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Two challenges for today’s anti-money laundering professionals: focusing on high-value functions and eliminating false positives that consume unnecessary resources. On the latest episode of the ABA Banking Journal Podcast — sponsored by Franklin Madison — Nicholas Piccininni, who leads a 1,500-person financial crimes risk management team at Wells Fargo, explains how Wells puts technology to use to tackle these challenges. One question he asks: “Are we doing tasks [just]because we want to do those tasks, or are they adding value?” More data isn’t enough. AML professionals need to understand what’s driving higher Suspicious Activity Report totals — it could be the presence of more bad actors in the system, or it could just be a sign of a growing customer base. Piccininni discusses how Wells Fargo employs artificial intelligence and machine learning techniques in the financial crimes area, as well as its use of robotics applications “to pull data from across the company so [AML professionals] don’t have to go into 30 systems.” He also explores sanctions-related screening techniques that “eliminate false positives so we don’t have people going through reams of data.”