The insurance industry is undergoing a radical transformation driven by the rise of artificial intelligence and big data analytics that enable companies to identify risks with unprecedented accuracy and set prices tailored to each individual profile. This development promises a more personalized and efficient future, but simultaneously threatens the collective sharing of risk and cost on which the insurance industry has been based since its inception.

Shared Risk-based insurance faces the challenge of “algorithms”
Insurance is based on a simple yet paradoxical principle.  On the one hand, it is founded  on shared risk: everyone contributes according to their means and is protected by the same pool when losses occur.
On the other hand, many argue  that technological advances, the explosion of  available data, and sophisticated pricing methods are undermining this shared risk through precise individual pricing that distinguishes between customer categories to an unprecedented degree.

Complicating matters is a strict legal framework that, in several countries, prohibits the use of sensitive data or discrimination based on gender, origin, or religion, even when such factors might correlate with actual risk.

The industry has now entered the age of algorithms and machine learning, where insurance companies can analyze vast amounts of data from smartphones, geographic location, driving behavior, lifestyle, and more. The goal: pricing based on “true risk.”

But this personalization may carry a dark side. Some warn that it could drive premiums for high-risk groups to levels that effectively shut them out of the market, threatening the very principle of collective protection on which group insurance is built.

Ethics under scrutiny
Insurance companies face complex legal and ethical questions concerning the limits of using sensitive data in risk assessment. European legislation in particular prohibits discrimination on the basis of gender, origin, or disability, and requires companies to adopt transparent and non-discriminatory pricing models.

With pressure mounting, companies find themselves required to clarify their pricing bases and incorporate privacy standards into their product design from the outset to avoid accusations of discrimination or opacity.

“More accurate pricing, not excessive pricing”
Dr. Charbel Bassil (Associate Professor at Qatar University's College of Economics) believes that AI as a technology can improve the accuracy of insurance pricing and reduce costs through its ability to develop routine processes that directly affect the cost and quality of these services. The ability of AI to quickly and accurately process large amounts of data helps detect fraud attempts and eliminate inefficiencies, which ultimately leads to lower insurance costs and improved services.

Basil points out that these technological developments may lead to more accurate and competitive pricing in the market, which is in the interest of both the sector and policyholders. He does not believe that it will contribute to excessive personalization of pricing, but rather to more accurate personalization, thanks to its ability to process data faster. With regard to the principle of shared risk or mutuality in insurance, Bassil suggests that any price reduction achieved through technology will have a positive impact on subscribers.

However, Bassil points out that the social groups benefiting from these developments may exclude those who are unable to participate in insurance due to their economic and social circumstances, i.e., those outside the insurance system.

Basil therefore stresses that legislation should focus on ensuring that the market remains competitive and not monopolistic, so that it is not dominated by a limited number of companies. Competition between companies contributes to lower prices and better services, while a monopoly of the market by a small number of companies makes prices subject to their control, regardless of the methods and technologies used. He also believes that the role of legislation should not restrict the use of technology itself, but rather protect subscribers' personal data and ensure that it is stored securely and accessed in a regulated manner.

A model that balances precision and shared risk 
The biggest challenge facing the insurance industry today is reconciling the precision offered by technology with the values of shared risk that have historically underpinned the sector. The way forward lies in finding a smart balance between the two via a model that allows everyone to pay according to their risk level, without excluding anyone from collective protection. After all, insurance is not just a mathematical equation, but a social contract based on sharing uncertainty