AI Governance for Insurance
Deploy AI in insurance while ensuring fair underwriting, claims accuracy, and regulatory compliance. Expert managed services for safe, unbiased insurance AI.
Unique AI Challenges in Insurance
Insurance companies face critical fairness and accuracy requirements when deploying AI
Algorithmic Fairness
AI systems must avoid discriminatory outcomes in underwriting, pricing, and claims processing.
- Protected class discrimination
- Proxy variable detection
- Adverse action notices
Claims Accuracy
AI hallucinations in claims processing can lead to wrongful denials or fraudulent approvals.
- Claim validity verification
- Fraud detection accuracy
- Payment calculation errors
Regulatory Scrutiny
State insurance departments and federal regulators are increasingly focused on AI in insurance.
- State-by-state requirements
- Model documentation demands
- Explainability requirements
Insurance Compliance Frameworks
We ensure your AI systems meet all insurance regulatory requirements
US Insurance Regulations
- NAIC Model Laws
State insurance regulatory frameworks
- Fair Credit Reporting Act
Consumer credit information in underwriting
- State Privacy Laws
CCPA, CPRA, and emerging state regulations
- SOX (for public insurers)
Financial reporting and controls
International Standards
- EU AI Act
High-risk AI for insurance underwriting
- GDPR
EU customer data protection
- Solvency II (EU)
Risk management and reporting
- ISO 42001
AI management systems
Managed Services for Insurance
Specialized AI governance for insurance carriers and brokers
Fair Underwriting AI
Continuous bias monitoring and fairness testing for AI-driven underwriting systems.
- Disparate impact analysis
- Protected attribute monitoring
- Adverse action documentation
Claims AI Governance
Quality assurance for AI in claims processing, fraud detection, and payment calculations.
- Decision validation
- Fraud model monitoring
- Human oversight workflows
AI Explainability
Generate clear explanations for AI decisions to meet regulatory and consumer disclosure requirements.
- Decision reason codes
- Consumer-friendly explanations
- Regulatory documentation
Customer Data Protection
Secure handling of sensitive customer data in AI systems with privacy-preserving techniques.
- PII masking and encryption
- Access controls and logging
- Third-party risk management
State Exam Readiness
Maintain audit-ready documentation for state insurance department examinations and market conduct reviews.
- Model governance documentation
- Fairness testing evidence
- Continuous compliance tracking
Insurance Model Risk
Comprehensive model risk management for actuarial and pricing AI systems.
- Model validation and testing
- Performance monitoring
- Change management controls
Zero discrimination findings
Reduced processing errors
Streamlined state exams
Lower compliance overhead
Deploy Fair, Compliant AI in Your Insurance Business
Ensure fairness, accuracy, and regulatory compliance. Schedule a consultation to discuss your insurance AI needs.