For an industry built on risk modeling, insurance companies face a surprising problem: not enough usable data.
Insurers accumulate vast amounts of data. Still, when it comes to training AI models, testing new products, modeling rare events, or sharing data across teams and partners, they face real shortages. Privacy rules are tighter, catastrophic claims are thankfully rare, new products lack much history, and we can’t copy sensitive health or financial data for testing.
That’s where synthetic data is stepping in.
Let’s take a closer look at synthetic data and why more insurance leaders are paying attention to it.
The Real Data Problem in Insurance
Insurance can be:
- Very sensitive in health records, financial details, and behavioral data
- Heavily regulated through HIPAA, GDPR, and CCPA
- Limited for high-impact events like cyberattacks, pandemics, and climatic disasters
- Segmented across multiple departments and systems
Privacy regulations create challenges. The National Conference of State Legislatures reports that nearly every U.S. state has recently passed or proposed consumer privacy laws, which makes compliance more complicated for insurers operating across multiple states.
Meanwhile, rare-event modeling is becoming more urgent. The National Oceanic and Atmospheric Administration (NOAA) reported 28 separate billion-dollar weather and climate disasters in the U.S. in 2023 alone.
But even with rising frequency, historical datasets remain limited for training predictive catastrophe models. You can’t build strong AI models with only limited historical data.
What Is Synthetic Data?
We generate synthetic data that statistically mimics real-world datasets without exposing actual customer information. It’s not random, and provides:
- Statistical distributions
- Correlations between variables
- Behavioral patterns
- Edge cases
But it removes direct links to real individuals. Gartner predicts that by 2030, synthetic data will replace real data in AI model training.
This prediction is a major change. For insurance companies, this opens up a powerful opportunity: experimenting without risk.
Why Synthetic Data Makes Strategic Sense for Insurers
Privacy-Safe Innovation
Customer trust is central in insurance. Synthetic data lets teams build, test, and stress AI models without putting sensitive policyholder data at risk.
According to the World Economic Forum, we recognize synthetic data as a privacy-enhancing technology (PET) that helps organizations comply with strict regulatory requirements and advance AI innovation. Finding the right equilibrium between innovation and regulatory risk is vital for large companies.
Modeling Rare and Emerging Risks
Insurance leaders face emerging risks that historical data can’t keep up with:
- Cyber events
- Climate volatility
- Self-driving vehicles
- Parametric products
- Pandemic-style disruptions
McKinsey states that advanced analytics and AI are critical for insurers to be competitive in emerging risk areas where current historical datasets fall short. Synthetic data enables actuaries and data scientists to simulate thousands of possible future scenarios rather than depending only on the past.
This aspect shifts the approach to proactively designing future scenarios.
Breaking Down Data Silos
Several insurance companies have fragmented policy administration, claims, underwriting, and customer engagement tools across separate departments. Synthetic data connects these systems while allowing for modeling environments without exposing live systems.
Teams can test analytics between divisions without risking real data, so that we can see:
- Faster model development
- Better cross-team collaboration
- Lower IT friction
- Fewer compliance bottlenecks
This functionality is significant for insurers upgrading older systems.
Accelerating AI and Machine Learning
AI projects stall because data science teams lack enough labeled, clean, and accessible data. The Stanford AI Index Report discusses how access to high-quality datasets remains a primary bottleneck to AI advancement.
Synthetic data fills that gap by:
- Increasing training data sets that produce more balanced class allocations
- Creating rare fraud or claims patterns
- Testing edge cases
Instead of waiting years for enough real examples of a new fraud pattern, insurers can quickly simulate thousands of them. That dramatically shortens development cycles.
Let’s be clear that synthetic data does not fix everything
Synthetic data is powerful but not magic. Poorly made synthetic data can introduce bias, distort risk signals, or minimize complex realities. If the original data is biased, the synthetic copies can amplify those distortions on a larger scale.
Good governance is still essential, and insurance companies need to manage synthetic data programs with the same care they use for model validation, fairness testing, and regulatory documentation. Synthetic data is still subject to regulations.
The Bigger Strategic Change
The important conversation insurance executives should have is whether synthetic data is more than a technical fix and is a strategic enabler. It allows insurers to:
- Test new underwriting models before market launch
- Simulate economic downturn scenarios
- Stress-test pricing methods
- Train AI claims triage systems are safe
- Build data partnerships without sharing raw customer data
In a market where speed and personalization matter, this process helps companies stand out. Most importantly, it lets companies develop without risking the human trust that insurance depends on.
Innovation Without Exposure
Insurance has always been about preparing for uncertainty. Ironically, many AI projects stall because companies lack enough safe data to experiment with.
Synthetic data changes that. It gives insurers space to test, learn, and build the future without exposing customers, breaking privacy rules, or waiting for the next big disaster dataset.
Companies that use synthetic data thoughtfully won’t just fix data shortages. They’ll open a more secure path to innovation since in insurance, safety is everything.
Welcome to the next era of insurance, moving at today’s speed. Agility Holdings Group (AHG) invests in innovative InsurTech, HealthTech, and related companies that aim to revolutionize access to insurance products, establish patient care, and improve health outcomes.
Please visit our LinkedIn page for more information about AHG.