Topic
Claims Automation
Claims is where policyholders find out what an insurer is actually worth. Automation is now reshaping every step of it, from first notice of loss to payment. This hub answers the questions carriers and vendors ask most, and links to the talks and demos worth watching.
The short version
- Straight-through processing (STP) handles simple, low-value claims with no human touch; most carriers start at 10-30% STP and grow from there.
- The biggest early wins are document intake and data extraction, not decisioning.
- AI fraud triage scores claims at FNOL so investigators spend time on the 1-2% that matter.
- Automation only pays off when claims data is clean — data quality work usually precedes model work.
- Regulators expect explainability: every automated decision needs an auditable reason code.
What is claims automation?
Claims automation is the use of software — rules, workflow, document AI and increasingly large language models — to complete steps of the claims journey without a human touching them. In practice it spans four stages: intake (FNOL through a portal, app or API), triage (severity, complexity and fraud scoring), assessment (document extraction, damage estimation, coverage checks) and settlement (payment orchestration and recovery). Full automation of a claim end to end is called straight-through processing. Most carriers automate individual steps long before they automate a whole claim.
How do you automate claims processing, step by step?
Start by instrumenting the current process: measure cycle time and touch count for each claim type, then rank by volume times touches. Digitise intake so data arrives structured rather than as email attachments. Add document AI to extract fields from invoices, police reports and medical notes. Codify the coverage and payment rules that adjusters already apply consistently, and route everything else to a human with the extracted data pre-populated. Only then introduce predictive models for severity and fraud. Carriers that reverse this order — model first, data last — usually stall.
Which claims can be settled straight through?
High-frequency, low-severity, well-documented claims: motor glass, travel delay, device damage, simple property contents, pet and dental. The common test is whether coverage can be verified from policy data alone and quantum can be verified from a single trusted document or a fixed schedule. Bodily injury, liability and large commercial losses are poor STP candidates and are better served by automating the evidence-gathering around a human decision.
How does AI detect claims fraud?
Fraud models score a claim at FNOL against historical patterns — network links between claimants and repairers, timing relative to policy inception, text anomalies in the description, image forensics on submitted photos. The output is a triage score, not a decision: high scores route to a special investigations unit, low scores accelerate. The measurable benefit is usually investigator productivity rather than a headline reduction in fraud, and the governance requirement is that no claim is declined on a score alone.
What does good look like — the metrics that matter
Track straight-through rate by claim type, average cycle time from FNOL to payment, touch count per claim, leakage (overpayment against policy terms), reopen rate and claimant NPS. Automation that lifts STP while raising the reopen rate has moved cost, not removed it. A realistic first-year target for a personal lines book is 20-30% STP on eligible claim types with cycle time halved on the automated segment.
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Frequently asked questions
What is automated claims processing?
Automated claims processing is the handling of a claim — or specific steps within it — by software rather than a person. It covers digital first notice of loss, automatic coverage verification, AI extraction of data from documents and photos, rules-based settlement calculation and automated payment. When every step completes without human involvement, the claim is said to have been settled straight through.
How much of the claims process can realistically be automated?
For a personal lines book, 20-40% of claims by count can typically be settled straight through once intake is digital and simple claim types are codified — but that represents a much smaller share of paid loss, because the automated claims are the small ones. Complex, high-severity and injury claims stay with humans; automation there means faster evidence gathering, not autonomous decisions.
Does claims automation reduce headcount?
Rarely directly. Most carriers redeploy adjuster capacity onto complex claims and customer contact rather than cutting it, because the automated segment was never where the loss cost sat. The financial case is usually built on cycle-time reduction, leakage control and improved retention rather than on salary savings.
Is an AI claims decision allowed by regulators?
Broadly yes, with conditions. Regulators expect an auditable reason for every decision, no reliance on prohibited or proxy rating factors, a route to human review, and evidence that outcomes have been tested for bias. Declining a claim solely on a model score without human review is the pattern most likely to attract scrutiny.
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Last reviewed 31 July 2026 by the InsurTech.TV editorial team.