Trust intelligence for insurance
From policyholder concierge to fraud, waste, and abuse detection. AI that works at scale - not just in demos.
Insurance AI is different
Insurance isn't just another regulated industry. Four characteristics make AI deployment uniquely challenging.
Fragmented regulatory landscape
No single regulator. In the US, 50 state insurance departments with different rules. In the EU, Solvency II plus national supervisors. In Asia-Pacific, MAS, APRA, IRDAI — each with distinct AI expectations. What's compliant in one jurisdiction may not be in another.
Adversarial environment
Fraud evolves. Fraudsters study your detection methods and adapt. Static models become obsolete. The system must evolve faster than the adversaries.
Litigation exposure
Every decision is discoverable. Bad faith claims. Class actions. Regulatory enforcement. Your AI's reasoning chain may end up in court - it better be defensible.
Volume economics
Millions of claims. Pennies of margin per claim. At scale, a 1% cost difference is millions of dollars. AI must be efficient, not just accurate.
The POC-to-Production gap
You proved the concept. The demo impressed leadership. Then you tried to scale it — and hit a wall.
Costs exploded
POC: $500/month for demos.
Production: $30,000-50,000/month for real volume.
Finance is asking questions you can't answer.
Latency killed UX
POC: "Wow, it thinks!"
Production: "Why is this so slow?"
Agents reasoning for 3-5 seconds per request. SLAs missed.
Reliability wasn't enterprise-grade
POC: "It works 90% of the time."
Production: "90% isn't good enough."
Hallucinations. Edge cases everywhere.
Ops couldn't manage it
No observability. No governance. No audit trail.
When it breaks, nobody knows why.
Why pure agentic AI breaks insurance math
Three architectural principles that separate production-grade insurance AI from expensive experiments.
Deterministic consistency
Insurance requires identical inputs to produce identical outputs. Same claim, same decision, every time. LLMs are probabilistic by design. Production insurance AI needs deterministic layers for consistency-critical decisions, with AI reserved for judgment calls.
Statistical precision
Actuarial models require precise probability estimates. "Probably fraudulent" isn't actionable. AI confidence must be calibrated and quantified. False positive rates and false negative rates must be measurable and manageable.
Focused agent attention
Give an agent everything and it will use nothing well. Insurance AI agents need narrow, well-defined tasks with specific data access. The "throw everything at a frontier model" approach produces inconsistent, expensive, slow results.
Adversarial robustness
Fraudsters will probe your AI for weaknesses. Every public-facing AI interface is an attack surface. Production AI must be hardened against adversarial inputs, prompt injection, and manipulation attempts.
Fraud, Waste & Abuse Detection — Done Right
Not "use less AI" — use AI where it matters. The Trust Intelligence Platform routes each claim to the cheapest processing layer that can handle it.
Click any level to explore details
Deterministic rules catch known fraud patterns instantly. No AI needed for obvious cases.
- Duplicate claim submissions
- Claims exceeding policy limits
- Velocity checks (too many claims too fast)
- Known bad actor lists
- Invalid provider/procedure combinations
Traditional ML models detect statistical anomalies and patterns that rules can't express.
- Unusual billing patterns for provider type
- Geographic anomalies
- Procedure frequency outliers
- Network analysis (provider rings)
- Temporal pattern anomalies
LLM agent reasons about complex cases, pulling context from multiple sources to make a decision.
- Medical necessity evaluation
- Complex documentation review
- Multi-claim pattern analysis
- Provider behavior reasoning
- Policy interpretation edge cases
Adversarial multi-agent debate for the highest-stakes decisions. Three agents argue it out.
- Prosecutor: Argues for fraud designation, finds evidence
- Defense: Argues for legitimacy, finds counter-evidence
- Judge: Weighs arguments, makes final determination
Five Gates to Production
A structured framework for moving insurance AI from POC to production. Each gate has specific criteria that must be met before proceeding.
Reliability baseline
Does the AI work consistently? Accuracy metrics established. Edge cases documented. Failure modes understood. Hallucination rate measured. You can't improve what you can't measure. Eval provides systematic testing.
Economics validation
Does the math work at scale? Cost per decision modeled. Volume projections validated. ROI calculated with realistic assumptions. The cascade architecture that makes AI affordable, not just accurate.
Compliance certification
Can you defend this to regulators? Fair lending and fairness testing complete. Adverse action explanations generated. Audit trails sufficient. Jurisdiction-by-jurisdiction compliance review.
Operational readiness
Can ops run this? Monitoring dashboards deployed. Alert thresholds set. Escalation procedures documented. Team trained. Guardian provides production observability.
Continuous improvement
How does it get better over time? Feedback loops established. Model update procedures. A/B testing framework. The system improves itself through APLS pattern extraction.
Production-grade operations
The cascade alone isn't enough. Production requires continuous monitoring, observability, and self-improvement. The numbers: 94% detection accuracy. $2,300/month for 1M claims. 86% cost reduction vs pure agentic. That's the difference between a science experiment and a business case.
