To synthesize data for insurance underwriting and claims models, you generate artificial datasets using machine learning techniques such as generative adversarial networks (GANs), variational autoencoders (VAEs), and statistical simulation methods that replicate the statistical properties of real policyholder and claims data without exposing any actual personal information. This approach works for any insurer needing to train or test AI models while navigating strict privacy regulations and the chronic scarcity of labeled claims data. The sections below unpack each dimension of the process, from what goes into these models to how you validate the output and stay compliant.
What types of data are used to train insurance underwriting and claims models?
Insurance underwriting and claims models draw on a wide range of structured and unstructured data sources. Structured inputs include claims histories, financial statements, policy records, and customer profiles. Unstructured inputs include broker submission notes, adjuster reports, satellite images for property evaluation, and even voice memos or chatbot transcripts from the first notice of loss. Together, these inputs give machine learning algorithms the raw material they need to assess risk and detect anomalies.
On the structured side, actuarial tables, loss ratios, and historical premium data remain foundational. But modern underwriting models increasingly incorporate IoT signals, including telematics from connected vehicles, wearable health devices, and smart home sensors, which allow insurers to price risk based on real-time behavior rather than static demographic proxies.
For property risk specifically, models may aggregate outputs from multiple sub-models covering perils such as hurricanes, floods, wildfires, earthquakes, and hail, each drawing on geospatial and environmental data. On the claims side, large language models are now being used to ingest simultaneously an Attending Physician Statement, an application form, an underwriting manual, and a calculator output, synthesizing them into a single recommendation. NLP pipelines process broker notes and inspection files that were historically handled manually, compressing what once took days into minutes.
The breadth of these inputs creates an immediate practical problem: much of this data is sensitive, siloed, or simply too sparse in certain categories to train reliable models. That is precisely where data analytics in underwriting has evolved toward synthetic generation as a core strategy.
Why is real insurance data so difficult to use for model training?
Real insurance data is difficult to use for model training because it is simultaneously too sensitive to share freely, too sparse in critical categories, and too fragmented across legacy systems to assemble into a clean, representative dataset. Privacy regulations, class imbalance, and infrastructure limitations combine to make raw policyholder data a poor foundation for modern AI development.
Privacy is the most immediate constraint. Insurers hold some of the most sensitive personal information in existence, including health records, financial histories, and behavioral data. Regulations such as HIPAA in the US and GDPR in the EU sharply limit how this information can be collected, moved between teams, or reused for model training. A single data breach could compromise thousands of customers, and generative AI models carry an additional risk: they can inadvertently memorize training data and reproduce protected health information during inference.
Class imbalance is the second major challenge. Claim events are inherently rare, and fraudulent or catastrophic claims are rarer still. In many portfolios, legitimate claims represent a small fraction of all policies, and catastrophic-event policies can have claim rates well below one percent. Standard machine learning algorithms trained on such skewed distributions tend to optimize for the majority class, producing models that are technically accurate overall but dangerously blind to the events that matter most.
Infrastructure compounds both problems. Many life and property insurers still operate on legacy systems with siloed databases that were never designed for real-time data exchange. Even when data exists, integrating it across business units for AI training requires significant engineering effort and governance overhead. According to research on AI readiness in insurance, data quality, integration, and governance consistently rank as the top obstacles to AI adoption across the industry. Traditional anonymization techniques offer only partial relief, because they tend to degrade the statistical utility of the data they protect, undermining the very models they are meant to enable.
How does synthetic data generation work for insurance datasets?
Synthetic data generation for insurance datasets works by training a generative model on real policyholder or claims data, then using that model to produce new, artificial records that share the same statistical structure as the original without containing any actual personal information. The generated data can be used freely for model training, testing, and sharing across teams.
Several generation methods are suited to insurance data, each with different strengths depending on the data type and use case.
- Generative Adversarial Networks (GANs): A generator and a discriminator network compete iteratively until the generator produces synthetic records indistinguishable from real ones. CTGAN, a GAN variant designed for tabular data, is widely used in insurance but can struggle with highly non-Gaussian distributions common in claims data.
- Variational Autoencoders (VAEs): VAEs compress real data into a low-dimensional latent space and then decode samples from that space into new synthetic records. TVAE employs mode-specific normalization to handle the severe class imbalance typical of insurance portfolios, making it particularly useful for fraud and catastrophic claims scenarios.
- Diffusion models: Newer approaches such as TabDDPM and TabSyn, which combine a diffusion model with a VAE latent space, represent the current state of the art for tabular synthetic data generation, offering high fidelity and better handling of mixed data types.
