PLAYBOOK ATLASPlaybook Atlas

Insurance

AI in insurance: explore 35 applications.

Bring claim and underwriting evidence into clear review, referral and servicing workflows.

Explore intake, document review, fraud referrals and claim coordination, with the insurance line and decision stage made explicit.

Plan your first AI move See the 35 applications

Where the 35 applications sit

Explore the domains
  1. 17Claims Management
  2. 6Financial Management
  3. 5Underwriting
  4. 4Regulatory Compliance
  5. 3Customer Acquisition

01Why AI now

Why insurance is moving on AI, in three figures.

Each figure links to its source.

  1. Global insurtech market: $152.4B by 2030

    Grand View Research projects the global insurtech market will reach $152.43 billion by 2030, with machine learning included among its technology segments.

    Source: MarketResearch.com

  2. Aviva liability assessment: 23 days faster

    McKinsey reports that Aviva's claims AI reduced average liability-assessment time for complex cases by 23 days.

    Source: KPMG South Africa

  3. U.S. insurance fraud costs $308.6B yearly

    The Coalition estimates that insurance fraud takes at least $308.6 billion from American consumers every year.

    Source: National Association of Insurance Commissioners

How to evaluate a first project

Choose one insurance line and claim or underwriting stage. Compare the proposed workflow with adjuster or underwriter review, including ambiguous documents, disputed facts and missed evidence. Track referral quality and downstream decisions, and require the appropriate authority before coverage, payment or customer records change.

02Capability map

Where insurance puts AI to work, domain by domain.

Pick a domain to see its applications. Each one opens a full report.

Inside Claims Management

Applications in Claims Management

What the use cases ask AI to do

Share of the insurance use cases, by the kind of work the AI does.

  1. Reasoning

    Analyzes, predicts and decides: scoring, forecasting, classifying.

    65%

  2. Generation

    Creates: text, images, code and other content.

    31%

  3. Agentic

    Acts: plans, uses tools and completes multi-step tasks.

    5%

Under 1% each: perception and optimization.

03What gets built

The approaches insurance uses most, and when to skip them.

Knowing when not to use one matters as much as knowing when to.

  1. 01 · 11 applications

    API-Wrapper

    Thin application layer over a managed AI API.

    API-Wrapper, in depth

  2. 02 · 10 applications

    Workflow Automation

    Use AI inside orchestrated workflows to automate multi-step business processes.

    Use it when

    • When you have repetitive, rule-heavy workflows that still require human judgment or document understanding.
    • When the process spans multiple systems (ERP, CRM, ticketing, email) and manual coordination is slow or error-prone.
    • When large volumes of semi-structured or unstructured data (emails, PDFs, forms) must be processed consistently.

    Skip it when

    • When the process is highly creative, exploratory, or non-repeatable, with no stable sequence of steps.
    • When decisions are extremely high-stakes (e.g., life-critical medical decisions, major legal judgments) and regulations require full human control.
    • When you lack reliable access to the necessary systems or data sources to complete the workflow end-to-end.
    Workflow Automation, in depth

  3. 03 · 6 applications

    OCR-Document

    Convert document images into structured, searchable, layout-aware text.

    Use it when

    • When you need to convert scanned PDFs or photographed documents into searchable, machine-readable text.
    • When downstream AI workflows (RAG, classification, extraction, summarization) require access to the full content of legacy or paper documents.
    • When document layout (tables, forms, multi-column text) must be preserved for accurate interpretation or regulatory reasons.

    Skip it when

    • When the source documents are already digital and contain embedded text (e.g., born-digital PDFs, DOCX) that can be extracted without OCR.
    • When images are extremely low quality (very low resolution, heavy compression, severe blur) and cannot be improved with preprocessing.
    • When only a few documents need to be processed occasionally and manual transcription is cheaper and more reliable.
    OCR-Document, in depth

04The applications

35 insurance applications, each with a full report.

