As generative AI moves from experimentation to production across financial services, compliance leaders are facing a pressing question: what standards of explainability and auditability must be met before they approve the use of GenAI in live environments?
For many firms operating in highly regulated sectors such as FinTech, InsurTech, WealthTech and RegTech, the issue is no longer theoretical. It is operational, reputational and regulatory.
At its core, the benchmark for AI is not radically different from the one applied to human colleagues, stresses Cardamon CEO Areg Nzsdejan in a recent LinkedIn post.
Any decision that materially impacts a customer, a transaction or a regulatory obligation must be explainable. If a compliance officer cannot clearly articulate why a specific outcome was reached, that outcome should not stand. In other words, the bar for AI systems should mirror the expectations placed on trained professionals: decisions must be reasoned, defensible and reviewable.
For GenAI in particular, this means that every material output must be traceable to a clear rationale. Compliance teams need to understand not only what the system concluded, but how it arrived there. Where possible, outputs should be grounded in citations to underlying source material, whether that be internal policy documents, regulatory texts, transaction records or client data. Without this linkage, firms risk relying on outputs that cannot be substantiated during audits or regulatory reviews.
The concept of traceability extends beyond simple transparency. It requires structured logging, version control and a robust record of prompts, data sources and model configurations. If a regulator or internal audit function asks how a suspicious activity report was generated or why a customer was flagged as high risk, firms must be able to reconstruct the decision path. This expectation aligns closely with broader regulatory trends that emphasise governance, documentation and accountability in AI deployment.
What does not meet this standard is a black-box approach. Compliance teams cannot sign off on decisions they cannot interrogate after the fact. A system that produces outputs without clear reasoning, without reference to source material and without a documented decision trail poses unacceptable risk. In regulated environments, opacity is not innovation; it is liability.
Crucially, auditability must cover not only AI-driven decisions but also human interventions. If a compliance analyst overrides a model recommendation, that action must be logged. If a manager approves a flagged transaction, the record must show who approved it, when the approval took place and why the decision was justified. True governance in the age of GenAI is therefore a hybrid discipline: it requires oversight of both automated and human processes.
As GenAI becomes embedded across onboarding, transaction monitoring and regulatory reporting workflows, explainability and auditability will increasingly define whether a solution is viable for regulated use. Compliance sign-off will depend not on novelty or efficiency alone, but on whether decisions can withstand scrutiny long after they are made.
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