{
  "title": "The Impact of Gray-Listing on Capital Flows: An Analysis Using Machine Learning",
  "publication": "IMF Working Papers, May 27, 2021",
  "sourceUrl": "https://www.imf.org/en/publications/wp/issues/2021/05/27/the-impact-of-gray-listing-on-capital-flows-an-analysis-using-machine-learning-50289",
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  "summary": "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).",
  "sections": [
    {
      "heading": "Summary and main finding",
      "content": "- 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).\n- This paper estimates the magnitude of the effect of gray-listing on a country’s capital flows using an inferential machine learning technique.\n- It finds that gray-listing results in a large and statistically significant reduction in capital inflows."
    },
    {
      "heading": "Methodology",
      "content": "- Analytical approach: inferential machine learning technique applied to measure the impact of gray-listing on capital flows.\n- Focus: estimating magnitude of effect on capital inflows attributable to gray-listing."
    },
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      "heading": "Policy relevance and audiences",
      "content": "- Findings are of interest to policy makers, investors, and the Fund.\n- 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."
    },
    {
      "heading": "Keywords and topics emphasized",
      "content": "- AML/CFT\n- analysis using machine learning\n- Anti-money laundering and combating the financing of terrorism (AML/CFT)\n- Capital flows\n- capital flows\n- Capital inflows\n- coefficient estimate\n- emerging market economies\n- Foreign direct investment\n- Global\n- gray list\n- gray-listing affect\n- inferential machine learning technique\n- machine learning\n- Machine learning\n\n---\n\n Content in this bundle\n\n- Working Paper\n  - Working Paper (Markdown version){rel=\"alternate\" type=\"text/markdown\"}\n  - Working Paper (PDF){rel=\"external\" type=\"application/pdf\"}\n\n---\n\nSource: https://www.imf.org/en/publications/wp/issues/2021/05/27/the-impact-of-gray-listing-on-capital-flows-an-analysis-using-machine-learning-50289"
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    "Authors: Mizuho Kida, Simon Paetzold",
    "Published: May 27, 2021",
    "Series: IMF Working Papers",
    "DOI: https://doi.org/10.5089/9781513582436.001",
    "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.",
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