The widest first, by the number of use cases each covers. Every report shows what changed, how it works, who has done it and what can go wrong.

  1. Claims Lifecycle CoordinationCoordinate claim intake, prioritization, approved service tasks and exceptions around one authoritative claim record. Begin with a defined property-and-casualty workflow; qualify each automation step and retain accountable review for consequential decisions.OCR & Document Intelligence20 use cases
  2. Claims Fraud Signal and Network ReviewCombine reviewable claim-risk indicators with permitted relationship analysis to help investigators examine suspicious patterns. Start with explicit referral criteria and reliable identities; treat model scores and graph links as leads that require investigation.Workflow Automation18 use cases
  3. Claims Lifecycle Fraud ScreeningScreen claims at defined lifecycle events and route suspicious cases to investigators with the supporting signals. Keep fraud suspicion separate from a finding, and measure the queue’s accuracy and workload before expanding automation.API Wrapper16 use cases
  4. Claims Liability Evidence ReviewOrganize claim facts, policy records and supporting documents for an adjuster’s liability review. Keep allegations, observed facts and professional conclusions separate; use predictive or image-derived signals only for a tested, limited question.OCR & Document Intelligence15 use cases
  5. Claims Document Risk ReviewTurn authorized claim documents into source-linked facts and review exceptions. Give adjusters a reproducible evidence view, then qualify any additional scoring or relationship analysis independently before it affects a claim.API Wrapper15 use cases
  6. Underwriting Submission Review and RoutingConvert a commercial insurance submission into checked facts, an appetite review and a traceable underwriting handoff. Qualify extraction and routing on the actual document mix; keep pricing, binding and exceptions within approved underwriting authority.API Wrapper14 use cases
All 35 applications, A to Z

Work with us

The catalog shows what is possible. A sprint decides what you should fund.

Reading every report still leaves the hardest question open: which move is worth funding first, here, given your constraints. That is the sprint: two weeks, fixed scope, and a candid answer either way.

Book a 30-minute callHow the sprint works

Two weeks Fixed scope A candid fit-or-no-fit answer

  1. 01

    Three ranked opportunities

    A decision-ready shortlist scored against value, feasibility, evidence, risk, and time to proof.

  2. 02

    One pilot-ready workflow

    A bounded operating design with actors, handoffs, data needs, controls, and success measures.

  3. 03

    A defensible 90-day plan

    Build-versus-buy guidance, ROI assumptions, risk treatment, sequencing, and executive decisions.

05Rules & standards

Rules and standards to check, with their scope and sources.

Selected US and EU references for these workflows. Applicability depends on jurisdiction, organization, data and intended use. The planning notes are Atlas editorial guidance; they are not official implementation-time estimates or an exhaustive list of obligations.

  1. NAIC model bulletin on AI systems

    Model bulletin

    United States · state insurance supervision

    The model bulletin outlines expectations for insurer AI governance. Its effect depends on state adoption and applicable law; it is not a single nationwide AI statute.

    NAIC: Artificial Intelligence and state oversight

    Source checked

    Project planning note

    Check the adopting state’s actual bulletin or rules and document how the underwriting or claims use is governed.

  2. GDPR · personal data and automated decisions

    Law or regulation

    European Union / EEA · processing within GDPR scope

    Personal-data processing needs an applicable lawful basis and safeguards. Article 9 adds conditions for special-category data; Article 22 addresses certain solely automated decisions with legal or similarly significant effects. Consent is not the only lawful basis.

    EUR-Lex: GDPR, Articles 3, 6, 9, 22 and 35

    Source checked

    Project planning note

    For in-scope processing, map sensitive data and automated decisions to the applicable GDPR conditions.

Your next step

Find your first move in insurance.

Ask the workspace which move fits your company. Add your size, systems and constraints, and it works from these 35 applications to a plan your team can act on.

Plan your first AI move

Rather talk it through? Book a 30-minute call