Rapid, Privacy-Safe Innovation in Risk, Claims, and Compliance
Unlock insurance innovation with privacy-safe synthetic data. Create realistic claims and policy datasets to power AI, cut compliance risks, and build faster, fairer models.
"This eliminates concerns about consumer privacy, allowing valuable insights to be drawn from sensitive data without compromising individual privacy"
Head of Data Science, $10B Insurance Company
Key Challenges
| Challenge | Description |
|---|---|
| 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. |
| 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. |
| 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| Legacy Data Systems | Insurers often deal with unstructured, incomplete, or outdated datasets across claims, underwriting, and risk assessment systems. |
Our Solutions
| 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 | Train 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 train 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 train chatbots and claims assistants without exposing real customer conversations |
Key Benefits
| Benefit | Description |
|---|---|
| Privacy-Safe Data | No exposure of customer PII while maintaining statistical fidelity |
| Accelerated AI Training | Cut data bottlenecks and deliver faster time-to-model |
| Reduced Costs | Avoid expensive recollection or annotation of rare-event data |
| Regulatory Compliance | Built-in privacy frameworks (HIPAA, GDPR, CCPA) |
| Fair & Representative Models | Mitigate bias in datasets to meet fairness requirements |
"We strive to start each relationship with establishing trust and building a long-term partnership. That is why, we offer a complimentary dataset to all our customers to help them get started."
Ready to Get Started?
Contact our team to learn how we can help your insurance organization develop AI systems that meet the highest standards.