The end-game of anti-money laundering operations will, logically, be a fully automated process whereby monitoring and screening, alert investigation and decisioning, and generation and filing of regulatory reports are executed without human intervention. This should be feasible within ten years, and perhaps sooner.
From our current viewpoint from within a paradigm that relies heavily on manual operations, automated AML may seem like a distant goal. But the reality is that we are already halfway there. To state the obvious, transaction monitoring and screening — the two main building blocks of AML — are performed by automated software engines. Although model building and tuning require significant and ongoing effort, once a scenario or filter is put into production the engines hum along, perform their analysis, and populate case management systems with the resulting alerts, risk scores, and associated transaction and customer data.
What is not automated today is the investigation of alerts once they’ve entered the case management system. And therein lies the rub, because the majority of alerts are false positives. False positive rates may be 70% or higher, and this immediately gums up the operation by draining resources from the crucial and value-added work of identifying true positives. High FP rates are the main driver behind the massive expansion of AML back- office teams, which have grown to be as large as small cities.
