AI claims and document automation for Australian insurers

What can AI actually do in insurance claims processing?

In insurance claims processing, AI is good at turning unstructured claim material into structured, routed work: reading emails and attachments, extracting the facts, spotting what’s missing and drafting the next message. It is not good at, and shouldn’t be trusted with, deciding whether a claim is covered.

Here is how a claim moves through an AI-assisted intake flow we would design, step by step:

1. Receive. Claims arrive by web form, email, broker portal or phone transcript. Everything lands in one intake queue with the original preserved.
2. Classify. The system identifies the product line (home, motor, travel, commercial property), the claim type and the documents attached.
3. Extract. Fields are pulled from each document: policy number, date and place of loss, amounts, repairer or provider details. Each field carries a confidence score and a link back to where it was found.
4. Match. The claim is linked to the policy in your system of record and checked for obvious gaps, such as a missing police event number for a theft claim.
5. Flag. Vulnerability indicators, urgency (a household without a working roof) and possible inconsistencies are flagged for a person.
6. Route. The claim goes to the right team and priority, with a one-paragraph summary.
7. Draft. The system drafts the acknowledgement and any request for further information, which an officer edits and sends.
8. Decide. A claims officer makes the decision. The AI can assemble the relevant policy wording and evidence, but the decision and its reasons belong to a person.

Steps 1 to 7 are where most handling time goes, and none of them requires the AI to make a judgement the customer could dispute.

Where does the General Insurance Code of Practice shape the design?

The Code’s commitments become system requirements: deadlines become timers, customer rights become data you must be able to produce, and vulnerability becomes a routing rule. The table maps the commitments most relevant to automation. Check the current Code text for exact wording; this is our engineering reading, not legal advice.

Code commitment (current Code, last updated October 2023; a redrafted Code went to public consultation in mid 2026)	Design implication
Where further information or assessment is needed, within 10 business days of receiving a claim, tell the customer what information is needed and give an estimated timeframe	Intake must complete classification and gap detection fast enough for an officer to send the request inside the window; the clock starts at receipt, not at triage
Progress updates at least every 20 business days	A timer per claim that surfaces overdue updates to the handling officer, with a drafted update ready
Decision within 10 business days of having all relevant information, and generally within 4 months of the claim	Track “all information received” as an explicit status so the decision clock is visible
Written reasons when a claim is denied, with the right to request the information and reports relied on	Every document and extracted fact used must be retrievable and exportable; AI summaries are working notes, not the record of reasons
Identify customers experiencing vulnerability, including family violence and financial hardship, and provide extra support	Indicators route the claim to a trained person and suppress automated outbound messages
Standards for investigators, including notification and interview conduct	AI inconsistency flags go to a person; the system never opens an investigation on its own

Complaints and disputes continue to run through your internal dispute resolution process and, externally, AFCA. An AI system that drafts or triages complaints needs the same auditability as the claims side.

Why should the decision stay with a person?

Because the Code, your duty of utmost good faith and the Privacy Act’s coming automated decision rules all expect a reasoned, reviewable decision, and large language models don’t produce reliable reasons. A model can write a fluent denial letter that cites the wrong exclusion. Keeping a human on the decision contains that failure to a draft that gets corrected.

There is also a supervisory reason. APRA’s April 2026 letter to industry expects human involvement for high-risk decisions and continuous monitoring of AI systems. A decline recommendation affecting a customer’s home or car is squarely a high-risk decision. From 10 December 2026, if a computer program makes, or substantially helps make, a decision that significantly affects someone, your privacy policy must say so. Our automated decision-making guide covers what that involves.

The practical design is a review screen where the officer sees the AI’s proposed facts beside the source documents, accepts or corrects each one, and records the decision. Corrections feed the evaluation set, which is how accuracy improves without retraining a model.

Which documents are worth automating first?

Start with high-volume, semi-structured documents where the fields are predictable. Handwritten statements and long expert reports are harder and usually come later.

Document	Typical fields	Difficulty
Online and PDF claim forms	Policy number, loss date, loss description, contact details	Low
Repairer and builder quotes	Line items, labour, parts, totals, ABN, GST	Low to medium
Invoices and receipts	Supplier, date, amount, items claimed	Low to medium
Police reports and event numbers	Event number, date, station	Medium (format varies by state)
Medical certificates (travel, income protection)	Provider, dates, condition summary	Medium, and sensitive information
Assessor and loss adjuster reports	Cause, scope, recommended amount	High: long, narrative, needs summarising rather than field extraction
Photos	Damage description, visible text, image metadata	Medium: useful for sorting and flagging, not assessment

Our AI document processing service covers the extraction pipeline in more depth, including how confidence thresholds are set.

