By Sameer Kumandan, Managing Director at SW360
Artificial intelligence is moving steadily from helping businesses make decisions to making more of them. It can assess applications, prioritise cases, identify unusual behaviour, score risk and determine which customers, suppliers or transactions require further scrutiny. For businesses under pressure to make faster decisions at scale, the appeal is obvious.
But as AI takes on more responsibility, a difficult compliance question is emerging: who is accountable when the machine gets it wrong?
For years, the compliance conversation has focused on whether organisations are performing the right checks and whether they can demonstrate that they have done so. AI changes the question. It is no longer enough to ask whether a decision was made using the right information. Businesses increasingly need to ask whether they can understand, explain and defend the decision an AI system has made. That distinction matters.
An automated system may flag a customer as high risk, reject an application or escalate a transaction based on patterns across thousands of data points. The outcome may be statistically justified, but that does not necessarily make it understandable to the person affected by the decision, or to a regulator asking why it happened. This creates a new compliance challenge: explainability must keep pace with automation.
The risk becomes greater as AI systems become more autonomous. If a human makes a decision, there is usually an identifiable chain of responsibility. When an AI system makes a recommendation, triggers an action and potentially learns from new information over time, that chain can become considerably less clear. Organisations cannot simply say, “the system made the decision.” The responsibility still sits with the business.
For businesses, this means putting a few basic guardrails around AI-driven decisions. Before an automated outcome can be trusted, four questions should be answerable:
- What information informed the decision?
- How was the decision reached?
- What happens when it is challenged or turns out to be wrong?
- And, ultimately, who owns the outcome?
The first question may be the most overlooked. Many organisations already hold vast amounts of information across customer records, company data, identity information, credit checks, transactions, supplier records and internal risk assessments. But having more data does not automatically produce better intelligence. If that information is fragmented, outdated or disconnected, AI can simply make decisions faster from an incomplete picture.
This is where data quality becomes a compliance issue. An organisation cannot meaningfully explain an AI decision if it cannot establish what information informed it in the first place. Nor can it confidently defend an outcome if different systems hold conflicting or incomplete versions of the same customer or counterparty.
The answer is not necessarily to slow automation down. It is to build greater visibility around it. Businesses need to know what data is feeding their AI, how that information is being interpreted, where decisions require human oversight and whether there is an auditable trail when something goes wrong.
AI can make decisions at machine speed, but accountability still moves at human speed. The organisations that recognise this will be better positioned to use automation without losing control of the decisions it makes.
As AI becomes increasingly embedded in compliance, the question should therefore move beyond, “Can we automate this decision?” The more important question is: “If we have to defend this decision tomorrow, can we explain exactly how it was made – and stand behind it?” That is the real test of responsible AI in compliance.
