The Inherent Risks of AI in Alert Disposition
AI promises to make sanctions screening faster and more efficient by helping compliance teams review and close alerts. But what happens when AI confidently close the wrong alert, or when the screening system fails to generate an alert at all.
These risks are why regulators remain cautious about where and how AI is used in the screening process. Current regulatory guidance, states that AI should not serve as the initial, primary screening solution. Because of the inherent “black box” nature of complex neural networks, regulators require that primary screening be deterministic and rules-based. AI is instead strictly relegated to the second line of defense, alert disposition. Relying on AI to triage and auto-close alerts introduces its own set of distinct vulnerabilities that compliance teams must navigate.
The Explainability Mandate
Regulators require full explainability of why an alert was dismissed. Explainable AI (XAI) attempts to bridge this gap, but mapping the complex logic of a deep learning model into a human-readable audit trail remains a significant technical challenge.
Confidently Wrong
Large language models and advanced AI classifiers are prone to hallucinations. They can generate highly convincing, yet entirely fabricated, justifications for dismissing a legitimate sanctions match.
The “Aims to Please” Problem
AI models often suffer from objective misalignment. If a model is trained to optimize for efficiency and reduce the alert queue, it may develop a bias toward closing alerts, aiming to “please” the operational metric at the expense of strict compliance.
Data Drift
AI models are static snapshots of the data they were trained on. As payment typologies, evasion tactics, and global sanctions lists evolve, a model can lose its accuracy over time if not constantly retrained and validated.
The Ultimate Flaw: AI Cannot Close What Was Not Alerted
While the flaws of AI in alert disposition are significant, they pale in comparison to the architectural flaw of pairing AI with an inadequate legacy screening engine.
AI cannot close what was not even alerted in the first place
If a legacy screening system fails to read a free-text field in an ISO 20022 payment, or drops a sub-element containing a sanctioned entity’s name, no alert is ever generated. The downstream AI engine, no matter how advanced, explainable, or perfectly calibrated, never sees the transaction.
The industry is currently spending millions attempting to perfect AI for alert disposition, while ignoring the fact that the initial net is torn. Upgrading to ISO 20022 without simultaneously upgrading the primary screening engine to natively parse and screen every mandatory and free-text field is a recipe for catastrophic compliance failures. Institutions must ensure their primary screening layer is catching everything correctly before they rely on AI to clean up the rest.