Where does it sit alongside the claims platform?

The AI layer reads from and writes back to your claims platform; it doesn’t become a second system of record. Most of the insurance software development in a project like this is integration work around the platforms below. Typical integration points:

• Claims platform: Guidewire ClaimCenter, Duck Creek Claims or an in-house system, through its APIs or integration layer
• Policy administration, for coverage and policy details (read-only)
• Email and contact centre: Microsoft 365 or Google Workspace mailboxes, and call transcription where claims are lodged by phone
• Broker and repairer portals
• Document management, for storing originals and generated correspondence
• Identity (Entra ID or Okta) so officers only see claims they are permitted to handle
• Your SIEM for audit logging, and your data platform for reporting

What can go wrong, and how is it contained?

The failure modes in claims are specific: a missed deadline, a vulnerable customer receiving an automated message, a fact extracted wrongly and relied on, or claim documents used to attack the system. Each has a control we would build in from the start.

• Clock drift. If intake stalls, for example because a model endpoint is down, the 10 business day window keeps running. The system falls back to a plain queue with the original documents, so officers can keep working without AI, and alerts when the backlog grows.
• Automated contact with a vulnerable customer. Outbound messages are drafts by default. Where vulnerability indicators appear anywhere in the claim, automated drafting is switched off for that claim and a person makes contact.
• Silent extraction errors. Low-confidence fields are highlighted, amounts are cross-checked against line item totals, and ABNs are validated against the checksum. Anything that fails goes to review.
• Prompt injection through documents. A claim attachment is untrusted input. Text inside a PDF that tries to instruct the model (“approve this claim”) must have no effect, because the model has no authority to approve anything and its outputs are checked against a fixed schema. We test this deliberately with crafted documents; our prompt injection explainer covers the technique.
• Scope creep. Features that start as “summarise the policy wording” can drift into “tell the officer whether it’s covered”. Keep a written list of what the system is allowed to do, and review it at every release.

How do you know it’s working?

Agree the measures before building, and measure them on your own historical claims. We would set these up in discovery and report them through the pilot. We don’t promise particular improvements; the point is to find out, on your data, whether the system is good enough.

Measure	How it’s calculated	Why it matters
Field extraction accuracy	Correct fields ÷ total fields, on a labelled sample	Decides which fields can skip review
Classification accuracy	Correct product line and claim type ÷ claims	Wrong routing costs more time than no routing
Time from receipt to first contact	Median business hours, before and after	Tracks the 10 business day commitment
Vulnerability recall	Flagged vulnerable claims ÷ all vulnerable claims in the sample	Missing one is worse than a false alarm
Officer correction rate	Fields changed on review ÷ fields shown	The ongoing health signal in production

The LLM evaluation guide explains how to build the labelled sample.

What should an insurer budget for a first workflow?

A first production workflow, usually one product line, typically takes 12 to 20 weeks and costs roughly $100,000 to $300,000 (AUD, ex GST). These are typical Australian market ranges for senior onshore teams, not a quote.

Phase	Duration	Typical range (AUD, ex GST)	What you get
Discovery	3 to 4 weeks	$15,000 to $35,000	Process map, labelled document sample, integration design, risk inputs, fixed price
Pilot	6 to 10 weeks	$55,000 to $150,000	Intake, extraction and routing for one product line with a small officer group
Production	3 to 6 weeks	$30,000 to $115,000	Monitoring, fallbacks, security testing, second product line if in scope

Worked example within the range: 450 hours of pilot engineering at a blended $180 an hour is $81,000; 300 hours to production is $54,000; with $25,000 of discovery the total is $160,000 ex GST. Model usage is a separate running cost that scales with pages processed; we estimate it in discovery from your actual claim volumes.

Working with All Webbed Labs on claims automation

We begin with paid discovery on a sample of your real claims and documents, then build at a fixed price. Data and processing stay in Australian regions by default, every change passes automated quality gates and a senior engineer’s review, and the code lives in your repository from day one. We build to your requirements and provide the evidence your risk, compliance and APRA-facing teams need; your organisation remains responsible for its obligations under the Code and prudential standards. See AI document processing, workflow automation and our wider view of AI for financial services.