Synthetic Data for Insurance AI

Industry Application

Synthetic DataInsurance

The Data Problem at the Heart of Insurance AI

Insurance is fundamentally a data business — carriers price risk, detect fraud, and settle claims by finding patterns in historical records. But the most valuable insurance data is also the most sensitive: medical diagnoses, accident reconstructions, financial disclosures, and property assessments. Privacy regulations (GDPR, CCPA, HIPAA, state insurance codes) and competitive confidentiality mean that insurers often cannot share or even fully utilize their own data assets across teams, partners, or model-training pipelines.

Synthetic data resolves this tension. By generating statistically faithful but non-identifiable replicas of policyholder records, claims histories, and loss events, insurers can train and validate AI models at scale without exposing real customer information. As of early 2026, synthetic data has moved from a compliance workaround to a core engineering primitive across underwriting, fraud, claims, and catastrophe modeling.

Fraud Detection: Training on Rare and Adversarial Signals

Insurance fraud costs U.S. carriers an estimated $308 billion annually, yet truly fraudulent claims are rare — typically 1–10% of submitted claims depending on line of business. This class imbalance is one of the hardest problems in supervised machine learning: models trained on real claims data see so few confirmed fraud examples that they fail to generalize. Synthetic data solves this directly.

Shift Technology, which serves over 100 insurance clients including AXA, Tokio Marine, and Covéa, uses generative models to synthesize realistic fraudulent claim patterns — staged accidents, inflated medical billing, organized ring activity — and augments real training sets to achieve balanced class distributions. Their fraud detection models, trained on hybrid real-and-synthetic datasets, have demonstrated false positive reductions of 75% compared to rules-based baselines. Similarly, Friss (now part of Duck Creek Technologies) generates synthetic anomaly patterns to continuously stress-test its fraud scoring models as new schemes emerge, without waiting for sufficient real-world fraud labels to accumulate.

Underwriting and Risk Pricing: Building Models Without Exposing Applicant Data

Actuarial and underwriting AI requires access to detailed policyholder attributes — age, health history, driving behavior, property characteristics — to learn how risk correlates with outcomes. Sharing this data across internal teams, with reinsurers, or with modeling vendors creates significant regulatory and reputational exposure.

Akur8, whose machine learning pricing platform is used by carriers including CNP Assurances, HDI, and Wakam, integrates synthetic data generation to let clients train pricing models on partner or third-party datasets without raw data transfer. The synthetic datasets preserve joint distributions between risk factors and loss ratios — the statistical signal an actuary cares about — while eliminating the ability to reconstruct any individual record. Planck, which specializes in commercial lines underwriting intelligence, uses synthetic augmentation to model underrepresented business classes where historical loss data is thin, improving rate adequacy for specialty risks like cyber liability and parametric weather products.

Catastrophe Modeling: Simulating Tail Events That Have Never Happened

Catastrophe models must price risks — pandemics, mega-earthquakes, cyberattacks at critical infrastructure — that have no adequate historical precedent. Reinsurers and ILS (insurance-linked securities) investors cannot rely solely on recorded events to understand their tail exposure. Synthetic scenario generation has become the standard approach.

Swiss Re's Magnum platform and Munich Re's NATHAN risk suite both incorporate stochastic event generation: synthetic storms, floods, and seismic sequences that are physically consistent but span a far wider range of intensities, tracks, and compounding interactions than the historical record allows. As climate change shifts loss distributions, these synthetic event sets are increasingly the primary basis for rate-on-line pricing in the cat bond market. Karen Clark & Company (KCC) and Verisk's AIR Worldwide division have similarly expanded their stochastic catalogs with generative model-derived scenarios, particularly for secondary perils like wildfire and convective storm that were historically underweighted in cat models.

State insurance regulators and the NAIC have increasingly scrutinized how carriers use AI in underwriting and claims — specifically whether AI models embed protected-class proxies. Demonstrating fairness and auditability requires sharing model training data with regulators and auditors, which creates a privacy conflict when that data contains sensitive policyholder records.

Mostly AI and Gretel.ai, the two dominant enterprise synthetic data platforms as of 2026, have both developed insurance-specific offerings that generate synthetic policyholder populations with documented privacy guarantees (differential privacy bounds, k-anonymity metrics) that satisfy regulatory audit requirements. Several Lloyd's of London syndicates have adopted these platforms to share synthetic loss datasets with Lloyd's central analytics team and with brokers, enabling market-wide AI development without violating the confidentiality of individual cedant data. The EU's AI Act, which came into full effect in 2026, creates further impetus: high-risk AI systems in insurance must maintain auditable training datasets, and synthetic records with formal privacy proofs are emerging as the compliance-friendly standard.

Applications & Use Cases

Fraud Detection Model Training

Synthetic fraudulent claims — staged accidents, inflated invoices, organized ring patterns — augment rare real fraud labels to create balanced training datasets. Shift Technology and Friss use this approach to train models that generalize to novel fraud schemes without waiting for real-world fraud volume to accumulate.

