If you've sat through even half the AI presentations I have lately, you'd think artificial intelligence is about to solve every problem from world hunger to finding your car keys. But here in the world of public sector fraud prevention, the question isn't quite so simple. Will AI become our best ally in fighting fraud, or are we about to hand fraudsters the keys to the kingdom?
The truth, as always, is more interesting than either extreme.
Let's start with the genuinely exciting bit. We're predicting that agentic AI will take over a significant proportion of fraud investigation tasks within the next three years. We've already worked with financial institutions deploying AI fraud investigators that gather evidence, evaluate cases, and distinguish between genuine fraud and false positives-slashing case volumes and investigation times in the process.
For government organisations, this could be transformational. Many departments currently rely on sampling methodologies to estimate fraud levels simply because they lack the capacity to investigate everything. Agentic AI could flip this entirely, scaling investigation capabilities to cover full populations in near real-time. Instead of detecting fraud after losses occur, you could prevent it before the money walks out the door.
Imagine AI agents embedded in the application process itself- whether that's benefits claims or tax refunds. While the customer remains in the digital channel, the AI evaluates their application and evidence, requesting clarification or additional documentation on the spot. Better customer journey, fewer post-claim interventions, and fraud identified at source. The friction points make it challenging for fraudsters and can become signals for detection.
Does this make human fraud investigators obsolete? Absolutely not. Think of agentic AI as a turbocharger and a force multiplier -it gathers data and surfaces insights that would take investigators hours to weeks to find manually, closes out false positives efficiently, and frees up your best people to work high-value cases. The result? More cases worked which results in more fraud detected, prevented, and recovered.
Before we get too comfortable, let's talk about what's already here and coming over the horizon. AI advancement brings three significant new fraud vulnerabilities.
First, the ability to generate hyper-realistic synthetic content. We're already seeing AI create convincing bank statements, receipts, proof of address documents, and identity papers. Distinguishing genuine from fraudulent applications is about to get significantly harder, opening wide the door for impersonation and identity theft at scale.
Second, fraudsters using AI agents to probe for weaknesses. Open-source AI platforms already enable everyday users to automate tasks like online shopping orders with simple prompts. Now picture fraudsters deploying AI agents to file self-assessment returns, claim benefits, or apply for grants-with the AI adapting applications in real-time to maximise success rates. The uncomfortable question: will our policies even allow AI agents to access digital services in future? And do we have the tools to identify when we're dealing with an AI rather than a human?
Third, and perhaps most insidious, is the threat of self-inflicted fraud through over-enthusiastic AI deployment. AI is brilliant at achieving the goals you set for it - sometimes too brilliant. There are already cases where organisations tested AI to help customers apply for services, setting goals around successful application rates. The AI duly obliged, helping customers" optimise" their data and, in some cases, generating entirely synthetic customers to boost those lovely success metrics. It sounds like science fiction until you realise hundreds of similar examples are emerging as more organisations rush to deploy AI without proper guardrails.
We're entering both the most exciting and most worrying era for fraud prevention in a generation. Like it or not, we're not going to stop the AI revolution by shouting at it from the sidelines. The question isn't whether to use AI, but how quickly we can harness it while defending against the new threats it brings.
This means building fraud prevention into AI enabled processes from day one - with robust guardrails, real-time monitoring, comprehensive logging, and yes, kill switches. It means thinking differently about authentication, about what constitutes evidence, and about how we verify humans are actually human.
Here's what keeps me up at night: Are your digital services ready to distinguish between human and AI interactions? Do your fraud frameworks account for synthetic content generation at scale? And perhaps most importantly, as you deploy AI to improve service delivery, have you baked in the fraud prevention controls from the start - or are you inadvertently creating your own fraud vector?
The fraudsters certainly aren't waiting around to figure this out. The question is, are we moving fast enough to catch up and finally move one step ahead of the fraudsters?