- Statistical simulation: Methods such as kernel density estimation generate synthetic data by modeling the underlying probability distributions of each variable. The Casualty Actuarial Society has used this approach in published actuarial research, demonstrating that it can preserve statistical relationships while enhancing privacy protection.
- Agent-based and rule-based simulation: Tools like the SynthETIC and SPLICE simulators generate individual synthetic claims with user-controlled characteristics, enabling actuaries to produce benchmark datasets for reserving and pricing research without touching real policyholder records.
In practice, insurers often combine methods. A VAE-GAN pipeline, for example, can generate diverse minority-class samples for fraud oversampling more effectively than either architecture alone. The choice of method depends on the structure of the source data, the downstream modeling task, and the privacy threshold required by the applicable regulatory framework.
What statistical properties must synthetic insurance data preserve?
Synthetic insurance data must preserve three categories of statistical properties: the marginal distributions of individual variables, the joint distributions and correlations between variables, and the accurate representation of rare events. If any of these properties is lost, the synthetic data will produce models that behave differently from models trained on real data, often in ways that are hard to detect until deployment.
Marginal and joint distributions
Marginal distributions capture how each variable behaves on its own, such as the shape of a loss severity distribution or the frequency of a particular claim type. Joint distributions capture how variables relate to each other: the correlation between a policyholder’s age and claim frequency, or the dependency between weather severity and property loss amounts. Synthetic data generation must replicate both. Comparing correlation matrices between real and synthetic datasets is a standard check, but higher-order dependencies and non-linear associations require more sophisticated validation, particularly for tabular insurance data where features often interact in complex, domain-specific ways.
Rare event representation
For insurance modeling, rare events are not edge cases to be smoothed away; they are the primary target. Catastrophic claims, fraudulent submissions, and tail-risk scenarios drive the majority of economic impact even when they represent a tiny fraction of records. Synthetic data generation must accurately reproduce these infrequent but critical occurrences rather than averaging them out. This is technically demanding: generation models trained on highly imbalanced data tend to underrepresent the minority class unless explicitly designed to handle it, for example through TVAE’s mode-specific normalization or targeted oversampling pipelines.
A related risk is hidden bias. If the original training data overrepresents certain demographic groups or systematically underreports certain claim types, the synthetic data will inherit and can amplify those patterns. Actuarial review is therefore essential alongside purely statistical checks, because a dataset can pass quantitative fidelity tests while still encoding assumptions that would produce unfair or inaccurate underwriting decisions.
How do you validate that synthetic insurance data is fit for model training?
Validating synthetic insurance data for model training requires evaluating three interdependent dimensions: fidelity (how closely the synthetic data matches the statistical properties of the real data), utility (whether models trained on the synthetic data perform comparably to those trained on real data), and privacy (whether the synthetic records can be used to re-identify real individuals). These three dimensions are sometimes called the validation trinity, and they exist in tension with each other.
Fidelity and utility testing
Fidelity is assessed using statistical tests such as the Kolmogorov-Smirnov test for continuous variables and chi-square tests for categorical ones, applied across both univariate and bivariate distributions. Correlation matrices are compared between real and synthetic datasets to confirm that multivariate relationships are preserved. The UK’s FCA synthetic data validation framework formalizes these three dimensions as the standard evaluation structure for financial services applications.
Utility is validated through a method called Train on Synthetic, Test on Real (TSTR): a model is trained on the synthetic dataset and then evaluated on held-out real data. If performance metrics such as AUC, precision, and recall are comparable to those of a model trained entirely on real data, the synthetic dataset is considered fit for purpose. Research in adjacent domains, including healthcare, has found that models trained on synthetic data can approach real-data performance, with deviations that are manageable for most downstream tasks, though tree-based classifiers tend to show larger gaps than other model types.
Privacy auditing
Privacy validation tests whether the synthetic data creates a meaningful disclosure risk. This involves checking for membership inference attacks, where an adversary attempts to determine whether a specific individual’s record was part of the training data, and testing for exact disclosure, where synthetic records reproduce real observations verbatim. Adding differential privacy to the generation process provides a formal mathematical privacy guarantee but typically reduces both fidelity and utility, making the calibration of that trade-off a judgment call that depends on regulatory requirements and model sensitivity.
Beyond quantitative metrics, actuarial review remains indispensable. Domain experts look for patterns that pass statistical tests but defy actuarial logic, for example, a synthetic dataset that correctly reproduces aggregate loss ratios but generates implausible combinations of coverage type and claim severity. Bias audits are equally important, particularly for underwriting applications where discriminatory patterns in the synthetic data could propagate into pricing decisions that violate fair lending or anti-discrimination regulations.
