As financial institutions accelerate the adoption of generative AI across compliance functions, one question continues to dominate internal governance discussions: what standards of explainability and auditability are required before compliance teams can confidently sign off on GenAI-driven decisions?
While the technology itself may feel novel, many compliance leaders argue that the underlying expectations are not radically different from those applied to human decision-makers, a point underlined by Areg Nzsdejan of Cardamon in a recent LinkedIn post.
At its core, compliance has always required accountability. Whether a decision is made by an analyst, a manager, or an automated system, organisations must be able to explain why a particular outcome occurred. In that sense, the bar for GenAI is surprisingly familiar. Compliance teams need to understand the reasoning behind decisions clearly enough to defend them internally, to auditors, and to regulators if challenged.
For GenAI systems, this means every material output must be traceable back to a clear rationale. Decisions should not simply appear as final answers, but as outcomes supported by transparent reasoning steps and, where possible, citations to underlying source material. Without this traceability, institutions risk deploying systems that generate outputs which cannot be justified after the fact — a scenario that introduces unacceptable regulatory and operational risk.
What consistently fails to meet compliance expectations is a black-box approach. Models that produce confident-sounding results without offering insight into how those results were reached are fundamentally incompatible with regulated environments. Compliance teams cannot approve decisions they are unable to interrogate, particularly when those decisions affect customer outcomes, risk classifications, or regulatory reporting obligations.
Crucially, explainability does not stop at the AI model itself. Effective auditability must also capture the full decision chain, including human interventions. When a GenAI system flags a risk, suggests an action, or drafts an assessment, organisations need visibility into who reviewed that output, who approved it, when that approval occurred, and why the final decision was taken. Without this context, accountability gaps quickly emerge.
This growing focus on end-to-end audit trails reflects a broader shift in how compliance teams evaluate AI adoption. Rather than asking whether GenAI is innovative or efficient, the priority is whether it can be governed to the same standard as existing compliance processes. Systems that embed explainability, version control, decision logs, and review workflows from the outset are far more likely to gain internal approval.
As regulators globally increase scrutiny of AI-driven decision-making, expectations around explainability and auditability are only set to rise. For financial institutions, the message is clear: GenAI can play a meaningful role in compliance, but only if it operates in a way that compliance teams can understand, question, and ultimately stand behind.
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