Insurers use synthetic data for fraud detection modeling by generating artificial datasets that replicate the statistical patterns of real claims, including rare fraudulent ones, without exposing sensitive policyholder information. This approach directly solves two problems that have historically crippled fraud models: severe class imbalance in real datasets and strict privacy restrictions that limit data sharing. The sections below unpack each dimension of how this works, from data generation to production performance.
What makes fraud detection models so hard to train with real data?
Training fraud detection models on real insurance data is hard primarily because genuine fraud is rare, labeled fraud examples are scarce, and privacy rules make sharing what little data exists nearly impossible. These three constraints compound each other, leaving data science teams with datasets that are too small, too imbalanced, and too restricted to build reliable models from alone.
The class imbalance problem is the most technically damaging. Fraud rates across insurance lines typically sit somewhere between a fraction of a percent and a few percent of all claims. When a machine learning model sees thousands of legitimate claims for every fraudulent one, it learns to predict “not fraud” almost universally and still achieves high accuracy on paper, while completely failing at the task that actually matters. The model has simply never seen enough examples of what fraud looks like to recognize it.
The data scarcity problem runs alongside imbalance. Even when insurers have historical fraud cases, those records are often locked behind internal privacy controls. Sharing limitations within insurers mean that claims data held by one department frequently cannot be passed to a data science team in another, let alone shared with an external vendor or research partner. This fragmentation keeps labeled fraud datasets small even at large carriers.
Finally, the industry has historically relied on rule-based systems and expert judgment rather than machine learning. Only a small fraction of carriers currently use AI as part of their fraud detection process, which means the feedback loops needed to generate well-labeled training data are still being built across most of the market. The result is a persistent shortage of the kind of high-quality, annotated fraud examples that modern ML models require.
How does synthetic data replicate fraud patterns without using real claims?
Synthetic data replicates fraud patterns by learning the statistical structure of real claims, including the relationships between variables that distinguish fraudulent from legitimate behavior, and then generating new, artificial records that preserve those patterns without containing any actual policyholder information. The generated data is not anonymized real data; it is entirely new data that never existed.
The most widely adopted generation technique for insurance fraud data is the Generative Adversarial Network, or GAN. A GAN works by pitting two neural networks against each other: a generator that produces synthetic records and a discriminator that tries to identify which records are fake. Through repeated competition, the generator learns to produce data that is statistically indistinguishable from real claims. A specialized variant called CTGAN is particularly well suited to insurance tabular data because it handles the mixed distributions and categorical columns typical of claims records, including the severely underrepresented fraud class.
Beyond GANs, simulation engines offer a more controllable approach. Rather than learning from existing data, a simulation engine lets researchers specify parameters directly, the number of policyholders, the desired fraud rate, the strength of individual risk features, and generates a dataset to those specifications. This is especially useful for benchmarking detection algorithms across different fraud scenarios, because the ground truth is known by construction.
Emerging multimodal frameworks extend synthetic generation beyond structured tables. Synthetic multimodal fraud research has demonstrated that it is possible to generate realistic agent-customer dialogue transcripts and audio recordings that simulate First Notice of Loss conditions, enabling fraud detection training on voice and text data without ever touching a real customer interaction. This matters as insurers increasingly look to detect fraud signals in unstructured communication channels.
What types of insurance fraud can synthetic data help detect?
Synthetic data for insurance fraud detection can be applied across all major lines, auto, health, property and casualty, and life, as well as emerging fraud categories like synthetic identity fraud and AI-generated document fraud. The technique is not line-specific; it addresses the underlying data problem that affects every fraud detection model regardless of what is being insured.
Traditional claim fraud by line
Auto insurance fraud has the longest track record of synthetic data application, with classification algorithms trained on artificially generated automobile claims datasets now well documented in the research literature. Health insurance fraud, which carries the largest annual loss burden across all lines, has seen CTGAN architectures adopted specifically because health claims data is both highly sensitive and severely class-imbalanced. Property and casualty fraud detection has similarly benefited, with network-based approaches using synthetic social graph data to model the relationships between claimants, repair shops, and attorneys that often signal organized fraud rings.
