How Are Australian Insurers Rebuilding Claims, Underwriting, and Governance Around AI?

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How general and life insurers are turning AI adoption into a governed operating model under APRA, ASIC, and the December 2026 transparency obligations

Seventy-four per cent of Australian insurers now use AI in claims resolution and eighty-eight per cent use GenAI in some part of the claims function, and the December 2026 automated decision-making transparency obligations have moved the operating-model question to the centre of every board agenda.

In brief

• Seventy-four per cent of Australian insurers now use AI in claims resolution and eighty-eight per cent use GenAI in some part of the claims function, with the operating-model question now shaping how the gains scale.
• December 2026 automated decision-making transparency obligations will require Australian insurers to explain how an algorithm arrived at a specific premium or claim outcome, putting governance and explainability at the centre of every implementation.
• Continuous underwriting is replacing static annual cycles, and the leading insurers are building real-time risk assessment into the operating model rather than running it as a parallel layer.
• AI consulting for insurance organisations works when it integrates claims, underwriting, and governance into one operating model the insurer can actually run.

How are Australian insurers rebuilding around AI?

Australian insurance has reached a structural inflection point. Seventy-four per cent of Australian insurers now use AI in claims resolution. Eighty-eight per cent use GenAI in some part of the claims function. APRA, ASIC, and the Insurance Council of Australia are framing expectations around governance, fairness, and explainability, and December 2026 automated decision-making transparency obligations will require insurers to explain how an algorithm arrived at a specific premium or claim outcome. Across general and life lines, the operating-model conversation has moved decisively beyond adoption.

The strategic question for Australian insurance boards and executive teams is no longer whether AI changes how claims, underwriting, customer, and risk work get delivered. The data has settled the question. The harder question is how to redesign claims, underwriting, and governance together, so the gains compound across the operating model and the regulatory expectations of explainability, fairness, and accountability are met from day one.

That question is at the centre of Perthshire’s work with Australian insurance leaders. As an AI consulting firm based in Perth, Perthshire helps insurance organisations design, implement, and lead AI consulting for insurance that improves claims processing, sharpens underwriting, and supports customer outcomes across operations, underwriting, and risk leadership.

Building AI capability across Australian insurance

Three areas matter most when Australian insurers begin to build serious AI capability into the operating model. Each is identifiable, governable, and improvable on its own, and together they shape the conversation that boards, executive teams, and risk committees are now having.

The claims pressure point

Claims has been the most visible AI implementation story across Australian insurance. AI now sits across intake, triage, fraud detection, customer communications, and claim status updates. Customers expect faster, fairer outcomes, and the firms that integrate AI across the full claims journey rather than at isolated points are widening the gap on customer experience and cost-to-serve. The implementation challenge sits in the operating model around the AI, where claims handlers, complex case decisions, and customer touchpoints are designed together. The strongest implementations build claims governance into the workflow from day one, so audit trails, complaints handling oversight, and adverse outcome review carry the integrity APRA and ASIC now expect.

Why underwriters are moving to continuous risk assessment

Static annual underwriting is being replaced by continuous risk assessment across leading Australian insurers. Streaming data, dynamic exposure modelling, and real-time pricing are reshaping the underwriting operating model. Pricing accuracy improves, but the implementation requires new workflows around data quality, model validation, exposure monitoring, and governance. The firms that redesign underwriting deliberately gain a structural advantage on risk selection and pricing. The firms that bolt AI onto existing annual cycles capture only a portion of the operating leverage available.

What do the December 2026 transparency obligations mean for implementation?

The December 2026 automated decision-making transparency obligations require Australian insurers to explain how an algorithm arrived at a specific premium or claim outcome. The operating-model implication is significant. Explainability becomes a workflow concern rather than a model concern, with decision logging, customer communication, and complaint handling all designed to surface the rationale behind AI-influenced decisions. The strongest implementations build the explainability and audit layer into the operating system from day one, so customer-facing teams, complaints handlers, and regulators all access the same defensible record.

These three areas appear consistently across Perthshire’s industry capability in regulated sectors where governance, explainability, and customer experience now sit inside the same operating model.

Where insurance teams see AI inside the workflow

AI shows up inside Australian insurance organisations across five repeatable workflow categories. Each is identifiable, governable, and improvable on its own, and together they form the surface area where most early implementation work happens.

