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A claims adjuster inspecting a damaged car

Insurance
Use Cases

Each part of an insurer has its own problems, data, and regulators. Start with your department.

Insurance overview

24 use cases

Search use casesAll departments

Actuarial

1 use case

Personalized rate managementAI personalized rate management estimates each policyholder's risk from individual behavior and context, not just broad rating classes, so pricing and actuarial teams can set rates that track real risk more closely.

Claims

6 use cases

Claims fraud detectionMachine learning scores every insurance claim for fraud risk from first notice of loss onward, so the SIU works the claims most likely to be fraudulent.Claims scoring, routing and denial reviewMachine learning that scores each insurance claim on arrival, routes it by risk and complexity, and explains every score and denial with reason codes.FNOL intake and claims triageAI that reads a first notice of loss packet, checks it against the policy, flags claims needing special handling, and drafts the routing for your claims team to approve.Policy and endorsement document intelligencePolicy and endorsement document intelligence reads your policy forms, endorsements, and contracts, then answers coverage questions with a citation to the exact wording, for underwriting, claims, and servicing teams.Subrogation identification and recoveryAI reads each claim file for third-party recovery potential, judges the evidence against your guidelines, and drafts the recovery package for your team to review and pursue.Telematics-based insurance modelsMachine learning models that use vehicle driving data to score claims fraud, customer churn and billing risk for each policyholder, with reasons attached.

Distribution

3 use cases

Agent and broker performance and next best actionPredictive models that score leads, compare producer performance, and suggest a next best action for each agent, broker or household, with reasons attached.Personalized product bundling for insurersMachine learning that predicts which additional policy or coverage each household is likely to need next, with a reason code, so agents, call centers and websites offer one relevant bundle instead of a generic package.Telematics-based insurance modelsMachine learning models that use vehicle driving data to score claims fraud, customer churn and billing risk for each policyholder, with reasons attached.

Documents

1 use case

Legal research and aid assistantsAn AI knowledge assistant that finds the statutes, cases and guidance behind a legal question and answers with citations a lawyer or paralegal can check, giving information, not advice.

Engagement

4 use cases

Call summary and resolutionGenerative AI reads or listens to every recorded customer call, writes a short summary of what happened, and flags whether the issue was resolved so your team can follow up first.Content and product personalization for marketingPredictive models score which message, image, offer and product each customer is most likely to respond to, so every channel shows content built for them.Customer retentionAI customer retention for insurers predicts which policyholders are likely to lapse or switch at renewal, and why, so your team spends offers on those worth keeping.Next best action and offerMachine learning that ranks the one offer, message or channel most likely to move each customer forward, so marketing sends one relevant action per customer instead of one campaign for everyone.

Finance

2 use cases

Accounting and auditingAI accounting and auditing scores every journal entry and account balance for unusual patterns, so your auditors test the riskiest items first instead of a random sample.Strategic insight summarizerAn AI assistant that reads annual reports, earnings releases and company websites, then returns a company's strategic priorities, key results and opportunities, with the source cited for each point.

Fraud

4 use cases

Application and account opening fraudApplication and account opening fraud detection scores each new application for stolen identities, synthetic identities and mule risk before the account opens, so only risky applicants reach your fraud analysts.Claims fraud detectionMachine learning scores every insurance claim for fraud risk from first notice of loss onward, so the SIU works the claims most likely to be fraudulent.Collusion fraud detectionMachine learning that links shared devices, addresses and payment paths across accounts to expose collusion rings that look normal one account at a time.Verifying identity documents at onboardingAn AI assistant reads the identity document an applicant uploads at onboarding, checks each field for gaps, expiry and tampering, and returns a verified or flagged result.

Marketing

5 use cases

Content and product personalization for marketingPredictive models score which message, image, offer and product each customer is most likely to respond to, so every channel shows content built for them.Customer retentionAI customer retention for insurers predicts which policyholders are likely to lapse or switch at renewal, and why, so your team spends offers on those worth keeping.Lead scoringAI lead scoring ranks every lead by how likely it is to become an opportunity and revenue, learning from the leads that converted and the ones that did not.Next best action and offerMachine learning that ranks the one offer, message or channel most likely to move each customer forward, so marketing sends one relevant action per customer instead of one campaign for everyone.Smart segmentationAI smart segmentation groups customers into small, well-defined microsegments by shared behavior and intent, so your marketers can target and explain who is most likely to respond to each message.

Underwriting

5 use cases

Automated underwriting risk scoringAutomated underwriting risk scoring gives each new insurance application a risk score and explains it, so your underwriter decides every application with the reasons in front of them.Personalized rate managementAI personalized rate management estimates each policyholder's risk from individual behavior and context, not just broad rating classes, so pricing and actuarial teams can set rates that track real risk more closely.Policy and endorsement document intelligencePolicy and endorsement document intelligence reads your policy forms, endorsements, and contracts, then answers coverage questions with a citation to the exact wording, for underwriting, claims, and servicing teams.Premium audit validationPremium audit validation uses AI to read payroll reports and records, check them against the exposure a policyholder declared at binding, and send each variance to your premium auditor.Turning broker submissions into clean risk dataAI reads broker submissions, loss runs and prior policies into clean, normalized risk data, checks it against your appetite, and drafts the clearance file for your underwriter.

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