The Impact of Gray-Listing on Capital Flows: An Analysis Using Machine Learning

IMF Working Papers, May 27, 2021

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The Financial Action Task Force’s gray list publicly identifies countries with strategic deficiencies in their AML/CFT regimes (i.e., in their policies to prevent money laundering and the financing of terrorism).
This paper estimates the magnitude of the effect of gray-listing on a country’s capital flows using an inferential machine learning technique.
It finds that gray-listing results in a large and statistically significant reduction in capital inflows.

Analytical approach: inferential machine learning technique applied to measure the impact of gray-listing on capital flows.
Focus: estimating magnitude of effect on capital inflows attributable to gray-listing.

Findings are of interest to policy makers, investors, and the Fund.
Subject areas covered: Anti-money laundering and combating the financing of terrorism (AML/CFT), Balance of payments, Capital flows, Capital inflows, Crime, Foreign direct investment, Machine learning, Technology.