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. 6. High-Impact Customer Actions: Freezing accounts or filing 314(b) requests affects real lives. AI can recommend — but humans must confirm and justify each step. 7. Explaining to Regulators and the Board: No algorithm can sit across from an examiner and defend itself. Your team must translate model logic into plain English, from feature weights to tuning rationales. BEST PRACTICES FOR COMMUNITY FIS To use AI safely and effectively in AML, community institutions should: • Use Explainable Models: Choose vendors that provide reason codes or variable weights so analysts can explain every decision. • Customize for Your Risk Profile: Tune models to reflect your institution’s size, market and product mix. • Keep Humans in the Loop: Let AI prioritize alerts, but reserve final decisions for trained analysts. • Validate Regularly: Conduct independent validation pre-launch, test after any material change and audit frequently. • Invest in Analyst Training: Run workshops on model interpretation and encourage staff to challenge or override model outputs when their gut says, “Dig deeper.” BRINGING IT ALL TOGETHER AI is fast becoming a standard part of AML programs, even for smaller institutions. When deployed thoughtfully, it can cut through noise, surface risk patterns and save staff hours of clerical work. But it must remain a co-pilot, not the one flying the plane. Community banks that strike the right balance will: • Adopt explainable, customizable hybrid systems. • Embed human review at all high-risk decision points. • Validate and document continuously. • Cultivate staff who understand both compliance and AI. Follow these steps, and you can get the best of both worlds: the speed of automation and the assurance of human oversight. Jessica Tirado is a product manager for CSI’s AML Solution. Working in AML is her passion, and she is excited to share what we have been working on over the past several months. Jessica has worked at CSI for three years and started her career here as a product manager for our Links Products in Meridian. Prior to coming to CSI she worked as a BSA/AML analyst for nearly six years at Commercial Bank. Automation is a force multiplier for your compliance team, not a replacement plan. 21 WEST VIRGINIA BANKER
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