Illustrative scenario
A generative AI-enabled claims and underwriting support solution helps insurance carriers reduce cycle times, improve loss ratio discipline, and free adjusters and underwriters from repetitive document review so they can focus on judgment-intensive decisions.
Visual overview · AI and Data
An illustrative diagram drawn from this scenario's architecture and phases. Choose a phase to see what it works on.
Phases
Structured data comes from the policy administration and claims systems through integration middleware. Choose a phase to highlight the parts of the solution it works on.
Discovery & Data Assessment Typically 4-6 weeks
Data Foundation & Governance Design Typically 6-8 weeks
Pilot Build & Model Grounding Typically 8-10 weeks
Pilot Rollout & Validation Typically 6-8 weeks
Scale & Continuous Improvement Typically Ongoing
The approach aims for faster first-notice-of-loss triage, more consistent underwriting submissions, and reduced manual data entry across the policy lifecycle. The intended outcome is a claims and underwriting operation that scales with volume without a proportional increase in headcount.
Insurance carriers are under sustained pressure from rising claims volumes, catastrophe-driven surges, and customer expectations shaped by digital experiences in other industries. At the same time, underwriting teams face an influx of unstructured submission data — broker emails, PDFs, loss runs, inspection reports, and images — that traditional rules engines were never designed to parse across the estate.
Regulatory scrutiny of automated decisioning (including state-level AI use disclosure requirements and NAIC model governance guidance) means any AI-enabled process must be explainable, auditable, and bias-tested, not just fast. Compounding this, many carriers still run claims and underwriting on a patchwork of legacy policy administration systems, document repositories, and point systems that do not share a common data layer.
This fragmentation slows adjuster workflows, creates inconsistent underwriting decisions across regions and books of business, and limits the carrier's ability to use its own historical claims and underwriting data as a strategic asset.
Claims adjusters and underwriters spend a disproportionate share of their time on manual document review, data extraction, and cross-system lookups rather than on adjudication and risk judgment. First-notice-of-loss intake is inconsistent, submission triage is slow, and underwriters frequently make decisions without a complete, synthesized view of prior claims history, exposure data, and policy documents.
This drives up loss adjustment expense, extends cycle times, increases leakage risk, and creates inconsistent underwriting quality across teams and geographies.
Claims are triaged, summarized, and routed automatically within minutes of intake, with adjusters presented with a synthesized case file rather than a stack of raw documents. Underwriters receive AI-generated submission summaries, risk flags, and comparable-policy context that let them focus on pricing and appetite decisions rather than data assembly.
Every AI-generated recommendation is traceable to source documents and model rationale, supporting internal audit and regulatory review, and the carrier's own historical data becomes a continuously improving asset that sharpens future decisioning.
In this scenario, the delivery team designs and delivers a generative AI layer that sits on top of the carrier's existing claims and policy administration systems rather than replacing them, ingesting structured and unstructured data (documents, emails, images, prior claims) into a governed data platform. Large language model-based services summarize claims files, extract structured data from unstructured submissions, draft adjuster and underwriter notes, and surface anomalies or potential fraud indicators for human review.
A human-in-the-loop review workflow, model governance framework, and audit trail are built in from day one so every AI-assisted decision remains explainable and reviewable by underwriting and compliance leadership.
The solution is organized in four layers. An ingestion layer pulls structured data from policy administration and claims systems alongside unstructured inputs (PDFs, scanned forms, emails, images) using document intelligence services.
A curated data layer harmonizes this information into a governed claims-and-underwriting data model with lineage tracking, so every downstream output can be traced back to its source record. A model layer combines retrieval-augmented generation (grounding generative AI outputs in the carrier's own policy documents and claims history) with predictive models for triage prioritization and anomaly flagging.
A presentation and governance layer delivers summaries, drafts, and risk flags into adjuster and underwriter workflows through existing tools, with role-based access, full audit logging, and human sign-off gates before any customer-facing or pricing action is taken.
Typically 4-6 weeks
Typically 6-8 weeks
Typically 8-10 weeks
Typically 6-8 weeks
Typically Ongoing
Solution design accounts for state-level AI use disclosure and model governance expectations (aligned with NAIC AI model governance guidance), is designed to keep explainability and audit trails for every AI-assisted recommendation, and enforces human-in-the-loop sign-off before any customer-facing or pricing decision.
We do not certify or audit. Decisions on compliance remain with your organization.
The service and industry pages behind this scenario, and other scenarios like it.
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