Insurance

Too many AI ideas for one claims process? Choose with evidence.

Insurers see AI proposals for first notice of loss, document checks, fraud signals and underwriting, often aimed at the same claims desk. MetroMinds compares them on value, feasibility and overlap, so you fund the ones that add up.

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84insurance use cases

29%in case and exception handling

37%ready to scale now

Insurance in focus

Many ideas, one claims desk

Most insurance use cases sit in case and exception handling: the claims that do not follow the standard path. They are not variations of one tool. They compete for the same team.

Case and Exception Handling24

Risk Identification and Assessment9

Service Delivery Management6

All other capabilities45

Source: MetroMinds Library, 84 insurance use cases, August 2026.

FeaturedHow a Customer Operations Manager Establishes Loss Facts at First NoticeManual review time 25% to 45% lower

Manual review time reduction25% to 45%

Rework reduction15% to 30%

Clean pass-through increase10% to 25%

Cases and exceptions come first

Of 84 insurance use cases in the Library, 24 sit in case and exception handling: the claims that do not follow the standard path. Risk identification and service delivery follow.

Top 5 capabilities

Case and Exception Handling24
Risk Identification and Assessment9
Service Delivery Management6
MLOps & LLMOps3
Risk Monitoring and Reporting3

How insurers can choose between claims AI ideas

Insurance use cases by value and feasibility

Business value →

Strategic bets

30 36%

Scale now

31 37%

Park / rethink

12 14%

Quick improvements

11 13%

Feasibility →

Business value 3.70Feasibility 3.44Risk 3.81

Source: MetroMinds Library, 84 insurance use cases, August 2026. High means a score of 4 or 5.

Browse insurance use cases

Filter by subsector, function or AI domain and compare benchmark ranges and risk scores.

Featured use case

Insurance (P&C and Life)AI-PerceptionDocument Intelligence

How a Customer Operations Manager Establishes Loss Facts at First Notice

At first notice of loss, the evidence arrives from several sources at once: a repairer estimate, a police report, photographs and forms. The AI reads the scanned pages, extracts loss dates, policy and claim numbers, amounts and signatures, and checks them against the policy record and the proof required. Each value links back to its page, and anything that does not match is flagged for review.

Benchmark ranges

Manual review time reduction25% to 45%

Rework reduction15% to 30%

Clean pass-through increase10% to 25%

Scores

Value Feasibility Risk

Out of 5. A higher risk score means more risk.

How the work changes

Less time opening files, locating fields and retyping values. More time validating extracted fields, resolving confidence flags and recording why a correction was made. The fraud risk manager is involved only when a value is flagged.

Benchmark ranges based on real-world implementations.

See the full use case

Where the EU AI Act applies in insurance

Life and health insurance pricing.

AI used for risk assessment and pricing of life and health insurance is high-risk under Annex III. 4 insurance use cases in the Library touch this area. As a deployer you also carry out a fundamental rights impact assessment.

Employment.

AI that allocates work to, monitors or assesses staff, for example in claims teams, is high-risk too. 3 insurance use cases touch this area.

Obligations for Annex III high-risk systems apply from 2 December 2027. Claims document checks such as the featured use case are usually outside Annex III, but they still belong in your AI register.

See how the AI register records each system