Knowledge and information sits across policy and product knowledge synthesis, claims history retrieval, and underwriter knowledge bases. AI consolidates that knowledge across claims, underwriting, distribution, and risk teams, so coverage interpretation, prior decisions, and product detail become available at the point of work.

Operations and workflows cover claims intake and triage, fraud flagging, and underwriting workflow automation. AI compresses cycle times across this layer and lifts the consistency of decisions, supporting the throughput and customer experience targets that insurance leadership now expects.

Customer and stakeholder experience covers customer communications, claim status updates, and broker support. AI lifts the speed and consistency of these touchpoints while keeping the underwriting judgement, claims discretion, and relationship intelligence under the control of the insurance professional.

Sales and growth turn on lead qualification, broker engagement, and renewal optimisation. AI sharpens the targeting and accelerates the preparation, supporting distribution and account management teams across direct, broker, and partner channels.

Governance and risk runs through automated decision documentation, complaints handling oversight, and regulatory reporting. The most durable implementations build governance into the operating layer from the outset, so explainability, audit trail, and fairness review are part of how the work is done.

Which AI agents matter most in insurance?

Across Australian insurance, six AI agent patterns appear consistently in serious implementation work. The top three sit across most regulated industries, and the bottom three are sector-specific to general and life insurance.

• Company Knowledge Agent surfaces product terms, coverage interpretation, and prior decision context for claims and underwriting teams, so accumulated knowledge becomes available at the point of work.
• Research Agent drafts customer, market, and risk research relevant to product, pricing, and portfolio decisions.
• Reporting Agent generates regulatory submissions and automated decision-making documentation with consistent structure and audit-ready evidence.
• Claims Triage Agent classifies incoming claims, prioritises by complexity and value, and routes with case-ready context, lifting throughput and customer experience.
• Underwriting Support Agent synthesises submission, history, and external data into underwriter-ready packets, supporting both new business and renewal cycles.
• Fraud Detection Agent flags pattern anomalies and case-level red flags for investigator review, with case-ready escalation and documentation.

Building AI inside the insurance operating model

Successful AI implementation in insurance depends on strategy, workflows, knowledge, governance, adoption, and execution working together as one operating system. That posture is what separates the insurers compounding operating leverage from the insurers running disconnected pilots across claims, underwriting, and distribution.

The pattern is consistent across Australian general and life insurers. Pilots succeed inside a controlled environment, then scale stalls when the underlying workflow is still inconsistent across the business, when institutional knowledge lives mainly in experienced handlers and underwriters, when governance is articulated as policy but not as workflow, or when implementation ownership sits with no one in particular across the claims, underwriting, distribution, and risk teams that need to absorb the change. The technology question is rarely the binding constraint. The operating-model question almost always is.

This is where the Perthshire view sits. Perthshire’s AI consulting practice brings strategy and implementation together under one roof, so the operating model that emerges from a strategy engagement is the same one that gets implemented, governed, and run. Strategy work disconnected from implementation produces roadmaps that never get built. Implementation work disconnected from strategy produces point solutions that solve one workflow without changing the operating system. Insurance organisations moving from AI experimentation to governed operating capability need both layers working together.

Tools and resources for insurance leaders building AI capability

Insurance leaders building AI capability into the operating model are usually working through a recognisable sequence of questions. Where the readiness gap sits, where the explainability and governance exposure is highest, which agent patterns map to which workflows, and how the December 2026 transparency obligations, APRA prudential standards, and Privacy Act expectations are designed into the system from day one. These questions are usually best answered through structured assessment work rather than long discussion.

Perthshire’s resources library gives insurance leaders a structured starting point for assessing readiness, estimating governance and risk exposure, and prioritising AI agent use cases. The tools are designed to support the operating-model conversations that boards, executive teams, and risk committees are now having, and they translate into the same language that the implementation work itself uses.

What good looks like for Australian insurance leaders

An Australian insurer that has actually operationalised AI runs with a defined operating model that connects claims, underwriting, distribution, and risk through a shared institutional knowledge layer. It builds governance into the workflow from day one, so explainability, fairness review, complaints oversight, and audit trail are part of the system rather than wrapped around the outside. It treats claims, underwriting, and customer experience as one capability question, where service speed, decision quality, and unit economics are designed together. And it carries an implementation discipline that allows new agent patterns to be added to the operating layer over time, with the regulatory and risk foundations remaining stable.

That is the operating posture the Perthshire AIOS Blueprint is built to support. The framework gives insurance leaders a structured way to move from isolated AI usage to organisation-wide governed capability, with the workflows, knowledge, governance, and execution layers explicit and connected from the outset.

