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Cut regulatory risks and costs by unleashing hidden metrics

Ronald F. Pol ( is an outcomes scientist at and

The global anti-money laundering system “has serious structural flaws,” according to The Economist,[1] citing my research describing conceivably the world’s least effective anti-crime policy experiment.[2]

Regrettably, the Economist article rehashed common “solution” tropes. Demanding more data, sharing it more widely, pressing politicians to extend anti-money laundering laws, and blaming banks for not properly implementing money laundering controls are convenient fictions obscuring serious problems with the system itself. Moreover, David Lewis, executive director of the Financial Action Task Force—the global anti-money laundering standard setter—recently declared that international standards are “good enough” (thereby excluded from a so-called “major” review), despite decades of contrary evidence in scientific journals. Lewis insisted that everyone just needs to continue implementing “the standards we already have.”[3]

Notwithstanding serious problems repeatedly overlooked, leadership for outcomes—in individual banks, other regulated firms, and countries— potentially offers a different future: slashing regulatory risk, compliance costs, and crime, even as the global anti-money laundering juggernaut races toward the impossibility of zero impact and infinite costs seemingly built into its design.[4]

So, while compliance teams assiduously gather all the data demanded by regulators, what they are not looking for – because the system’s built-in metrics overlook it – can amount to potentially significant, persistent, unknown regulatory risk, but this negative can be turned into a positive. In other words, if what isn’t being measured hides unknown risk, it can also help unlock new ways to demonstrate value, improve outcomes, and cut risk and costs.

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