Compliance-aware AI automation for insurtech and insurance ops
Claims intake, underwriting research, customer communication AI with human-in-the-loop and audit trails. $3,000/mo retainer.
Who this is for
Insurtech ops, broker-tech ops lead, or insurance group digital officer where claims processing is slow, underwriting research is manual, and customer comms are repetitive.
The pain today
- Claims intake takes days to structure for assessment
- Underwriting research eats underwriter time on every case
- Customer communications (renewal, claims status) are slow
- Regulatory review would catch anything lacking audit trails
- Previous AI experiments could not meet compliance bar
The outcome you get
- AI automations for insurance ops on $3,000/mo retainer
- Claims intake structuring from documents and calls
- Underwriting research summarised from policy and case data
- Customer comms personalised with brand-voice preservation
- Audit trails on every AI decision for regulatory review
Where AI pays back in insurance ops
Three places deliver clear ROI. Claims intake — LLM reads claims submissions (forms, emails, documents, recorded calls) and extracts structured data for assessor review. Cuts intake time 50 to 70 percent. Underwriting research — LLM pulls relevant policy history, case precedents, and risk factors from internal knowledge to inform underwriter decisions. Underwriter makes final decision. Customer communications — renewal notices, claims updates, policy changes drafted in brand voice for agent review. Each preserves regulatory human-in-the-loop while removing typing work.
Compliance-aware automation
Insurance is regulated. AI compliance rules: human-in-the-loop on any decision affecting coverage, pricing, or claim outcome. Audit trails on every AI input, output, and human override. Explainability for any AI-informed decision affecting policyholders. Fair-insurance compliance (no protected-class discrimination in underwriting). Violating any of these creates regulatory exposure. I build to these rules from day one. No AI-autonomous coverage decisions, no AI-autonomous claim denials.
Typical stack (RAG, structured extraction, workflow engines)
LLMs: Anthropic Claude for complex reasoning (claims narrative analysis, underwriting review). OpenAI for structured extraction. Vector DB for RAG over policy documents, case history, and regulatory guidance. Workflow engines: Temporal or Inngest for multi-step claims processing with human approval steps. Monitoring: custom dashboards for AI decision patterns, accuracy, cost. For insurtechs with specific regulatory posture, self-hosted open-source models for data that cannot leave infrastructure.
Pricing and engagement model
$3,000/mo retainer. Covers AI integration, compliance-aware architecture, RAG setup, monitoring, iteration. 14-day money-back guarantee. Cancel anytime. 100 percent code ownership under Work Made for Hire. NDA standard. US LLC invoicing — IRS/IR35 safe. LLM and infrastructure costs pass through. For insurtechs handling regulated health or financial data, infrastructure tier runs higher than general AI ($500 to $3,000/month).
Case: bolttech — integration-heavy fintech/insurance playbook
At bolttech, the $1B+ unicorn backed by Tokio Marine and MetLife, I led the Payment Service integrating 40+ providers across Asia and Europe with 99.9 percent uptime and zero post-launch critical bugs. Stack: NestJS, React, MongoDB, Redis, TypeScript. For insurtech AI, the same discipline applies — reliability at unicorn scale, audit trails baked in, regulatory review-ready. The integration patterns for payment providers map directly to carrier integrations and claims processors. Same governance rigor applies to AI automation in insurance.
When full manual review remains mandatory
Some insurance workflows must stay fully manual. Final claim approval or denial decisions. Underwriting decisions for high-value policies. Coverage determination in disputed cases. Anything where a regulator could challenge the decision basis. I help draw the line in the first month. Within the line, AI reduces time dramatically. Outside the line, expert humans do the work as they always have. The line is hard, not fuzzy — violations create regulatory exposure that can end businesses.
Recent proof
A comparable engagement, delivered and documented.
Unified payment orchestration across Asia and Europe
Delivered the payment orchestration platform at bolttech, a $1B+ unicorn, with 40+ integrations across multiple regions.
Frequently asked questions
The questions prospects ask before they book.
- How do you handle fair-insurance compliance?
- Protected classes (race, religion, national origin, sex, age in some contexts) cannot inform underwriting or pricing. AI systems explicitly exclude protected-class inputs and protected-class-correlated inputs (zip code in some jurisdictions as race-correlated, for example). Regular bias audits on AI-informed decisions catch subtle discrimination. For insurtechs in strict-compliance markets (California, New York), additional auditing layers. Fair-insurance compliance is foundational, not optional.
- What about PII handling?
- PII minimisation in prompts — only what the task needs. LLM providers with DPAs and no-training terms. For highly sensitive data (SSNs, full financial records, health-related underwriting data), self-hosted open-source models or dedicated services. Audit logs track PII appearance in prompts with redaction on shared views. Documented in privacy posture. This is baseline for insurtech AI.
- How do evals work?
- Golden-set evals for each AI task with hand-crafted test cases and expected outputs. Regression testing on every prompt change. Production sampling with actuarial or compliance review. For high-stakes outputs (claim assessments, underwriting drafts), 100 percent human review before production actions. Eval infrastructure documented as part of compliance posture for regulator review.
- Which LLM do you use for insurance?
- Anthropic Claude as primary for complex reasoning (claims narrative analysis, underwriting review). OpenAI (through Azure for HIPAA coverage where needed) for structured extraction. For data that cannot leave in-region infrastructure, self-hosted open-source (Llama via vLLM). Model choice per task based on capability, cost, and data sensitivity. Documented in architecture decisions.
- What about audit trails for regulators?
- Every AI decision logs: inputs (PII-redacted in shared views), output, model used, timestamp, human override if applicable. For claim-adjacent decisions, full trail preserved for the retention period required by state or country regulation (typically 7+ years for insurance). For regulator review, trail is queryable and reproducible — the same inputs produce the same output (modulo model version). Audit trail architecture documented from day one.
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