Imagine a finance department where AI performs the invoice processing.
Another AI system identifies anomalies.
A third AI tool reviews transactions for potential fraud.
And an automated reporting platform produces the month-end analysis.
On paper, that sounds incredibly efficient.
Multiple layers of intelligent technology working together.
But here's an interesting question.
What happens when the systems checking each other share the same weaknesses?
Traditional financial controls were designed around segregation of duties.
Different people.
Different responsibilities.
Independent checks.
The objective wasn't simply to catch mistakes.
It was to reduce the risk that one error, assumption or individual could compromise an entire process.
As organisations introduce AI across multiple stages of a financial workflow, that principle becomes increasingly important.
Two AI systems don't necessarily provide independent assurance simply because they're separate applications.
They might rely on the same underlying data.
The same assumptions.
The same models.
Or the same incorrect interpretation of a transaction.
That doesn't mean AI shouldn't be used for financial controls.
Quite the opposite.
AI could significantly strengthen how organisations detect unusual activity, identify risks and monitor transactions.
But the design of those controls matters.
Automating a control doesn't automatically make it independent.
For finance leaders and auditors, this raises a question worth exploring.
As AI becomes responsible for both processing and checking financial transactions, how should we demonstrate that the controls remain genuinely independent?
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