Can synthetic data help with rare events like insurance fraud or catastrophic claims?
Yes, synthetic data is particularly well suited to rare insurance events precisely because real datasets contain so few examples of them. For fraud detection and catastrophic claims modeling, synthetic data generation can artificially increase the representation of minority-class events, giving machine learning algorithms enough labeled examples to learn meaningful patterns rather than defaulting to the majority class.
Insurance fraud is a severe class imbalance problem. In nearly any operational claims dataset, fraudulent submissions represent a tiny fraction of total records, yet they account for a disproportionate share of economic losses. UK insurers alone detected over a billion pounds in fraudulent general insurance claims in a single recent year, according to industry fraud reporting. When a standard ML model trains on such imbalanced data, it learns to classify almost everything as legitimate because that maximizes overall accuracy, producing a model that is useless for the task it was built to perform.
Synthetic oversampling addresses this directly. SMOTE, the Synthetic Minority Over-sampling Technique, generates new minority-class instances by interpolating between existing fraudulent claims and their nearest neighbors. It is widely used as a baseline but can introduce noise in regions where fraudulent and legitimate claims overlap. More sophisticated approaches, including VAE-GAN pipelines, generate diverse minority-class samples that better capture the complex, non-linear structure of sophisticated fraud patterns. MAPFRE’s analytics team demonstrated this concretely: using synthetic data generation to address class imbalance in a homeowners’ fraud dataset produced measurable improvements in model performance compared to training on the original skewed data alone.
For catastrophic and emerging risk scenarios, synthetic data serves a different but equally important function. Insurers modeling hurricane, wildfire, or pandemic scenarios cannot wait for enough historical events to accumulate before training their models. Simulation engines can generate synthetic catastrophic claims with user-controlled characteristics, including policyholder profile, geographic exposure, and claim severity, enabling insurers to stress-test their models against scenarios that have not yet occurred. This is particularly valuable for climate-driven perils where historical loss data may no longer be representative of future exposure.
Does using synthetic data satisfy insurance data privacy regulations?
Synthetic data can satisfy insurance data privacy regulations, but only when it is generated and validated to a standard that regulators recognize as genuinely anonymized rather than merely pseudonymized. The answer depends on which jurisdiction applies, how the synthetic data was generated, and whether re-identification remains a realistic risk given the specific dataset and context.
US regulatory landscape
In the United States, insurance data privacy is governed by a combination of state-level laws, HIPAA for health-related data, and the Gramm-Leach-Bliley Act for financial data. Under HIPAA, de-identified information that meets specified criteria falls outside the scope of individually identifiable health information requirements. Under CCPA, synthetic data that qualifies as de-identified or aggregate information is explicitly excluded from the regulation’s scope. The NAIC’s AI Model Bulletin, adopted by the majority of US states as of 2026, requires insurers to maintain governance, documentation, and audit procedures for AI systems used in underwriting and claims, including documentation of the data used to train those systems.
EU and UK regulatory landscape
In the EU, GDPR applies unless the synthetic data is truly anonymized. The UK ICO’s 2025 anonymisation guidance takes a contextual approach, applying a “motivated intruder” standard: data is considered anonymous if re-identification is not reasonably likely given the specific circumstances, without requiring organizations to eliminate every theoretical re-identification risk. This is more permissive than the European Data Protection Board’s draft position, which treats pseudonymized data as personal data even in the hands of third parties. For EU operations, the stricter EDPB position may apply, and the regulatory situation remains in flux. The EU AI Act adds a further layer: life and health insurance AI systems, including underwriting and risk assessment models, are classified as high-risk under Annex III, triggering obligations around data governance, human oversight, and transparency that apply regardless of whether synthetic or real data is used.
A CAS Forum study published in 2026 confirmed that synthetic data generated with privacy-preserving algorithms can maintain compliance with both GDPR and CCPA, provided the generation process applies appropriate smoothing for small population subgroups that might otherwise be re-identifiable. The key practical point is that synthetic data supports compliance but does not replace it: the generation process itself must be documented, the privacy guarantees must be tested, and the resulting dataset must be evaluated in the context of the specific regulatory framework that governs its use.
For insurers navigating this landscape, the most defensible approach combines a technically rigorous generation method, formal privacy auditing including membership inference testing, and actuarial review to confirm that the synthetic data does not encode patterns that would produce discriminatory outcomes. If you want to see how this works end to end in a platform built for regulated industries, we are happy to walk you through it in a demo.
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