Agentic AI in Insurance

Introduction

Agentic AI describes AI systems that go beyond producing outputs and instead take actions, often autonomously, to achieve defined goals. Within insurance, agentic systems are increasingly discussed as a means of automating tasks across claims, underwriting, customer service, and fraud investigation.

As agentic AI moves from concept to deployment, Australian regulatory expectations are increasingly clear: AI use must be governed, risk-managed, and consistent with existing prudential and conduct standards.

What Agentic AI Means (Plain English)

Traditional AI systems produce outputs — predictions, classifications, recommendations — that humans then act on. Agentic AI systems take the next step: they act on those outputs themselves, often across multiple tools or systems, to complete a task end to end.

In an insurance context, examples might include:

Triage agents that route incoming claims based on assessed risk and complexity
Investigation agents that gather supporting evidence for suspected fraud
Compliance agents that prepare alert dispositions for human review
Customer service agents that handle routine enquiries end to end

Why Agentic AI Is Attracting Attention

Agentic AI is attracting particular attention because it has the potential to reduce manual handling significantly. Where current AI systems support specific decisions, agentic systems could automate entire workflows — provided the right governance is in place.

This potential is also why regulators are paying close attention. On 30 April 2026, APRA published a letter to industry calling for a step-change in AI-related risk management and governance, drawing on the findings of a targeted supervisory review of large banks, insurers, and superannuation trustees in late 2025.^¹

The Australian Regulatory Context

Australia’s approach to AI in financial services is largely technology-neutral, with regulators applying existing prudential and conduct standards rather than introducing dedicated AI legislation. Key reference points include:

APRA’s CPS 230 Operational Risk Management, effective from 1 July 2025, which covers AI systems used in critical business services^²
ASIC’s October 2024 REP 798 on governance arrangements in AI^³
APRA’s 30 April 2026 industry letter on AI^¹
Australia’s Voluntary AI Safety Standard and National AI Plan published in December 2025^⁴
The Privacy Act 1988 and Australian Privacy Principles^⁵

These frameworks apply with full force regardless of whether AI tools are deployed, and ASIC has indicated that AI governance is now a present-day supervisory priority.

Key Risks to Manage

Agentic AI introduces specific risks that must be addressed:

Decision autonomy: actions taken without adequate human oversight may produce poor outcomes at speed
Explainability: chains of agentic actions can be harder to reconstruct than single AI decisions
Accountability: clear ownership for outcomes must be maintained, even where actions are automated
Operational resilience: failures in agentic systems can affect critical business services
Third-party dependency: many agentic systems rely on external models and APIs, bringing service provider risk

Implications for Fraud Detection

Agentic AI has clear potential in fraud detection and investigation. Investigation agents can gather supporting data across systems, prepare case packages, and support decision-making. Triage agents can route cases by complexity and risk. Disposition agents can support consistent handling of routine alerts.

In each case, the underlying principle is the same: the agent supports human decision-making, with humans retaining accountability for the outcome. APRA’s expectations on explainability, controls, and governance apply regardless of how the technology is described.^¹,²

Role of Governance and Oversight

Effective deployment of agentic AI requires governance disciplines that extend well beyond model development. These include: