Artificial intelligence (AI) is no longer a futuristic concept for Australian insurers. It’s already reshaping claims triage, fraud detection, marketing and customer engagement. But with new technology comes new risks and regulatory expectations.
White Edges Advisory’s latest blog post outlines how insurers can leverage existing governance structures to manage AI safely, ethically, and compliantly without overengineering or starting from scratch.
Why AI Governance Matters
AI systems can automate decisions that directly affect customers and your business. Without adequate governance, they can introduce serious risks such as biased decision-making, privacy breaches, or non-compliant conduct. Fortunately, insurers are better placed than many to manage these challenges, with existing risk and compliance frameworks that can be adapted and expanded to include AI oversight.
Where AI Is Already at Work in Insurance
Many insurance companies have already deployed AI solutions and machine learning tools at an operational level to improve their business processes and efficiency. Common use cases include:
- Customer Service Automation: AI chatbots handle common queries, lodge claims, and escalate issues;
- Fraud Detection: Machine learning models flag potentially fraudulent claims or unusual behaviour across portfolios;
- Marketing and Communications: Generative AI tools help create customer-specific marketing messages and policy information;
- Underwriting and Risk Scoring: Predictive analytics models that can help make pricing and eligibility decisions from complex datasets; and
- Back Office Efficiency: AI tools streamline manual processes, from document review to claims triage.
These tools offer significant operational and customer benefits but they’re not without risk. AI systems require careful oversight to avoid compliance breaches, reputational harm, and unintended or unfair outcomes.
What Governance Gaps Are Emerging
Insurers are rightly cautious and concerned not just with “what the AI does” but “what happens when it goes wrong.” In Australia, there is no single piece of legislation dedicated to AI (yet). However, AI is already subject to regulation through a patchwork of existing laws, including the Privacy Act, the Australian Consumer Law, Anti-Discrimination Laws, the Corporations Act, and sector-specific frameworks such as APRA’s prudential standards.
Some examples of governance risks that may arise in insurance use cases include:
- Privacy: Insurers work with large volumes of customer data, much of it sensitive. Even de-identified data can sometimes be re-identified when cross-referenced with other sources. Any data use must comply with privacy obligations including appropriate disclosure and consent mechanisms, security controls, and review of data handling by third-party providers.
- Accuracy and Hallucinations: Gen AI systems can generate factually incorrect or misleading content (“hallucinations”), risking regulatory breaches, misleading conduct and reputational harm if you don’t have sufficient human oversight.
- Transparency: Decisions made by AI must be explainable to customers and regulators. Lack of clear disclosure when customers are targeted or engaged by Gen AI generated content can erode trust.
- Discrimination: AI-enabled underwriting can expose insurers to liability under anti-discrimination laws. If an algorithm disadvantages a person based on age, gender, race, or disability, insurers may be in breach, even unintentionally.
- Data Quality and Bias: AI models are only as good as the data they are trained on. Poor data hygiene or embedded bias can lead to unfair or non-compliant outcomes.
- Shadow IT Use: Staff may independently use tools like ChatGPT or Gemini, bypassing governance structures and creating risks around privacy, security, and regulatory compliance.
- Agent Autonomy: Where AI tools act semi-independently (e.g., triggering marketing communications), firms must define when and how decisions are escalated for human oversight.
- Director Accountability: Inadequate AI governance can expose accountable persons under the Financial Accountability Regime (FAR). As with any technology, FAR requires that there is clear accountability for AI systems and associated risk management.
How You Can Build from What You Have
You don’t need to start from scratch. As APRA regulated entities, insurers already have operational risk frameworks, compliance processes, and privacy governance in place. These governance functions don’t need to be rebuilt, just extended. Some examples of how existing frameworks can be uplifted for AI include:
- Risk Management: APRA Prudential Standard CPS 220 Risk Management and CPS 230 Operational Risk Management aligned frameworks should be expanded to include AI-specific risks (e.g., model drift, explainability, bias, misuse).
- Compliance: Embed AI-related questions into due diligence, marketing reviews, and third-party onboarding.
- Privacy: Update privacy impact assessments to address automated decision-making and third-party AI vendor risks. Update Privacy Collection Notices and Policies to disclose AI usage.
- Outsourcing & Procurement: Strengthen third-party risk assessments for AI vendors. Include robust AI-specific clauses in contracts such as transparency obligations, audit rights, data usage limits, and human oversight requirements. Ensure consistency with CPS 230 and APRA’s expectations around operational risk and accountability.
White Edges can work with you to review and enhance existing governance frameworks to reflect AI risks proportionately without overengineering or adding bureaucratic drag.
Practical First Steps
If you’re unsure how to begin, here are five practical steps any insurer can take to start governing AI use safely and proportionately:
- Assess Your Current AI Use and Exposure
Identify where AI is already in use, including informal or “shadow” tools used by marketing, IT, or claims teams. - Establish an AI Governance Framework
Create a framework that is informed by IEC/ISO 42001 Information technology — Artificial intelligence Management system and APRA’s risk expectations covering oversight, escalation, risk ownership, and human-in-the-loop safeguards. - Improve AI Literacy Across the Business
Build awareness across business units not just IT. Directors, customer service, claims officers, marketers, and legal staff all need to understand AI’s benefits and boundaries. Invest in training and develop policies and procedures covering AI usage. - Monitor and Audit Use Cases
Defining metrics for model performance, bias, and customer impact is essential. This will enable continuous monitoring and regular review to detect any issues or model drift. Regular audits of AI systems and decision-making needs to be embedded into operations to ensure ongoing reliability, fairness and compliance. - Embed Cross-Functional Ownership
AI is not “just a tech project.” Form a working group or steering committee with legal, risk, compliance, and business input.
The Role of Human Oversight
Embedding “human in the loop” oversight is the cornerstone of any AI governance approach. Human judgement is essential when AI systems are influencing consequential decisions.
It’s crucial to have clear thresholds where escalation to a human is mandatory and ensure manual overrides are logged and tested. Without human oversight, there is a much greater risk of AI breaching regulatory requirements or causing customer harm.
White Edges Advisory can support you as you take these steps in a practical, right-sized way from policy and checklist development, contract uplift, readiness assessments, to risk uplift workshops tailored to your organisation.
Final Thoughts
AI is moving fast and regulatory expectations are rising. Small to medium insurers need to be proactive and have the right governance in place to use AI responsibly and compliantly. The solution isn’t about throwing money at the problem or copying big players. It’s about scaling smart, governing proportionately, and building trust.
At White Edges Advisory we combine in-house insurance legal and risk experience with cutting edge knowledge of AI governance, ESG, and regulatory change. If you’re trialling AI or facing internal pressure to “just get on with it,” we can help you adopt AI in a way that’s efficient, ethical, and regulator-ready.