Emerging and AI-generated fraud
Two newer fraud categories are pushing the boundaries of what synthetic training data needs to capture. Synthetic identity fraud, where fraudsters construct fictitious persons from a mix of real and fabricated data elements, then use those identities to purchase policies and file claims, grew dramatically in 2025. Detection models need training examples of these fabricated identity patterns, which real claims data rarely contains in sufficient volume. Separately, AI-generated media fraud, where claimants submit altered images, fabricated documents, or synthetic medical reports, now affects a significant share of claims according to industry estimates. Training a model to detect this category requires exposure to realistic examples of manipulated artifacts, which synthetic generation can provide without sourcing real fraudulent documents.
How do insurers validate that synthetic fraud data is accurate enough to use?
Insurers validate synthetic fraud data across three dimensions, fidelity, utility, and privacy, and the industry standard for utility validation is the Train on Synthetic, Test on Real methodology, where a model trained entirely on synthetic data is evaluated against held-out real claims. Passing all three dimensions is necessary, but research has shown it is not always sufficient.
Fidelity measures how closely the statistical distributions of the synthetic dataset match the original. Common metrics include KL Divergence and the Kolmogorov-Smirnov test, which compare individual feature distributions between real and synthetic records. The important limitation here is that these metrics assess features in isolation. They do not capture whether the correlations between features, for example, the relationship between claim timing, repair cost, and claimant history that distinguishes fraud, have been preserved. A dataset can pass every fidelity test and still produce a model that misses the fraud signal entirely.
Utility validation through TSTR is more direct: train a fraud detection model on synthetic data, then measure its performance on real data the model has never seen. This approach produces interpretable scores like AUC and F1, but documented production failures reveal a critical blind spot. TSTR scores are averages, and averages can mask failure in the tails. A model might achieve strong overall performance while completely failing on the rarest, highest-value fraud cases, precisely the cases that matter most. Breaking TSTR results down by transaction decile, rather than relying on a single aggregate score, is now considered a best practice for catching this tail loss problem before deployment.
Privacy validation assesses whether the synthetic dataset could be used to re-identify real policyholders through membership inference attacks. Even data that contains no direct personal information can leak signals about individuals who were in the training set. Insurers operating under GDPR, HIPAA, or state privacy statutes need to quantify this risk, not just assume it is zero because the data is labeled synthetic.
What’s the difference between augmenting real data and replacing it with synthetic data?
Augmentation means adding synthetic records to an existing real dataset to improve its balance or coverage, while replacement means building a training set from synthetic data alone because real data cannot be used at all. Most production fraud detection pipelines use augmentation; full replacement is reserved for situations where real data is legally or operationally unavailable.
In the augmentation approach, the real training data remains the foundation. Synthetic records, typically oversampling the minority fraud class, are added to give the model more examples of fraudulent behavior to learn from. The test set always uses real data only, so performance evaluation reflects how the model will behave in the real world. MAPFRE’s homeowners fraud detection model followed exactly this pattern: synthetic data was combined with real claims for training, while evaluation used only historical real claims. This is the industry standard pipeline because it preserves the integrity of the performance signal.
Full replacement is driven by privacy constraints that make real data completely inaccessible. When a team cannot use real claims data at all, because of regulatory restrictions, cross-border data transfer rules, or the sensitivity of the specific data elements involved, synthetic data generated from a model trained elsewhere can serve as a stand-in. Research has shown that models trained exclusively on synthetic fraud data can achieve strong recall on real test data, though results vary significantly with the quality of the generative model and the complexity of the fraud patterns involved.
One practical advantage that applies to both approaches is that synthetic data can be shared freely across teams and with external vendors without privacy risk. This makes it possible to involve third-party model developers, academic researchers, or technology partners in fraud detection work that would otherwise be impossible to collaborate on. The privacy-safe data sharing benefit is often as valuable as the class balancing benefit, particularly for insurers working with specialized fraud analytics vendors.
