Insurance & Insurtech
AI That Understands Risk — And Regulation
Your competitors are automating claims in hours, not weeks. Fraud detection catches patterns humans miss. The question isn't whether to adopt AI — it's how to deploy it without triggering Solvency II and IDD compliance issues.
80% faster
Claims processing with AI triage
3x more
Fraud detected vs. rule-based systems
60% reduction
Underwriting cycle time
The Insurance AI Challenge
Why most insurtech AI projects stall at regulatory review
Regulatory Complexity
Solvency II, IDD, AI Act, national insurance supervisory laws. Each adds requirements for model governance, explainability, and consumer protection. Most AI vendors don't understand insurance regulation.
Explainability Requirements
Insurance decisions must be explainable to policyholders and regulators. Black-box models don't survive supervisory review. Every AI-assisted decision needs an audit trail.
Data Quality & Legacy Systems
Decades of claims data in disparate formats. Policy administration systems from the 1990s. Connecting AI to legacy infrastructure without disrupting operations is the real challenge.
Bias & Fairness in Pricing
AI-driven pricing and underwriting must avoid discriminatory outcomes. Protected characteristics, proxy variables, indirect discrimination — regulators are watching closely.
European Insurance Compliance
Beyond basic regulatory checkboxes
Insurance AI in Europe faces a triple compliance burden: financial regulation (Solvency II), distribution rules (IDD), and AI-specific requirements (AI Act). Building compliant systems requires understanding all three.
Solvency II
- Model governance and validation requirements
- Own Risk and Solvency Assessment (ORSA) for AI models
- Internal model approval process for AI-driven pricing
- Documentation and audit trail for supervisory review
IDD
- Product oversight and governance for AI-designed products
- Fair treatment of customers in AI-assisted advice
- Suitability and appropriateness assessments
- Conflict of interest management in automated recommendations
AI Act
- High-risk classification for insurance pricing/underwriting AI
- Mandatory conformity assessments for AI systems
- Transparency obligations for AI-assisted decisions
- Human oversight requirements for critical decisions
Regulatory Timeline
Now
Solvency II & IDD fully enforced
Aug 2025
AI Act: Prohibited practices
Aug 2026
AI Act: High-risk requirements
2027+
EIOPA AI supervisory guidelines
Insurance AI Use Cases
Where AI delivers measurable value in insurance
Claims Automation
AI triages incoming claims, extracts information from documents, assesses damage from photos, and routes complex cases to adjusters. Simple claims processed end-to-end.
80% of simple claims automated
Fraud Detection
Pattern recognition across claims networks, behavioral analysis, anomaly detection. AI spots fraud rings and staged claims that rule-based systems miss entirely.
3x fraud detection improvement
Intelligent Underwriting
AI augments underwriters with risk assessment, data enrichment, and portfolio analysis. Faster decisions on standard risks, more time for complex cases.
60% faster underwriting decisions
Customer Service
AI handles policy inquiries, coverage questions, claims status updates. Multilingual, 24/7, with seamless handoff to human agents for complex issues.
70% of routine queries handled automatically
Risk Assessment
Dynamic risk scoring using alternative data sources, IoT telemetry, and real-time market data. More accurate pricing without discriminatory proxies.
25% improvement in loss ratio prediction
Regulatory Reporting
Automate Solvency II reporting, EIOPA submissions, and national supervisory returns. Cross-reference data, ensure consistency, reduce manual effort.
50% reduction in reporting preparation time
The Explainability Question
If you can't explain it,
you can't underwrite with it.
Regulators require explainable AI decisions in insurance. Policyholders have the right to understand why their claim was denied or their premium increased. We build systems where AI provides recommendations with clear reasoning — and human underwriters make the final call. Every decision is traceable, every factor documented.
How It Works
Three-layer architecture for compliant insurance AI
Insurance AI Agents
Connect to Your Data
MCP Connectors
Teach How to Operate
Agent Skills
Foundation Layer
Claude Models
Haiku / Sonnet / Opus
Human Oversight
HITL Integration
Compliance Engine
Solvency II / IDD / AI Act
Audit Trail
Full Traceability
Kenaz builds all three layers — from MCP connectors that integrate with your policy admin and claims systems, through custom Agent Skills for your specific insurance workflows, to deployment with proper regulatory controls.
How We Help
Insurance AI infrastructure for European carriers and insurtechs
AI Safety & Compliance Audit
Not just checkboxes — actual compliance that survives supervisory review. Solvency II model governance, IDD consumer protection, AI Act risk assessment.
Learn more →Custom AI Agents
Purpose-built agents for claims processing, underwriting, fraud detection. Designed around your workflows, integrated with your systems.
Learn more →GDPR & Compliance
Insurance data is personal data. Proper legal basis, data minimization, policyholder rights, cross-border transfer controls for reinsurance.
Learn more →MCP Integration
Connect AI models to your policy admin, claims, and actuarial systems via Model Context Protocol. Secure, auditable, reversible.
Learn more →Deep Dive: AI in Insurance
Beyond claims automation — how AI is transforming underwriting, fraud detection, and regulatory compliance for European insurers.
Read the full analysis →FAQ
How does the AI Act affect insurance AI?
AI systems used for insurance pricing, underwriting, and claims assessment are classified as high-risk under the AI Act. This means mandatory conformity assessments, human oversight, transparency obligations, and detailed technical documentation. Most provisions apply from August 2026.
Can AI replace underwriters?
AI augments underwriters, not replaces them. AI handles data gathering, risk scoring, and routine decisions. Human underwriters focus on complex cases, relationship management, and final approval on significant risks. This is both best practice and a regulatory expectation.
How do you prevent bias in insurance AI?
We implement bias testing at every stage: training data audit, model validation, output monitoring. Protected characteristics and proxy variables are systematically identified and controlled. Regular fairness assessments ensure ongoing compliance with anti-discrimination requirements.
What about data residency for insurance data?
Insurance data stays in the EU/EEA. All processing happens on your infrastructure or Swiss/EU-hosted systems. Cross-border transfers for reinsurance follow GDPR Chapter V requirements with appropriate safeguards.
How long to implement insurance AI?
Depends on scope. Claims triage automation: 2-3 months. Full underwriting augmentation: 6-9 months. Fraud detection overlay: 3-4 months. We start with an assessment (2-3 weeks) to map your specific requirements and integration points.
Ready to Deploy Insurance AI?
The regulatory landscape is shifting. Insurers building compliant AI infrastructure now will have years of competitive advantage. Start with an assessment — we'll map your compliance requirements and implementation roadmap.
Request Insurance AI Assessment