Continuous Monitoring
Track detection accuracy over time. Detect model drift as fraud patterns evolve. Alert when reliability degrades. Know before customers do. Guardian monitors around the clock.
Full Observability
What the cascade decided and why. Which layer handled which claims. Cost attribution by claim type. Audit trail for compliance and litigation. AgentOps provides the visibility.
Self-Improvement (APLS)
When expensive layers catch fraud that cheap layers missed, the system extracts patterns and proposes new rules. Over time, detection migrates from $0.05 to $0.0001. The system gets cheaper and better simultaneously.
Adversarial Testing (Red Queen)
Genetic algorithm continuously probes the system. Strongest "attacks" train the cascade. The system evolves against emerging fraud patterns before they become incidents.
Beyond fraud detection
The Trust Intelligence Platform enables reliable AI across the insurance value chain — from policyholder service to underwriting to claims.
AI Concierge for Policyholders
24/7 AI assistant that knows your policy, answers questions instantly, helps file claims. Guardian monitors for hallucination. Steer enforces compliance language. Full audit trail.
Products: Guardian, Steer, AgentOps, Context Engine
AI-Assisted Underwriting
Synthesize data from dozens of sources — medical records, financial data, third-party scores. Context Engine provides contextual integration. Guardian tracks model accuracy. Full reasoning capture for explainability.
Products: Context Engine, Guardian, AgentOps
Claims Automation
Trust Cascade for claims adjudication. Simple claims processed automatically. Complex claims routed to appropriate level. Full audit trail for every decision.
Products: Orchestrate, Guardian, Context Engine
FWA cascade: POC to production in 10 weeks
A top-10 P&C insurer was spending $45K/month on pure agentic fraud detection with 78% accuracy. Workers' comp claims were the worst — high volume, complex patterns, expensive false positives. They needed a better architecture.
Audited existing FWA detection pipeline. Mapped claim volume by type and complexity. Identified that 68% of claims were being sent to expensive AI agents unnecessarily.
Deployed Trust Cascade with 4 levels for workers' comp claims. Integrated with Guidewire ClaimCenter. Built deterministic rules for known fraud patterns. Calibrated ML models against 3 years of historical data.
Tuned escalation thresholds. Tested against 10,000 historical fraud cases. Validated false positive rates with SIU team. Refined the Multi-Agent Tribunal for high-stakes cases.
Trained SIU team on monitoring dashboards. Established alert thresholds and escalation procedures. Team was self-sufficient by end of week 10.
Related reading
Deep dives from our team on the topics that matter most.
Built for insurance regulation
Our solutions align with regulatory requirements from day one.
NAIC & Solvency II
Aligned with NAIC's Model Bulletin on AI in insurance and EU Solvency II requirements. Transparency, fairness, and governance requirements built into every deployment.
Multi-jurisdictional compliance
Compliant with regulatory requirements across jurisdictions — US state insurance departments, EU national supervisors, MAS, APRA, and others. Documentation ready for regulatory examination worldwide.
Fairness & Anti-Discrimination
Bias testing and fairness monitoring built in. Continuous monitoring ensures AI decisions don't inadvertently discriminate against protected groups — meeting requirements from fair lending laws to EU AI Act equity provisions.
Data Privacy & Security
SOC 2 compliant. GDPR, CCPA, PDPA, and regional data privacy requirements supported. Full audit trail for every AI decision.
Insurance Data Intelligence
Claims, policies, and customer data spread across dozens of systems. Our Data Intelligence capabilities unify it for AI consumption.
Insurance Data Engine
Deploy on Google Cloud, AWS, or Azure. Native connectors for Guidewire, Duck Creek, and major policy admin systems. SOC 2 compliant with full audit trail.
ETL-C for Insurance Data
Context-first processing for claims, policies, and customer data. Preserve relationships between entities that FWA detection and underwriting AI need to reason correctly.
SARP for Claim Volume
Agent-ready data platform built for high-volume claims processing. AI agents can query millions of claims without latency or hallucination issues.
Start your insurance AI journey
FWA Assessment
$30K
2-3 weeks. Current detection audit. Cost and accuracy analysis. Cascade design recommendations. Business case modeling.
FWA Pilot
$75K
6-8 weeks. Implement cascade for one claim type. Demonstrate detection rate and cost savings. Prove the model before full investment.
FWA Production Platform
$300K+
4-6 months. Complete cascade implementation. Integration with claims systems. Observability and governance. Team training and enablement.
Customer success stories
See how organizations like yours have deployed AI with trust.
Life Insurance: Fraud, Waste & Abuse Detection
Fraud detection needed AI accuracy at affordable cost. Traditional approaches either missed fraud or broke the budget.
Life Insurance: Policyholder AI Concierge
AI concierge POC succeeded but production revealed reliability, compliance, and escalation failures.
AI you can defend in court
Every claim decision documented. Every detection explainable. Every dollar accounted for.