Synthetic Data in Insurance: Future Opportunities and Hidden Risks

Aug 30, 2025 AI, Data management , Article , AI, Data

By Łukasz Terlecki

The insurance industry is navigating a period of rapid digital transformation. Among the innovations gaining traction in this area is synthetic data – artificially generated information that mirrors the patterns we see in real customer data, but without exposing any actual individuals. As advanced algorithms and AI technologies accelerate, insurers are increasingly turning to synthetic data to improve how they assess risks, protect privacy, and operate more efficiently. But as we welcome these innovations, it’s worth stopping to ask: is synthetic data the answer to all the industry’s data challenges, or does it also bring a new set of risks?

Pros of Synthetic Data in Insurance

One of the key advantages of using synthetic data in insurance is its built-in privacy. Since synthetic datasets do not contain actual individuals, they offer stronger privacy protection and help ensure compliance with regulations such as GDPR. Additionally, synthetic data is highly scalable, meaning it can be generated in virtually unlimited volumes. This is especially useful when there is a lack of real-world data for specific groups or scenarios. Another benefit is its customisability – synthetic data can be tailored to model highly specific risks or demographics, making it a powerful tool for refining insurance models and strategies.

How Do Insurers Create Synthetic Data?

Insurance companies are already generating synthetic data, using a variety of methods for this purpose:

Generative Adversarial Networks (GANs): These advanced AI models learn how to generate surprisingly realistic data through a competition between two neural networks.
Variational Autoencoders (VAEs): These models “compress” real data into a basic form and then rebuild it, generating new, similar examples.
Statistical Simulations: Classic mathematical techniques that recreate patterns seen in real claims or customer data.
Transformer and Diffusion Models: Recent breakthroughs that help produce highly accurate and diverse synthetic data.

Synthetic data mimics real data by preserving its statistical properties, allowing firms to perform meaningful analysis without using sensitive or restricted information. It’s generated through methods like AI, simulations, or statistical modelling, and can be tailored for specific needs – ranging from testing environments to advanced analytics. This makes it a powerful, privacy-compliant alternative to real data, especially valuable for international insurance companies facing regulatory and data access challenges. By offering scalability, cost-efficiency, and the ability to simulate rare or risky scenarios, synthetic data gives organisations a strategic edge in innovation and decision-making.

Synthetic data is not a perfect replica of real data, and its reliability depends heavily on how well it’s generated and validated. If done poorly, it can lead to biased, misleading, or irrelevant results. But if done correctly – with robust statistical modelling, AI tools, expert oversight, and ongoing validation against real data – it can be a safe and highly effective alternative.

Where Is Synthetic Data Making a Difference?

Synthetic data is making a significant impact across various areas of the insurance industry, especially in underwriting, risk assessment, fraud prevention, and claims processing. In underwriting and risk assessment, synthetic data enables insurers to model rare or hypothetical events, such as emerging risks or catastrophic scenarios, without needing to rely on historical occurrences. This kind of “what if” testing strengthens policy design and preparedness. It also offers a powerful tool for promoting fairness – because insurers have full control over the data inputs, they can actively work to minimise bias, resulting in more equitable pricing and approval decisions. Moreover, synthetic data can help to fill the gaps where real-world data is lacking. If certain demographics or risk profiles are underrepresented, synthetic records can augment the dataset, allowing for more accurate and inclusive analytics.

When it comes to fighting fraud, synthetic data enhances the training of AI models by incorporating a wide range of fraud patterns, from common scams to rare or evolving tactics. This helps systems detect suspicious claims more effectively. It also allows insurers to stay one step ahead by simulating new fraud methods before they become widespread. Importantly, synthetic data enables collaboration across organisations, since companies can share insights without compromising real customer information.

In claims processing, synthetic data supports smarter automation by helping AI systems learn how to handle a broad spectrum of claim types with speed and accuracy. It also strengthens analytical capabilities, allowing insurers to anticipate and prevent complications in the claims process. Before deploying automated tools, insurers can use synthetic data to rigorously test systems against unusual or complex cases, ensuring they perform reliably in real-world scenarios.

Navigating Privacy and Compliance

Synthetic data offers insurance companies a powerful way to meet, and even exceed data privacy regulations. When generated correctly, synthetic data can be classified as anonymous under laws such as the GDPR, significantly reducing regulatory burdens. Techniques like differential privacy, which introduce controlled randomness into the data, help further protect individual details and enhance overall privacy safeguards. Additionally, because synthetic datasets don’t contain real customer information, they are much easier to share across borders or with external partners, minimising the risk of exposing sensitive personal data. This makes synthetic data a valuable tool for navigating the increasingly complex landscape of data compliance.

Don’t Ignore the Risks

While synthetic data brings clear advantages, it also comes with important risks that insurers shouldn’t overlook. On the technical side, realism is crucial. Synthetic data must accurately reflect the complexity of real-world insurance scenarios. Otherwise, AI models trained on it may perform poorly when faced with actual cases. There's also the challenge of validation. It can be difficult to ensure that predictions based on artificial data will genuinely benefit real customers.

Beyond the technical hurdles, there are ethical and regulatory uncertainties to consider. One key issue is hidden bias. If the original data used to train synthetic data algorithms contains imbalances (such as overrepresenting certain groups or outcomes) those patterns can be inherited and even amplified by the synthetic data, potentially leading to unfair results. Transparency is another concern. The process of generating synthetic data is often complex, making it harder for regulators and customers to fully understand how decisions are being made. On top of that, laws around synthetic data are still evolving. This legal uncertainty can create risks for insurers, possibly resulting in unexpected compliance challenges or customer dissatisfaction.

Balancing Promise and Peril in Synthetic Data Adoption

Synthetic data holds transformative potential for the insurance industry, enabling enhanced risk modelling, fraud detection, and privacy-compliant innovation. By simulating realistic yet anonymised datasets, insurers can overcome data scarcity, test extreme scenarios, and promote fairness in underwriting. However, its adoption is not without risks. Technical challenges – such as ensuring statistical fidelity and avoiding bias amplification – threaten model reliability, while ethical and regulatory ambiguities demand transparency and rigorous validation. As the industry moves toward widespread use, success will hinge on striking a delicate balance: harnessing its scalability and privacy benefits while proactively addressing its limitations through robust governance, cross-industry collaboration, and adaptive regulatory frameworks. The future of synthetic data in insurance is bright, but only if its pitfalls are navigated as carefully as its possibilities are pursued.

Synthetic data offers insurers powerful opportunities to improve risk modelling, fraud detection, and compliance while protecting privacy. Its scalability and flexibility make it a strategic tool for innovation, but poor generation or hidden bias can undermine trust and accuracy. To unlock its full potential, insurers must pair technology with strong governance, transparency, and ongoing validation.

The future of synthetic data is promising — provided its risks are managed as carefully as its benefits are embraced.

Explore our Data Management offer