Are there regulatory or compliance risks to using synthetic data in insurance?
Yes, using synthetic data in insurance fraud detection carries real regulatory risks, and insurers cannot treat synthetic data as a compliance shortcut. The key risks fall into three categories: accountability for the models trained on synthetic data, bias inherited from the real data used to generate it, and explainability requirements that complex generative models may struggle to satisfy.
Accountability and vendor responsibility
Regulators are clear that insurers bear full responsibility for any model they deploy, regardless of whether the underlying data or the model itself came from a third-party vendor. This means that if a fraud detection model trained on synthetic data produces discriminatory outcomes, the insurer, not the synthetic data vendor, is accountable. State-level frameworks, including New York’s DFS guidance and Colorado’s anti-discrimination statute, require insurers to demonstrate that AI systems do not proxy for protected classes and to maintain documentation sufficient for regulatory review.
Bias propagation and explainability
Synthetic data inherits the biases present in the real data used to train the generative model. If historical claims data reflects patterns of unequal treatment, whether in claim investigation rates, settlement amounts, or fraud flagging, a GAN trained on that data will reproduce those patterns in its synthetic output. A fraud detection model trained on biased synthetic data can then systematically flag certain demographic groups at higher rates, creating disparate impact liability even when no protected characteristic was explicitly included as a feature.
Explainability is a separate but related challenge. Regulators increasingly require that AI models used in insurance decisions be interpretable, that an insurer can explain why a claim was flagged. Complex GAN architectures are not inherently interpretable, and the synthetic generation process adds a layer of indirection that can make it harder to trace a model’s behavior back to specific data patterns. Insurers should document the synthetic data generation process, the validation methodology, and the downstream model’s decision logic as part of any compliance framework.
How do fraud detection models trained on synthetic data perform in production?
Fraud detection models trained with synthetic data augmentation generally outperform models trained on imbalanced real data alone, with documented improvements in AUC, F1 score, and recall across multiple production and near-production deployments. However, production performance varies significantly with the quality of the synthetic data, the augmentation strategy used, and how rigorously the model is validated before deployment.
The clearest evidence comes from cases where synthetic augmentation directly addressed class imbalance. Research has shown AUC improvements from around 0.89 to 0.94 and meaningful F1 score gains when optimized synthetic training data is used, improvements that translate into fewer missed fraud cases and fewer false positives that waste investigator time. Automated claims processing systems incorporating synthetic data-trained models reduced fraudulent claims by a meaningful margin in 2025, according to industry reporting. MAPFRE’s experience with auto and homeowners fraud detection illustrates the pattern at the carrier level: synthetic augmentation improved both accuracy and efficiency, and positive results in one line drove expansion to another.
The most important production caveat is the tail loss problem described in the validation section. A model can achieve strong aggregate performance metrics while failing completely on the rarest fraud patterns, the high-value edge cases that often represent the most significant financial exposure. This is not a theoretical risk; there are documented cases of models that passed all pre-deployment validation tests and then failed to detect entire categories of transactions in production. Continuous monitoring after deployment, with performance broken down by claim type and value segment rather than measured only in aggregate, is essential for catching this kind of degradation early.
The broader market trajectory reflects growing confidence in the approach. The global insurance fraud detection market is expanding rapidly, driven in large part by adoption of AI and ML techniques that depend on high-quality training data, exactly the gap that synthetic data addresses. For insurers evaluating whether to invest in synthetic data capabilities, the production evidence is encouraging, provided the validation and monitoring infrastructure is in place to catch the failure modes that aggregate metrics can hide.
If you are exploring how synthetic data could strengthen your fraud detection pipeline, we would love to show you what is possible. Book a demo and see how we approach synthetic data generation for insurance use cases, from class balancing to privacy-safe collaboration with your modeling teams.
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