How AI Could Be Your Biggest Audit Blind Spot

Hi everyone,   

When we think of financial fraud, most of us picture dishonest employees, shady suppliers, or loopholes in financial controls. But the uncomfortable truth is that fraud is evolving, and our frameworks haven’t kept pace.   

Fraud frameworks were built for a world where people made errors and people tried to hide them. But now? We’re stepping into uncharted territory. 

As firms adopt AI in audits and finance, new blind spots will emerge and may prove costlier than human fraud ever was. 

Biased Outputs Are the Silent Fraud Enabler 

Every AI system learns from data. If that data is incomplete, biased, or even intentionally manipulated, the outputs will reflect those flaws.  

The problem is that these distortions don’t always appear as fraud on the surface, but they can quietly enable it.  

Why this matters: 

• If an AI audit tool has been trained mostly on low-risk geographies, it might underplay anomalies in high-risk regions.  

• If historical fraud cases weren’t captured in the training set, the model may consistently flag only minor deviations while missing the sophisticated ones.  

This creates a dangerous situation: firms believe they’re safer because they “use AI,” but they’ve automated the blind spots. 

What firms should do: 

• Audit the AI, not just the numbers. Treat models like financial controls as they need regular, independent validation.  

• Test against edge cases. Don’t just feed the AI “normal” data. Challenge it with atypical, high-risk scenarios to see how it responds.  

• Keep human scepticism alive. AI can suggest, but auditors must still question. Trusting the system blindly is the biggest risk of all.  

   

Manipulated Models = New Insider Threat 

Traditional fraud frameworks worry about insiders overriding financial systems or bending processes to their advantage.  

AI introduces a new dimension: tampering with the model itself. This can take many forms. Data poisoning involves injecting misleading data into the training set, so the system “learns” to normalise fraudulent patterns.  

Model drift happens when gradual, unmonitored changes reduce the model’s ability to detect anomalies over time. And in generative models, prompt injection can be used to make the AI ignore controls or even reveal sensitive information.  

How manipulation can happen: 

• Data poisoning: malicious actors inject flawed or misleading data during the training stage, so the AI learns to “normalise” fraud.  

• Model drift: subtle adjustments over time can shift the AI’s sensitivity, making it less likely to flag risks it once did.  

• Prompt injection (in generative models): feeding malicious prompts that cause the AI to reveal sensitive data or ignore controls.  

A poisoned model still produces outputs that look clean on the surface. By the time anyone notices, losses can already be significant. 

Actionable steps: 

• Introduce AI governance committees. Just as firms have audit committees, models need oversight at board level.  

• Track versioning rigorously. Firms should know exactly which model version is in use at any time and what changes were made.  

• Simulate attacks. Cybersecurity isn’t just about firewalls anymore. Firms need “red teams” that actively test for AI manipulation. 

Audit Blind Spots Are Growing 

AI is marketed as a way to close audit blind spots, but in reality, it often creates new ones. Why? Because most auditors don’t fully understand the systems they’re being asked to sign off on. 

If this continues, audit quality could decline rather than improve. The solution is twofold:  

• First, firms must invest in AI literacy across audit teams, ensuring professionals understand how models are trained, where bias creeps in, and when outputs can’t be trusted.  

• Second, hybrid models of auditing need to be embraced, where AI handles the routine checks but human judgement is applied to context and exceptions.   

Over time, as firms expand AI into more complex areas, scope creep sets in, but oversight structures rarely expand to match. 

What firms must prioritise: 

• AI literacy for auditors. Every professional involved in audits needs at least a basic understanding of how models work, where they fail, and how bias creeps in. 

• Hybrid audit models. AI should be used as a support tool, not a replacement. The best results will come from combining machine efficiency with human judgement. 

• Redefine assurance frameworks. Audit methodologies must also evaluate the integrity of the tools that generate the numbers. 

   

Where Samera Fits In 

With Samera.ai, we’ve been asking hard questions about where AI is taking finance. 

Right now, we’re piloting it inside our own firm first, precisely because we want to understand the risks and opportunities before taking it further. 

If you’re running a firm, here’s my advice: don’t wait until regulators catch up. Start thinking now about how AI is reshaping fraud risk and what controls you need in place.  

👉 See what we’re building with Samera.ai and book a discovery call to explore how these ideas could shape your firm’s future. 

 Cheers, 

Arun