Denmark Least Risky Country for Corruption and Bribery: Report

Get United Against Corruption. Photo: UNODC
Get United Against Corruption. Photo: UNODC

Denmark is rated the least risky country for corruption and bribery, followed by New Zealand and Singapore in a list of top 10 performers dominated by Nordic and northern European countries.

These are among the findings of the Basel AML (anti-money laundering) Index, which is an independent annual ranking that assesses the risk of money laundering and terrorist financing (ML/TF) around the world.

This risk category draws on Transparency International’s Corruption Perceptions Index and the TRACE Bribery Risk Matrix, which was newly added to the Basel AML Index this year as an indicator of business bribery risk.

The research suggests that the correlation between corruption and money laundering must be considered by governments when they seek to make their AML systems more effective. Ineffective anti-money laundering and counter financing of terrorism (AML/CFT) systems and lack of transparency are leaving the door open to increasingly sophisticated money laundering schemes.

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These are two findings of the 8th edition of the Basel AML Index, a research-based ranking that assesses countries’ risk exposure to money laundering and terrorist financing (ML/TF).

They may help to explain why countries generally considered “safe” for investment have been hitting headlines recently with high-profile money laundering scandals.

Published by the Basel Institute on Governance since 2012, Basel AML Index provides risk scores based on data from 15 publicly available sources such as the Financial Action Task Force (FATF), Transparency International, the World Bank, and the World Economic Forum. The risk scores cover five domains:

  • Quality of ML/TF Framework
  • Bribery and Corruption
  • Financial Transparency and Standards
  • Public Transparency and Accountability
  • Legal and Political Risks

The Public Edition of the Basel AML Index 2019 covers 125 countries with sufficient data to calculate a reliable ML/TF risk score.

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Rakesh Raman