Automation is a force multiplier for your compliance team, not a replacement plan. BIAS AND BLIND SPOTS AI reflects the biases in its training data: • Under-represented groups may be missed or unfairly targeted. • Media sources or sanctions lists can encode geopolitical bias. • Analyst behavior, like clearing alerts faster for familiar customer types, can reinforce skewed patterns. These issues are harder to spot in opaque models, making governance reviews essential. MISSED RED FLAGS AI models only know what they’ve seen before. Emerging typologies like crypto off-ramps can evade detection. Human oversight is essential for recognizing novelty and interpreting real-world context. AMPLIFIED ERRORS Faulty inputs or logic scale quickly in AI systems. A single mis-weighted variable could freeze hundreds of accounts or overlook major fraud before anyone notices. REGULATORY RESPONSIBILITY The OCC and FinCEN have made it clear: You own your AI’s outcomes. Institutions must validate, document and explain model behavior. “The algorithm did it” won’t satisfy an examiner. AML TASKS TO KEEP IN HUMAN HANDS Automation is a force multiplier for your compliance team, not a replacement plan. These critical functions should remain human-led: 1. Setting Risk Appetite: Only the board and senior leadership can define acceptable levels of residual AML risk. AI can enforce thresholds, but deciding what those thresholds should be belongs in boardroom minutes, not model settings. 2. Designing Customer Risk Scores: AI can crunch data but can’t make value judgments. For example, should cash volume or political exposure carry more weight? That’s a question of ethics, strategy and regulatory expectations. 3. Clearing Alerts: Models can cluster alerts or assign “likely benign” scores, but a human must make the final call. Auto-closing alerts removes your ability to defend decisions in hindsight. 4. Finalizing SARs: AI can draft SARs by linking accounts and summarizing activity. But only a trained analyst can verify accuracy, add context and craft a clear, defensible narrative. 5. Model Governance and Tuning: Vendors may build the models, but you’re on the hook. That means validating data inputs, sanity-checking the math and signing off on all changes. 7 The Community Banker
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