Claims automation
Claims automation, explained: what it covers and where it stops
Claims automation is the use of software to carry out steps in the claims process that a handler would otherwise do by hand, from intake and validation through triage, reserving, decisioning, payment and customer correspondence. The aim is straight-through handling of simple claims and faster, better-informed routing of complex ones.
For insurers
See the damage before you send anyone
Venta Capture, a product of VentaVid, sends the policyholder a link. They film the damage on their own phone, guided step by step, and the evidence lands with the claim.
It is rarely one product. In most claims operations it is a stack: rules and models sitting on the policy system, a document and image pipeline, integrations out to repair networks and suppliers, and a case layer that decides what a human sees and when they see it.
What does claims automation actually automate?
- Notification and intake: capturing the loss, validating cover, opening the case and setting an initial reserve without a phone call. The mechanics are covered in FNOL automation.
- Data extraction: reading estimates, invoices, incident references and medical reports into structured fields instead of into a handler's notes.
- Triage and segmentation: deciding which claims go straight through, which go to a desk handler, which go to an engineer, and which go to counter-fraud.
- Bounded decisioning: liability rules, total loss thresholds, settlement offers inside defined tolerances, with anything outside them escalated.
- Orchestration: booking the repair, instructing suppliers, releasing payment, updating the customer at each status change.
- Screening: running database and internal history checks at first touch rather than in week three.
How does claims automation work end to end?
Every claim arrives as two things: a set of facts and a set of artefacts. Automation is strong on the facts, because they are structured and checkable. It is weak on the artefacts, because photographs, video, invoices and free-text descriptions turn up in whatever form the claimant happened to send.
Sequencing follows from that. Structure the input first, then automate the decision. Teams that automate the decision on top of unstructured input tend to build a large exception queue and call it a workflow.
Claims automation explained: a practical example
A windscreen claim arrives through a web form at nine in the evening. Cover, excess, vehicle identity and registration validity are checked automatically, and the claim is matched to an approved fitter covering that postcode.
The customer picks a fitting slot before the contact centre opens the next morning, and the file carries an audit trail of every automated decision. No handler touched it. That is straight-through processing doing exactly what it is good at, on the kind of claim that suits it.
Why automation stalls when the visual evidence is incomplete
This part rarely appears in the business case. A rules engine can settle instantly whether a policy was in force. It cannot settle whether the damage in a photograph matches the account given, and it certainly cannot do anything useful when the photograph shows half a panel and no context.
So the claim stops. It lands in an exception queue, a handler requests more images, the customer replies three days later with two more shots taken in the dark, and round it goes. The logic did not fail. The input did.
The cost pressure behind that delay is measurable. The Association of British Insurers reported that motor insurers paid a record £3.2 billion in claims in the second quarter of 2026, with the average motor claim up 4% to £4,900, driven largely by the cost of repairing vehicles packed with cameras and sensors. Every extra day a claim waits for a usable image sits on top of that number.
Claims automation vs claims digitisation
The two get used interchangeably and they are not the same. Digitisation moves a process off paper. Automation removes the human step. A PDF form emailed into a shared inbox is fully digitised, and it can generate more manual work than the paper form it replaced.
The test is unglamorous: count human touches per claim before and after. If the count did not fall, nothing was automated.
What claims teams measure
- Straight-through rate: share of claims settled with no human intervention, reported by claim type rather than as one blended figure.
- Touch count: average number of human interventions per claim, which is usually the more honest number.
- Exception reasons, ranked: what actually breaks straight-through processing. Missing or unusable evidence sits near the top in most motor and property books.
- Cycle time: worth defining precisely before benchmarking, since the start and stop points vary between insurers. See claims cycle time.
- Leakage and rework: what automation cost in wrong decisions, not only what it saved in handling minutes.
If missing visual evidence is the dominant exception reason, the fix sits upstream of the workflow engine rather than inside it. Venta Capture, a product of VentaVid, sends the claimant a link that guides them through what to capture and in what order, and returns a structured, timestamped submission the workflow can act on instead of a folder of loose images. It does not assess the damage. That stays with the handler or the engineer. The upstream standards are covered in guided photo capture and the downstream effect in reduce claims cycle time.
