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Examples

AI in insurance use cases that work today

Eight ways insurers and agencies use AI and automation, from claims intake to customer replies, with what each one does and where a person decides.

Updated 5 min read

The short answer

AI in insurance use cases cover claims intake and triage, document reading, underwriting support, fraud flags, policy servicing and customer replies. Rules-based bots handle the fixed steps; AI reads the messy parts. Below are the main examples, with what each one does.

Key points

  • Claims and documents are the most common starting point
  • Bots handle fixed steps, AI reads unstructured files
  • A licensed person keeps the final say on decisions

Examples

AI in insurance use cases by workflow

Results appear only where a client project or a published source backs them.

  • Claims

    Claims intake and triage

    AI reads documents and photos, checks the policy and recommends a decision, so adjusters handle only the complex cases.

    75%faster claims processing

    Read the case →

  • Operations

    Document reading

    AI extracts data from forms, statements and medical or repair documents and fills the right fields in your system.

    Read the case →

  • Underwriting

    Underwriting support

    AI pulls application data, flags missing items and prepares a summary, so the underwriter reviews and decides.

    Read the case →

  • Special investigations

    Fraud flags

    Predictive models score claims for unusual patterns and send the suspicious ones to an investigator.

    80%of fraud-survey respondents use predictive modeling

  • Service

    Policy servicing

    Automation handles address changes, certificate requests and renewal reminders without a person retyping data.

    Read the case →

  • Agencies

    Agency quote intake

    AI captures a prospect's details from email or a web form, fills the quote request and follows up on missing items.

    Read the case →

  • Customer service

    Customer replies and chatbots

    A chatbot answers coverage and claim-status questions at any hour and passes complex cases to a person with the context.

    Read the case →

  • Back office

    Routine back-office tasks

    Bots move data between systems, reconcile payments and file documents on a fixed schedule.

    Read the case →

Where insurers stand on AI

  • Scaled

    Only 7% have scaled AI across the company

  • Piloting

    Roughly two-thirds remain in pilots

  • Exploring

    88% of auto insurers use or plan AI

Adoption is wide but shallow. BCG found that only 7% of insurers have scaled AI across their organizations, while roughly two-thirds remain in pilots.

Of 193 auto insurers answering an NAIC survey, 88% already use AI or machine learning, or plan to use or explore it.

McKinsey reports that Aviva saved more than £60 million in 2024 after transforming its motor claims domain.

Generative AI use cases in insurance

  1. 01

    Read

    AI reads the claim file or policy

  2. 02

    Draft

    It writes a summary or reply

  3. 03

    Review

    A licensed person checks it

  4. 04

    Send

    The approved version goes out

Generative AI use cases in insurance center on reading and writing. Think claim file summaries, customer letter drafts and answers to agent questions from policy documents.

In Deloitte's survey of 200 US insurance executives, 76% said gen AI was already running in at least one business function.

Conning reported that nearly two thirds of respondents use large language models in sales and underwriting.

Gen AI insurance use cases still need review. A person checks anything that affects coverage or payment. Many of these gen AI use cases in insurance industry settings sit inside a written governance program.

RPA use cases in insurance

RPA bot

  • Follows fixed rules
  • Moves data between systems
  • Breaks when a form changes

AI

  • Reads messy documents
  • Handles varied wording
  • Flags cases for a person

Use cases of RPA in insurance are the fixed, repeatable steps. Bots move data between policy and billing systems, file documents and send status updates.

Most common RPA use cases insurance teams report include claims data entry, policy renewals and payment reconciliation. An RPA in insurance case study shows the usual pattern.

Like most RPA case studies in insurance, the bot does the clicking and AI reads only the documents.

If you want to compare with AI, see AI vs intelligent automation. For a deeper look, our RPA guide covers the basics.

Insurance chatbot use cases

Good for a chatbot

  • Claim status checks
  • Coverage questions
  • Certificate requests
  • Report a loss

Keep with a person

  • Coverage disputes
  • Denied claims
  • Complex injury claims

Insurance chatbot examples include claim-status checks, coverage questions, certificate requests and first notice of loss intake.

Insurance chatbot use cases work best when the bot knows the policy data and hands off cleanly. Our AI chatbot work follows that rule: the bot answers routine questions and a person takes the rest.

Jobs and regulators

  • Underwriters

    Projected 4% decline to 2035

  • Claims staff

    Projected 6% decline to 2035

  • Regulators

    Expect a written AI program

Underwriter jobs are projected to shrink 4 percent between 2025 and 2035, with BLS pointing to automated underwriting software. For claims adjusters and examiners it projects a 6 percent decline.

In the NAIC's view, AI will mostly assist people rather than take over their jobs.

The NAIC model bulletin asks insurers for a written program governing responsible AI use in underwriting, pricing, claims and fraud work.

How an AI in insurance case study looks

In one AI in insurance case study of ours, a regional insurer now processes claims 75% faster. We built an AI that reads the documents and photos, checks the policy and recommends a decision.

Adjusters handle only the complex cases. Every month the insurer gets 600+ hours back, with 99.5% decision accuracy and 30% more capacity, all without hiring.

See the full approach on our claims automation page.

  • 600+ hours back every month
  • 99.5% decision accuracy
  • 30% more capacity, no new hires

Our data

What AI answers when buyers ask

We asked ChatGPT, Gemini and Perplexity 10 buyer questions about AI for insurance agencies. They named 119 companies, and 85% appeared in only one of the three.

CriterionChatGPTGeminiPerplexity
Companies named484653
Sites cited mostsonant.ai (3 of 10)reddit.com and wyecliff.ai (3 of 10)agentforthefuture.com and cloudtalk.io (3 of 10)

Nexum measurement, October 20, 2026, via DataForSEO: 10 buyer questions to each AI, US English, 30 answers in total. 10 companies appeared in all three. Google showed an AI Overview for 9 of the 10 questions.

FAQ

Questions about AI in insurance

  • Insurers use it in underwriting, pricing, customer service, claims handling, marketing and fraud detection. The regulators' association lists these as the main areas.

  • RPA means robotic process automation: software bots that follow fixed rules. A common example is a bot that copies claim data from an email into the policy system and files the documents.

  • No. RPA still handles fixed, rule-based steps well. AI is added when a document or message must be read and understood, and the two often run in the same workflow.

  • Claims intake, document reading, underwriting support, fraud flags and customer replies lead the list. Each is frequent, repetitive and simple to measure.

  • Not by default. The AI recommends and a licensed adjuster approves, especially for complex or disputed claims. Regulators expect insurers to be able to explain how AI supports those decisions.

  • Triple-I cites the Coalition Against Insurance Fraud estimate that fraud costs the US $308.6 billion a year. That is why many insurers use predictive models to flag suspicious claims.

  • Yes. Agencies use AI for quote intake, follow-up and policy servicing. Independent agencies place 61.5% of all property and casualty insurance written in the US, so those tasks add up.

Related

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