APRA's Expectations for AI Fraud Detection in Insurance: A Compliance Guide

The Australian Prudential Regulation Authority (APRA) has made its position clear: insurers deploying artificial intelligence must do so within a robust governance framework. As deepfake-enabled fraud escalates across the Australian insurance sector — with synthetic identity claims increasing 340% between 2023 and 2025 according to the Insurance Council of Australia — understanding APRA’s expectations is no longer optional. It is a board-level imperative.

This guide maps APRA’s regulatory framework to the specific requirements of AI-powered deepfake detection in insurance claims.

The Regulatory Landscape for AI in Australian Insurance

APRA does not regulate AI through a single, standalone standard. Instead, its expectations are distributed across multiple prudential standards and guidance notes that collectively form a comprehensive framework for technology risk management.

The key instruments relevant to AI fraud detection include:

CPS 230 Operational Risk Management (effective 1 July 2025)
CPS 234 Information Security
CPG 235 Managing Data Risk (guidance)
SPS 220 Risk Management (superannuation, with insurance parallels)
APRA’s broader technology risk guidance issued through information papers and letters to regulated entities

APRA’s 2024 information paper on artificial intelligence explicitly stated that “the use of AI and machine learning by regulated entities does not diminish the obligations under existing prudential standards.” This means insurers cannot treat AI fraud detection tools as exempt from standard governance requirements.

CPS 230: Operational Resilience and Deepfake Detection

CPS 230, which replaced CPS 231 and CPS 232, represents the most significant shift in APRA’s operational risk framework in over a decade. Its implications for AI-powered fraud detection are substantial.

Critical Operations

Under CPS 230, insurers must identify and maintain a register of critical operations — business activities whose disruption could have a material adverse impact on policyholders or the financial system. For insurers where claims processing represents a core function, the fraud detection mechanisms embedded within that process are likely to fall within the scope of critical operations.

If deepfake detection is integrated into the claims workflow — as it should be — it inherits the resilience requirements of CPS 230. This means:

Tolerance levels must be set for disruption to the deepfake detection capability
Testing of continuity arrangements must include scenarios where AI detection tools are unavailable
Recovery objectives must be defined and documented

Third-Party Risk Management

CPS 230 introduces rigorous requirements for material service providers. Any insurer using a third-party deepfake detection platform — including API-based solutions like deetech’s detection service — must:

Conduct due diligence on the provider’s operational resilience
Maintain contractual provisions ensuring the provider meets APRA’s expectations
Monitor the provider’s performance against agreed service levels
Maintain exit strategies and substitutability plans

This does not mean insurers should avoid third-party AI tools. APRA has acknowledged that outsourcing can improve capability. The requirement is that it be done with proper governance.

Incident Management

When a deepfake detection system produces an incorrect result — whether a false positive that delays a legitimate claim or a false negative that allows a fraudulent one — this may constitute an operational risk incident under CPS 230. Insurers need clear escalation protocols and root cause analysis processes for AI system failures.

CPS 234: Information Security Implications

CPS 234 requires APRA-regulated entities to maintain information security capability commensurate with the threats they face. Deepfake fraud represents a direct information security threat, and the detection tools used to counter it create their own security considerations.

Data Classification

Deepfake detection systems process highly sensitive data: biometric information, identity documents, and claims evidence. Under CPS 234, this data must be classified according to its sensitivity and criticality. Biometric data, in particular, attracts the highest classification levels under most frameworks.

Insurers must ensure that:

Data processed by deepfake detection tools is encrypted in transit and at rest
Access controls follow least-privilege principles
Audit trails capture all interactions with biometric data
Data retention policies comply with both APRA requirements and the Privacy Act 1988

Vulnerability Management

AI models are susceptible to adversarial attacks — deliberate attempts to evade detection. APRA’s vulnerability management requirements under CPS 234 extend to AI systems. Insurers must:

Regularly assess deepfake detection models against emerging attack vectors
Maintain patch and update processes for AI components
Conduct penetration testing that includes adversarial AI scenarios

CPG 235: Managing Data Risk in AI Systems

While CPG 235 is guidance rather than a binding standard, APRA expects regulated entities to treat it seriously. Its data risk management principles are directly applicable to AI fraud detection.

Data Quality

The effectiveness of deepfake detection depends entirely on data quality. CPG 235 requires entities to establish data quality standards covering accuracy, completeness, timeliness, and consistency. For deepfake detection, this means:

Training data used to build detection models must be representative and unbiased
Input data (claims evidence, identity documents) must meet minimum quality thresholds before being processed
Output data (detection results, confidence scores) must be validated and calibrated

Data Lineage

APRA expects entities to maintain data lineage — the ability to trace data from its origin through all transformations to its final use. For deepfake detection, this requires documenting:

Where claim evidence originated (e.g., uploaded by claimant, captured in-person)
What processing the evidence underwent before analysis
How the detection model produced its output
How that output influenced the claims decision

This lineage is critical for defending claims decisions if challenged by policyholders or in court.