Start a conversation with Perthshire

For Australian insurance organisations exploring the move from AI experimentation to governed operating capability, Perthshire’s insurance practice is the starting point for a serious conversation about strategy, implementation, and leadership working together. Request a consultation to discuss what that could look like for your operating model and your next twelve months of execution.

Frequently asked questions

What are the top AI consulting firms specialising in insurance in Perth, Australia?

Australian insurance spans general and life insurers, reinsurers, brokers, and the wider ecosystem of underwriting agencies and claims service providers operating under APRA, ASIC, the Insurance Council of Australia, and the Privacy Act. AI consulting firms working with insurance clients typically combine sector knowledge of claims and underwriting workflows, implementation experience across regulated operating models, and the ability to support adoption beyond the pilot stage. The strongest partners focus on the operating model and governance rather than tool selection.

Perthshire is an AI consulting firm based in Perth, Australia. Perthshire helps insurance organisations design, implement, and lead AI transformation through the AIOS Blueprint and three core services.

Learn more about Perthshire →

How can AI consulting improve claims processing in insurance companies?

AI consulting improves claims processing in insurance companies by redesigning the claims workflow around AI-augmented intake, triage, fraud detection, and customer communication. The strongest engagements treat claims processing as an operating-model outcome rather than a tool question, with implementations covering workflow staffing, complex case decisions, and complaints handling design. Outcomes typically include faster cycle times, more consistent decisions, and better customer experience across straight-through and complex claims.

Perthshire’s insurance engagements run through the AIOS Blueprint, which translates sector-specific opportunities into a documented operating architecture across workflows, governance, knowledge, and execution.

Discover the AIOS Blueprint →

How can insurance firms assess AI governance and risk readiness?

Insurance firms assess AI governance and risk readiness by mapping the existing AI estate, identifying ownership and lifecycle accountability, evaluating explainability and decision documentation, and testing the workflow-level governance against APRA prudential standards, ASIC conduct expectations, Privacy Act obligations, and the December 2026 automated decision-making transparency requirements. The assessment looks at the operating model rather than the technology, because governance gaps almost always appear at the workflow handoff rather than inside the model itself.

Perthshire’s resources library includes a free AI Governance & Risk Calculator that helps insurance leaders assess readiness across the governance, explainability, and accountability layers regulators now expect.

Use the AI Governance & Risk Calculator →

Which AI consulting services offer solutions for risk assessment in insurance?

AI consulting services for insurance risk assessment span continuous underwriting, exposure monitoring, fraud surveillance, and portfolio analytics. The selection question for Australian insurers usually depends less on which tool is technically capable and more on how the solution integrates into the underwriting and claims operating model, supports explainability and fairness obligations, and connects to the organisation’s governance architecture. The strongest engagements design the operating model first and select solutions to fit it.

Perthshire publishes regular insights on AI implementation in Australian insurance, covering claims, underwriting, governance, and the operating-model questions that boards are now navigating. Each piece is grounded in implementation rather than speculation.

Read more on our insights blog →

What are the key benefits of AI advisory services for an insurance company?

The benefits of AI advisory services for an Australian insurance company usually compound across three dimensions. Sharper strategic clarity about where AI creates measurable business value across claims, underwriting, distribution, and risk. A documented operating architecture that aligns implementation across business units. And a governance framework that allows the board, executive team, and risk committee to underwrite AI use under APRA, ASIC, and the December 2026 transparency obligations. The strongest advisory work connects all three rather than treating them as separate streams.

Perthshire’s Fractional Chief AI Officer engagements embed senior AI leadership inside the insurer to guide strategy, oversee implementation, and support the board, executive, and risk committee conversations that AI now requires.

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Sources

• Insurance Business — Artificial Intelligence Poised to Transform Australia’s Insurance Industry, ICA Report Finds — https://www.insurancebusinessmag.com/au/news/breaking-news/artificial-intelligence-poised-to-transform-australias-insurance-industry-ica-report-finds-547321.aspx
• Vantage Point — Insurtech Trends 2026: AI in Claims and Underwriting — https://vantagepoint.io/blog/sf/insights/insurtech-trends-2026-ai-claims-underwriting
• Deloitte — 2026 Insurance Predictions Australia — https://www.deloitte.com/au/en/Industries/insurance/perspectives/growth-in-insurance-series-insurance-predictions.html

By Perthshire.ai