Your insurance AI teams are ready. Their data isn't.
Take your insurance data further — generate, anonymize, and simulate diverse datasets for claims automation, fraud detection, underwriting models, and compliance testing. Starting from your own samples. Your data never has to leave.
What's blocking your insurance AI team?
Your claims and policyholder data is too sensitive to use.
Anonymize or transform it — structure intact, PII removed. HIPAA, GDPR, and CCPA compliant by design.
A — Anonymize, AugmentFraud, catastrophic events, and rare claims don't exist in sufficient volume.
Simulate the edge cases and rare-event scenarios your real data never captured — labeled and ready to evaluate on.
S — SimulateYour models need more diverse claims and policy data than you have.
Generate diverse, scaled datasets from your own samples — claims records, policy documents, underwriting submissions, actuarial data.
G — GenerateWhy insurance AI teams are blocked
| Challenge | Description |
|---|---|
| Privacy & Compliance | Strict regulations (HIPAA, GDPR, CCPA, state-level insurance laws) prevent easy use of sensitive customer data. Sharing data across teams or partners is risky and slow. |
| Data Scarcity & Bias | Insurance data is often fragmented, siloed, or limited to narrow demographics. Rare claims, fraud scenarios, and edge cases are especially hard to capture. |
| High Cost of Data Collection | Collecting new claims, fraud, or actuarial data is expensive and time-consuming, especially for rare-event scenarios. |
| Legacy Data Systems | Insurers often deal with unstructured, incomplete, or outdated datasets across claims, underwriting, and risk assessment systems. |
Works from your insurance data — adding diversity while preserving structure and constraints.
Diverse, distribution-tuned datasets. DataFramer starts from your real samples — claims records, policy documents, underwriting submissions, actuarial datasets — and extends them faithfully. Every output respects the schema, value ranges, regulatory constraints, and structural relationships your models depend on.
Any textual dataset. Multi-file submission packages, nested JSON claims records, high-token policy documents, structured actuarial tables, multi-format underwriting data — any complexity, any format.
How DataFramer solves it
Each solution starts from your own data — no random generation, no fabricated inputs.
When real data doesn't capture what your model needs to handle
Catastrophic weather events, large-scale fraud rings, rare claim types, and edge-case underwriting scenarios don't occur frequently enough in real data to evaluate or test reliably. DataFramer simulates these scenarios from your own data — preserving the structural and statistical properties of your real claims while generating the rare-event coverage your models are missing.
| Solution | Description |
|---|---|
| Synthetic Claim & Policy Data | Generate realistic, statistically accurate insurance claims, policyholder profiles, and actuarial datasets without exposing real PII. |
| Fraud & Risk Simulation | Create rare-event datasets for fraud detection, AML, and high-risk claims — improving recall/precision of fraud models. |
| Regulatory-Ready Data Sharing | Build privacy-preserving synthetic datasets that can be safely shared across internal teams, partners, and regulators. |
| Bias Mitigation & Model Fairness | Balance demographic gaps in underwriting and claims data to ensure fairer models. |
Use Cases
| Use Case | Description |
|---|---|
| Fraud Detection | Test and evaluate fraud models with abundant synthetic 'fraudulent claim' examples that don't exist in sufficient real-world volume |
| KYC & AML Compliance | Safely model customer onboarding and transaction data while maintaining regulatory compliance |
| Claims Analysis & Automation | Generate realistic claims datasets to test and evaluate AI agents that process claims faster and more accurately |
| Underwriting & Risk Models | Expand risk profiles with synthetic customers and edge cases to improve predictive accuracy |
| Customer Service & Virtual Agents | Use synthetic dialogue and case histories to evaluate chatbots and claims assistants without exposing real customer conversations |
| Multi-File Submission Package Generation | Generate and anonymize complex multi-file insurance submission packages — policy documents, claims records, medical attachments, supporting forms — with structure and constraints preserved across every file in the package. Test and evaluate submission processing AI without exposing real policyholder records. |
Built for insurance data regulation
DataFramer works within the regulatory constraints that govern insurance AI — not around them. Every dataset generated or anonymized preserves the structural fidelity your compliance and audit teams require, while removing what cannot leave your governance boundary.
Share data across teams and partners without compliance risk
Insurance AI teams often need to share data across internal teams, insurtech partners, or third-party vendors — but privacy regulations make that slow, expensive, and risky. DataFramer lets you work from your own data inside your own environment, producing outputs that are structurally faithful but contain no real customer information. No data use agreements, no governance violations.
Key Benefits
| Benefit | Description |
|---|---|
| Your data never has to leave | Generate and anonymize inside your own environment — cloud or on-prem. No compliance violations, no data movement risk. |
| Accurate outputs at scale | In-built revision loops enforce distribution accuracy and schema fidelity across high-volume outputs — not just sample checks. |
| Control your distributions | Analyze seed samples and define exactly what you need — fraud ratios, claim severity distributions, demographic splits, regional variance. Your output reflects your world, not a generic one. |
| Multi-file submission package support | Generate and anonymize complex multi-file insurance submissions — policy documents, claims records, supporting attachments — with structure and constraints preserved across files. |
| Share with partners safely | Give insurtechs and partners synthetic data that is structurally faithful but contains no real customer information — no NDAs, no governance risk. |
| State and federal compliant | HIPAA, GDPR, CCPA, and state insurance regulations covered by default. Audit-ready lineage built in. |
Common questions from insurance AI teams
Does DataFramer work with data that can't leave our regulatory boundary?
Yes. DataFramer deploys inside your own environment — cloud or on-prem. Your claims and policy data never has to move. Outputs are generated and anonymized within your own infrastructure.
How faithful are the outputs to our real underwriting, claims and policy data?
DataFramer starts from your own seed samples — it doesn't generate from scratch. Outputs preserve your schema, value distributions, and domain-specific constraints. Built-in distribution comparison lets you verify fidelity before anything touches your model.
What insurance data formats and document types does DataFramer support?
Any textual insurance dataset — nested JSON claims records, multi-file submission packages, high-token policy documents, structured actuarial tables, underwriting forms, dialogue logs from customer service interactions. Any format, any complexity.
Can we use DataFramer to share data with insurtech partners or internal teams safely?
Yes. DataFramer produces outputs that are structurally faithful to your real data but contain no real customer information — no PII, no sensitive policyholder records. No NDAs or data use agreements required.
How does DataFramer handle rare events like fraud or catastrophic claims?
DataFramer simulates rare-event scenarios from your own seed data — generating labeled fraud examples, catastrophic claim scenarios, and edge cases that don't exist in sufficient volume in your real data. Outputs reflect the structure and constraints of your real claims, not arbitrary values.
"This eliminates concerns about consumer privacy, allowing valuable insights to be drawn from sensitive data without compromising individual privacy."
See what DataFramer does with your data.
Send us a sample claims or policy dataset and we'll show you what's possible — diverse, faithful outputs in your format, your schema, your constraints.