Model Risk Management

APRA has not issued a standalone model risk management standard, but its expectations are embedded across multiple instruments and reinforced through supervisory engagement. The key principles for deepfake detection models include:

Model Validation

Before deploying a deepfake detection model in production, insurers should:

Validate accuracy across diverse demographic groups to ensure no systematic bias
Stress test the model against known adversarial techniques
Benchmark against independent datasets, not just the vendor’s claimed performance metrics
Document the model’s limitations and known failure modes

Ongoing Monitoring

Deepfake technology evolves rapidly. A detection model that performs well today may be ineffective against next-generation synthetic media. APRA expects:

Regular model performance reviews (at minimum annually, ideally quarterly for high-risk models)
Monitoring of detection rates, false positive rates, and false negative rates
Trigger-based reviews when significant changes occur in the threat landscape
Independent review of model performance, separate from the team responsible for the model

Model Governance

APRA expects clear accountability for AI models. This means:

A designated model owner at senior management level
A model risk management framework that covers the full lifecycle
Board reporting on material model risks, including AI fraud detection performance
Documentation sufficient for APRA to understand and assess the model during supervisory reviews

Practical Compliance Checklist

For insurers implementing or reviewing their deepfake detection capability against APRA expectations:

Governance

Board-approved AI governance framework that covers deepfake detection
Designated senior management accountability for the AI fraud detection model
Regular board reporting on model performance and material incidents

CPS 230 Compliance

Deepfake detection included in critical operations register (if applicable)
Tolerance levels set for detection system disruption
Third-party provider due diligence completed and documented
Exit strategy and substitutability plan in place for detection provider
Business continuity plan covers AI detection system failure scenarios

CPS 234 Compliance

Data classification completed for all data processed by detection systems
Encryption in transit and at rest for biometric and claims data
Access controls and audit trails implemented
Vulnerability assessment includes adversarial AI attack vectors

Model Risk

Model validation completed, including demographic bias testing
Ongoing monitoring regime established with defined metrics and thresholds
Independent review process in place
Model documentation sufficient for regulatory review

Data Management

Data quality standards defined for input and output data
Data lineage documented from evidence capture through to claims decision
Data retention and disposal policies aligned with Privacy Act and APRA requirements

APRA’s Evolving Posture on AI

APRA’s approach to AI regulation is principles-based rather than prescriptive. The regulator has consistently stated that it does not intend to stifle innovation, but expects regulated entities to manage the risks of new technologies within existing frameworks.

However, the trajectory is toward increased scrutiny. APRA’s 2025 Corporate Plan flagged artificial intelligence as a supervisory priority, and thematic reviews of AI governance practices across general insurers are anticipated. Insurers that cannot demonstrate robust AI governance during these reviews face supervisory action, including potential conditions on their license.

The message is clear: deploy deepfake detection if you need it — and given the current state of deepfake fraud in insurance, you almost certainly do — but do so within a framework that APRA can examine and trust.

Preparing for Regulatory Engagement

When APRA engages with an insurer on AI fraud detection, it will typically seek to understand:

Why the insurer chose to deploy (or not deploy) deepfake detection
How the technology was selected and validated
What governance surrounds the technology’s use in claims decisions
Who is accountable for the technology’s performance and outcomes
Whether the insurer can demonstrate fair treatment of policyholders

Insurers should be prepared to walk APRA through the complete lifecycle: from the business case for deepfake detection, through vendor selection and model validation, to ongoing monitoring and incident management. Documentation is not optional — it is the primary evidence of compliance.

Conclusion

APRA’s expectations for AI fraud detection in insurance are not ambiguous. They are embedded in existing prudential standards — particularly CPS 230, CPS 234, and CPG 235 — and reinforced through supervisory engagement. Insurers deploying deepfake detection technology must treat it as they would any critical operational capability: governed, monitored, tested, and accountable.

The cost of non-compliance extends beyond regulatory sanctions. An insurer that deploys AI fraud detection without adequate governance risks both legal liability for errors and reputational damage that no amount of technology can repair.

For insurers seeking a deepfake detection solution designed with regulatory compliance in mind, deetech’s platform includes built-in audit trails, explainable AI outputs, and documentation packages aligned with APRA’s expectations.

This article is for informational purposes only and does not constitute legal, regulatory, or compliance advice. Insurers should consult qualified legal and compliance professionals for guidance specific to their circumstances and jurisdiction.