Actuarial Pricing and Rate Adequacy

Synthetic policyholder populations with realistic risk factor distributions let pricing teams train and backtest ML rating algorithms on statistically rich datasets without exposing applicant PII. Particularly valuable for specialty and surplus lines where thin historical data makes traditional credibility methods unreliable.

Catastrophe Scenario Generation

Stochastic synthetic event catalogs — spanning physically plausible but historically unobserved storm tracks, flood extents, and earthquake sequences — enable reinsurers and cat bond issuers to price tail risk beyond the limits of the historical record. Swiss Re, Munich Re, and AIR Worldwide all rely on generative simulation for their core cat model event sets.

Claims AI Development and Testing

Tractable and similar computer vision vendors train damage assessment models on synthetic imagery — photorealistic renderings of vehicle damage, roof degradation, and water intrusion at varying severities — generated via diffusion models. This eliminates the privacy and consent complexity of building large real-claims image datasets.

Regulatory Fairness Auditing

Synthetic policyholder datasets with controlled demographic distributions allow carriers to stress-test underwriting models for disparate impact without exposing real applicant records to auditors or regulators. Supports compliance with NAIC Model Bulletin requirements on AI fairness in personal lines.

Telematics and IoT Data Augmentation

Driving behavior datasets are dominated by normal operation; rare high-risk events (hard braking at highway speed, night driving in adverse weather) are underrepresented. Synthetic telematics sequences augment real sensor data to train more accurate usage-based insurance (UBI) risk models — an approach used by carriers building on platforms like Cambridge Mobile Telematics and Arity.

Key Players

Mostly AI — Enterprise synthetic data platform with deep insurance deployments; used by Lloyd's syndicates and European carriers to generate privacy-safe policyholder and claims datasets compliant with GDPR and the EU AI Act.
Gretel.ai — Synthetic data cloud with differential privacy guarantees; serves insurers and financial institutions requiring audit-ready documentation of training data provenance and privacy bounds.
Shift Technology — AI fraud detection platform serving 100+ insurers including AXA, Tokio Marine, and Covéa; uses synthetic fraud pattern generation to overcome class imbalance in claims training data.
Akur8 — Machine learning pricing platform for P&C and life carriers; integrates synthetic data pipelines to enable privacy-preserving model training across carrier and reinsurer partnerships.
Verisk / AIR Worldwide — The dominant catastrophe modeling vendor; stochastic synthetic event catalogs underpin cat bond pricing and reinsurance treaty terms for carriers worldwide.
Swiss Re (Magnum platform) — Reinsurance giant whose proprietary risk platform uses synthetic scenario generation for climate-adjusted catastrophe modeling and emerging risk quantification.
Tractable — Computer vision AI for claims used by Aviva, Admiral, and others; leverages synthetic damage imagery to train and continuously improve repair cost estimation models.
Syntho — Synthetic data startup with specific insurance and healthcare verticals; focuses on time-series fidelity for claims trajectories and actuarial cohort analysis.

Challenges & Considerations

Actuarial Regulatory Acceptance — State insurance departments and the NAIC have not yet established uniform standards for when synthetic training data is acceptable in filed rating algorithms. Carriers using synthetic data in rate filings face inconsistent review standards across jurisdictions, creating compliance uncertainty even when privacy benefits are clear.
Tail Risk Fidelity — The statistical value of synthetic data in insurance depends on accurate representation of extreme events — the 1-in-200-year flood, the catastrophic liability claim. Generative models trained on historical data tend to underrepresent tails, potentially producing synthetic datasets that understate the very risks insurers most need to quantify.
Proxy Discrimination and Fairness — Synthetic data generated from biased historical records inherits and can amplify those biases. If real underwriting data reflects historical redlining or discriminatory rating practices, synthetic data trained on it will encode the same patterns — making fairness auditing of synthetic datasets a regulatory and ethical imperative, not merely a technical nicety.
Reinsurance Data Governance — Cedants and reinsurers share loss data under complex contractual confidentiality arrangements. The legal status of synthetic derivatives of that data — whether sharing a synthetic version of ceded loss records constitutes a confidentiality breach — remains an unsettled contractual and legal question across most treaty structures.
Model Validation Complexity — Insurance regulators expect actuarial models to be validated against out-of-sample real experience. When training data is predominantly synthetic, traditional backtesting methodologies require adaptation; regulators and internal model validation teams lack standardized frameworks for assessing synthetic-data-trained model performance.
Claims Litigation Risk — If an AI claims decision trained on synthetic data is challenged in litigation, the insurer must be prepared to defend the representativeness and accuracy of that synthetic training data in discovery. The evidentiary standards for synthetic dataset documentation in insurance bad-faith litigation are largely untested as of 2026.