Can AI approve or decline insurance claims automatically?

Approval of simple, low-value, fully-evidenced claims within defined rules is achievable. Declines and any decision materially affecting a claimant should keep a human decision-maker with a documented rationale.

How does the EU AI Act affect claims AI?

Classification depends on the specific use. Systems influencing access to essential services or insurance pricing attract higher obligations around risk management, documentation, human oversight and monitoring. Classify each use case explicitly and record the reasoning.

AI use case

AI in claims processing

AI across the claims lifecycle — intake, triage, document handling, fraud signals and status communication — with regulatory controls, risks, KPIs and ROI considerations.

Miguel Torres, Founder, Merjora · Updated 3 September 2026

In short

AI supports claims through faster intake and triage, document extraction, consistency checks against policy terms, surfacing of fraud signals for investigator review, and proactive status communication to claimants. Decisions that affect a customer's entitlement — declines, reserve setting, settlement amounts — require a human decision-maker and a documented rationale, both as good practice and to stay clear of restrictions on fully automated decision-making.

The workflow today

A loss is notified, the claim is registered, documents are gathered, coverage is checked, the claim is assessed, a decision is made and the claimant is kept informed throughout.

Where AI helps

Structuring first notification of loss from email, form or call transcript
Extracting fields from supporting documents
Segmenting claims by complexity so simple claims move faster
Checking submitted facts against policy terms and flagging mismatches
Surfacing anomaly and fraud signals for specialist review
Drafting proactive status updates to claimants

Common use cases

FNOL intake automation
Claims triage and complexity segmentation
Document and evidence extraction
Fraud signal detection with investigator review
Automated claimant status communication

Expected benefits

Shorter cycle time on straightforward claims
Handler time focused on complex cases
More consistent evidence handling
Fewer inbound 'where is my claim' contacts

Implementation complexity

High. Technically tractable, but the governance load is significant: regulatory oversight, EU AI Act classification, fairness testing and audit requirements shape the design from day one.

Data requirements

Historical claims with outcomes and handling times
Policy wording in a machine-readable form
Document corpus covering real-world quality variation

Risks and controls

Automated decisions affecting entitlement without meaningful human review
Bias in triage or fraud scoring against particular groups
Special-category data handling in medical or injury claims
Poor explainability undermining complaint handling and regulatory response

Example workflow

FNOL is captured and structured with confidence flags
Claim is segmented by complexity; simple claims take a fast path
Documents are extracted and checked against policy terms
Anomalies route to an investigator with the evidence attached
Handler decides; rationale and inputs are recorded
Claimant receives automatic status updates at each stage

KPIs to track

Cycle time by claim segment
Straight-through intake rate
Handler touches per claim
Leakage rate
Complaint volume relating to communication

ROI considerations

Value comes from handler minutes per claim and cycle-time reduction on the simple segment
Fraud detection value should only be claimed with a measured control comparison
Governance, testing and documentation are a real cost line in regulated claims

Real-world example

Publicly documented industry example — not a Merjora client.

Aviva: AI in claims →

Frequently asked questions

Can AI approve or decline insurance claims automatically?: Approval of simple, low-value, fully-evidenced claims within defined rules is achievable. Declines and any decision materially affecting a claimant should keep a human decision-maker with a documented rationale.
How does the EU AI Act affect claims AI?: Classification depends on the specific use. Systems influencing access to essential services or insurance pricing attract higher obligations around risk management, documentation, human oversight and monitoring. Classify each use case explicitly and record the reasoning.

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