly indispensable component and function of in order to increase their risk management activities. The increasing use of AI in making decisions that affect our daily lives will also require a level of explainable transparency to employees and customers.

Given the huge volumes and diverse sources of data, the best way to realize the true value of AI and machine learning is when making intelligent decisions at scale without human intervention. However, this once achieved ability leads to a “black box” perception where most business employees do not fully understand why or how a particular action is taken by the predictive model. This is because, as companies leverage data more and the types of analytics and models they build become more complex, understanding the model becomes more difficult. This, in turn, leads to an increased demand for the “explainability” of models and an easier way to access and understand the models, including from an organizer’s point of view.

The question of how to apply machine learning and artificial intelligence ethically is high on the minds of insurance leaders.

Transparent AI can help organizations explain the individual decisions of their AI models to employees and customers. With the recent General Data Protection Regulation (GDPR) rule in place, there is also regulatory pressure to give customers insight into how their data is 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 they made that decision. This means that the bank must have a thorough understanding of how its AI model came to a decision, and be able to explain this in plain language.

Realizing the potential of artificial intelligence

Opportunities for more advanced pricing and an immediate P&L impact have never been better. Following the evolution of prices can enable transformational shifts toward advanced analytics, automation, new data sources, and the ability to respond quickly to changing market environments.

External data can help insurance companies better understand the risks they are underwriting. With the full picture of the driver and vehicle, car insurance companies can better assess risks and detect fraud. By feeding external data into analytical models, they can quote more accurately and attract desirable risk profiles at the right price point. Investing in AI can also enable an insurer to enhance the customer experience even more throughout the lifecycle of the policy – ​​from simplifying quote time to processing claims more quickly.

The demand for transparent and responsible AI is, of course, part of a broader discussion about corporate ethics. What are the core values ​​of an insurance company, how do they relate to its technology and data capabilities, and what governance frameworks and processes do it have in place to keep pace with these values? Ultimately, for AI to have the greatest 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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