This award was sponsored by KBS 

L-R: International IAATI 1st VP, Rusty Russell and Janeen Cole, IAG
 

Building an Intelligence-Led, Typology-Driven Fraud Capability at Insurance Australia Group (IAG)

2025 IAATI Awards Submission 
Category: Insurance Industry Investigation of the Year 

Insurance Australia Group (IAG) is at the beginning of a transformative journey led by the Intelligence team within the Counter Fraud & Intelligence Unit. We are initiating efforts to fundamentally redefine how the insurance industry approaches fraud detection and prevention. Historically, the focus has been on claims fraud, with systems designed to detect anomalies at the point of claim. While this has been somewhat effective, it has also left significant blind spots across the insurance value chain and customer journey and has struggled to keep up with the evolving scale, speed, and sophistication of modern fraud and financial crime schemes.

Our mission is to revolutionize IAG’s approach by shifting from reactive fraud detection to proactive fraud prevention. We are in the early stages of implementing an intelligence-led and typology-driven model that goes well beyond traditional claims fraud. By leveraging data and intelligence, we aim to identify emerging threats and trends, develop typologies to inform fraud rules and machine learning models, and drive prevention activities across the entire business.

With this transformation, IAG is beginning to build one of the most advanced fraud intelligence capabilities in the Australian insurance sector. This initiative aims to set a new benchmark for innovation, collaboration, and industry leadership.

Background & Context

Fraud remains one of the most significant non-weather-related financial risks to insurers, costing billions annually across the industry. The challenge is magnified by the increasing professionalisation of organised fraud groups, the integration of identity crime into fraud schemes, and the exploitation of new digital channels.Traditionally, insurance fraud detection has been claims-focused – identifying anomalies within the claims process and flagging suspicious cases for investigation.

While valuable, this approach is reactive and often fails to address fraud that originates earlier in the policy lifecycle (e.g., application fraud, account takeover, or ghost broking).

Recognising these limitations, IAG’s Counter Fraud & Intelligence Unit has initiated a transformation program to:

• Shift from reactive detection to proactive prevention.
• Expand the scope beyond claims fraud, developing typologies that capture threats across the insurance value chain and customer journey.
• Embed intelligence into every stage of fraud management – from emerging threat identification to operational disruption.
• Leverage advanced analytics, machine learning, and data partnerships to surface risks at scale.

This transformation aims to position IAG as a market leader in intelligence-led fraudprevention in Australia.

Our Intelligence-Led, Typology Driven Approach

At the heart of our transformation is the principle that intelligence must drive fraud prevention. This means moving beyond traditional fraud indicators to systematically identifying how fraud manifests – the behaviours, enablers, and signatures of fraudulent activity.

Key Elements of the Approach:

• Typology Development - We are building typologies that define the end-to-end structure of fraud threats – including enablers (e.g., compromised identities, mule accounts), modus operandi, and risk indicators. This allows us to move from broad “red flag” detection to precise, intelligence-informed targeting.
• Data-Driven Intelligence - By fusing internal claims, policy, and digital interaction data with external data sources, we aim to generate actionable intelligence. This intelligence not only detects known fraud but also surfaces emerging threats and trends before they are fully realised.
• Integration into Detection Systems - Our typologies will be operationalised through fraud rules, models, and advanced analytics integrated into IAG’s sophisticated fraud detection platform. This ensures real-time prevention rather than retrospective identification.
• Cross-Business Prevention Activity - Intelligence will inform decision-making across the enterprise, from underwriting and claims to digital security and customer identity management. Fraud prevention will no longer be siloed but embedded across IAG’s operations.

This approach aims to create a feedback loop: data → intelligence → typologies → detection → prevention → refined intelligence.

Outcomes & Industry Impact

Our intelligence-led capability aims to deliver measurable benefits:

• Expanded Scope of Fraud Detection - We aim to identify fraud typologies across applications, digital channels, repair networks, staged incidents, and identity crime – well beyond the traditional claims-only view.
• Proactive Disruption of Organised Fraud - Intelligence aims to enable us to disrupt organised fraud groups by surfacing links between claims, policyholders, and third parties, leading to early intervention and prevention.
• Enterprise-Wide Prevention - Typologies aim to inform prevention activity outside of claims, such as tightening digital identity verification, detecting mule accounts, and strengthening underwriting controls.
• Advanced Detection Models - Typology-driven data will be embedded into machine learning models within our fraud detection platform, increasing both the accuracy and efficiency of fraud identification.
• Industry Leadership - By sharing insights with industry partners, regulators, and law enforcement, IAG aims to contribute to the broader fraud prevention ecosystem in Australia. Our approach sets a benchmark for how insurers can evolve to meet modern fraud challenges.

Future Direction

Our vision is to continue advancing IAG’s fraud capability into an industry-leading, intelligence-led centre of excellence. Key priorities include:

• Automation & AI – Scaling typology-driven detection with advanced AI models to accelerate prevention and minimise false positives.
• Data Partnerships – Expanding access to external data sources, including cross-industry collaborations, to detect multi-sector fraud.
• Identity-Centric Protection – Building intelligence around compromised identities to counter the growing convergence of cybercrime and fraud.
• Continuous Threat Monitoring – Establishing horizon scanning capabilities to anticipate and prepare for emerging fraud threats.

Conclusion

Through its intelligence-led and typology-driven transformation, IAG’s Counter Fraud & Intelligence Unit is building a capability that aims to shape the future of fraud prevention in the insurance industry. By moving beyond traditional claims fraud, embedding intelligence across the enterprise, and driving proactive prevention, we aim to deliver an industry-leading service that aligns perfectly with the values recognised by the IAATI Forensic and Supporting Service Award.