Anti-money laundering ratings: uncovering evidence hidden in plain sight

Abstract

Purpose

This paper aims to increase the transparency of information in official anti-money laundering rating data to assist evidence-informed decision-making in compliance, policy-making and research.

Design/methodology/approach

This paper converts anti-money laundering rating data into information-rich visualisations, reintroduces a comparison methodology and ranks all anti-money laundering regimes evaluated to date.

Findings

Official anti-money laundering ratings as currently structured and presented offer surprisingly little policy-relevant information. Persistent failure to transform available data into information for knowledge and insight suggests that the risk has been realised that impressionistic judgments or politicised interests drive the policy agenda at least as much as objective evidence or substantive economic and social goals.

Practical implications

Any reluctance to generate policy-relevant information from the industry’s primary data set or disinclination to engage constructively with a growing body of independent critical policy effectiveness evidence calls into question whether implementing anti-money laundering controls with some prospect of achieving substantial societal benefits, or perpetuating the current system, prevails.

Originality/value

With a dearth of scholarship at the intersection of money laundering and policy effectiveness scholarship and practice, this paper combines elements of these disciplines and examines anti-money laundering effectiveness from a different viewpoint. Rather than seeking to measure money laundering or estimate the proportion of criminal proceeds successfully intercepted, this paper draws directly from the anti-money laundering industry’s own “main” data set.

Citation

Pol, R.F. (2019), "Anti-money laundering ratings: uncovering evidence hidden in plain sight", Journal of Money Laundering Control, Vol. 22 No. 4, pp. 836-857. https://doi.org/10.1108/JMLC-01-2019-0006

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