You have been in this profession long enough to know when something is off. A cluster of transactions that are each individually explainable but collectively strange. An access pattern that does not match the role. Expense claims that sit just below every relevant threshold, month after month, from the same team. The instinct is there. What is often missing is the time, the structure, and the analytical firepower to turn that instinct into something you can actually act on confidently.
This is the specific gap that AI fills for compliance and risk professionals – and it is more significant than most people realise until they experience it firsthand.
Start with what AI does to the investigation itself. When suspicious activity surfaces, the pressure to respond quickly can work against you. Moving too fast risks a poorly documented process. Moving too carefully while the data sits risks missing something time-sensitive. AI tools like Claude or ChatGPT allow you to move fast and rigorously at the same time, because they help you structure your thinking in real time without cutting corners on the analysis.
Here is a prompt built for exactly that moment – when you are looking at something that concerns you and you need to move from observation to structured response:
“I am a compliance and risk manager and I have identified a pattern of activity that I believe warrants further investigation. The pattern involves [describe what you are seeing in specific detail – for example: multiple internal expense claims from the same department, all falling just below our approval threshold of X, submitted consistently in the final three days of each month over the past six months]. I need you to help me approach this systematically. Please identify which specific fraud typologies or financial crime indicators this pattern is most consistent with, explain what additional documentation or data I should request to either confirm or rule out those typologies, describe what a well-structured and defensible initial investigation process would look like from this point, and highlight any regulatory expectations or internal governance considerations I should be factoring in as I decide whether and how to escalate.”
The output you get is not a decision – that judgement belongs to you, and that is how it should be. What it gives you is a rigorous framework assembled in under a minute, covering angles that might otherwise take an hour of research and internal discussion to surface. The professional expertise is yours. AI removes the blank-page problem and the time cost of structuring the analysis from scratch.
What makes this genuinely powerful is the second thing AI unlocks, which most compliance professionals have not yet tried: using it to test your controls before fraud tests them for you.
How Stress-Testing Your Own Controls Changes What You Are Able to Catch
Most compliance teams only find out their fraud detection process has a gap when something real slips through it. Red-teaming exercises – where you deliberately try to find the weaknesses in your own controls – are well-established in theory and underused in practice, largely because they require time, specialist input, and a structured approach that is hard to generate internally.
AI makes this genuinely accessible for the first time. Rather than waiting for an external review or an incident to expose your vulnerabilities, you can run a structured stress test yourself, in a single session, using a prompt like this one:
“I work as a compliance and risk manager at a [insert your sector, for example: regional bank / insurance company / investment firm / retailer with a large accounts payable function]. I want to run a red-teaming exercise on our current fraud and suspicious activity detection processes. Please generate five detailed and realistic scenarios in which a bad actor – whether an external fraudster or an internal employee – could attempt to commit fraud or engage in suspicious financial activity within a firm like mine. For each scenario, describe the method they would use, the specific behavioural or data signals that should theoretically alert a compliance team, which controls or monitoring processes should catch it, and where realistic gaps or blind spots in a typical compliance function might allow it to go undetected. I want to use this to assess how well our current controls would hold up and where we should be focusing our attention.”
Running through the results of a session like this with your team creates something valuable and durable: a shared, specific vocabulary for talking about fraud risk that is grounded in your actual sector, your actual exposure, and the real gaps in your real processes. It also produces documentation. A well-structured red-teaming output is the kind of evidence that demonstrates genuine proactive governance – not just to internal stakeholders, but to regulators who want to see that your compliance function is doing more than reacting.
What Compliance and Risk Managers Who Build This Skill Actually Gain
There is a version of this work where fraud detection is a reactive, under-resourced scramble. And there is a version where your team catches patterns early, investigates with structure and confidence, and stress-tests its own controls regularly enough that gaps get found internally rather than externally. AI does not magically deliver the second version – but it makes it genuinely achievable for a compliance professional who is willing to learn how to use it well.
If this kind of hands-on, practical application appeals to you, the AI for Compliance & Risk Managers course at the Workplace AI Institute is where to go next. It is designed specifically for professionals in your role, covers a wide range of real-world applications, and requires no technical background whatsoever – just the expertise you already have and the willingness to use it differently.
