From Detection to Prevention: AI for Government Fraud, Waste and Abuse​

Fraud, waste, and abuse (FWA) remain a major global challenge for public sector organisations, with estimates suggesting up to 16% of government budgets may be lost annually. At the same time, fraud risks are becoming more complex, with increasing use of AI-enabled schemes. Yet for many agencies, the challenge is no longer only about finding more suspicious cases, but also how to prioritise, manage, and resolve the growing volume of cases already entering the system.

 

Despite significant investment in controls and audit mechanisms, many agencies still struggle to detect and prevent FWA. Fragmented data systems, legacy infrastructure, and siloed operations limit visibility, while traditional rule based and manual approaches struggle to keep pace with evolving fraud tactics, resulting in delayed detection and financial leakage that is often difficult to recover. Only 1 in 10 public sector organisations believe they currently have the tools and resources needed to effectively combat fraud.

 

This webinar will explore how data, AI, and advanced analytics can help governments to manage FWA more effectively, not only by identifying risk, but by enabling prioritise cases, automate audit and collection workflows, and guide taxpayers or beneficiaries toward voluntary compliance. It will highlight practical applications such as case prioritisation, workload management, anomaly identification, network analytics, automated audit support, and data-driven compliance nudges, alongside the importance of strong data foundations, governance, and ethical considerations.

 

Participants will gain insight into how governments can shift from reactive investigations to proactive prevention and smarter resolution, strengthening operational efficiency and enabling a more intelligence-led approach to tackling FWA.

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Key Takeaways: 

• Demonstrate how modern data capabilities and AI-driven approaches enable governments to shift from reactive fraud detection to proactive, intelligence-led prevention and more effective response to evolving FWA risks.  

• Raise awareness among public sector leaders of the increasing complexity of external and internal fraud challenges, including fragmented data systems, legacy infrastructure, and rapidly evolving fraud tactics that require new analytical approaches.  

• Engage government agencies and decision-makers exploring modern fraud detection and investigation capabilities, while highlighting practical use cases and real-world applications of AI in improving integrity, efficiency, and trust in public services.