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Key Lessons Learned from The Basel AML Index 2021

Financial institutions around the world need to heed the warnings highlighted in The Basel AML Index 2021. The annual global study of 110 jurisdictions, which assesses money laundering risks around the world and ranks jurisdictions on how well they’re addressing them, found that the fight against dirty money remains a challenge. It also notes there is still too big a gap between promises to change behavior and actual results.

The Basel AML Index risk scores are based on data from publicly available sources such as the Financial Action Task Force (FATF), Transparency International, the World Bank, and the World Economic Forum. Each jurisdiction is scored out of 10, with 10 being the highest risk possible.

The 10th Annual Basel AML index found that the average global money laundering risk score increased from 5.22 to 5.3 out of 10, as assessed across all 110 jurisdictions. Even among jurisdictions whose risk scores improved this year, none managed to improve by even one point out of 10. Half of the improvements were 0.3 of a point or less.

Haiti has the highest risk (8.49) for money laundering, while the landlocked European city-state of Andorra has the lowest (2.73). The Cayman Islands was also among the highest risk jurisdictions, with a score of 7.66, just ahead of Mozambique (7.71). China also scored poorly (6.7). Panama is rated at 6, Macao is 5.93, and the United Arab Emirates is at 5.91. Malta, a European Union Member State, is at 47th place, at 5.45. Hong Kong is in a medium-risk area of 5.2, while Japan fares slightly better at 4.99. Switzerland, the world’s largest offshore hub, ranks at 4.89; Singapore is at 4.65, the US is at 4.6, the UK is at 4.05.

The Latest Basel Index Focuses on Data Behind Four Key Trends

Overall, four areas of AML/CFT policy were flagged by the Index as urgently needing more attention:

  1. The latest data on how jurisdictions are responding to money laundering threats related to virtual assets does not bring good news. Most jurisdictions assessed or re-assessed in the last year have worsened their scores for technical compliance with FATF recommendations on virtual assets and virtual asset providers. Average compliance levels have dropped by 10 percentage points globally.
  2. The marked difference between the prevention of money laundering and enforcement was also flagged as a problem across most jurisdictions. Ineffective systems are the general rule, but jurisdictions consistently score worse for prevention than for enforcement.
  3. The implementation of beneficial ownership registers around the world was analyzed by the report. This is intended to shed light on how beneficial ownership transparency is directly related to the effectiveness of a jurisdiction’s AML systems and the essential role of these systems in preventing, detecting, prosecuting, and sanctioning financial crimes. The report shows how slow and ineffective implementation of beneficial ownership transparency measures continues to provide safe havens for dirty money. This undermines global efforts to combat money laundering.
  4. Money laundering and terrorism financing vulnerabilities beyond the financial sector were also flagged by the Index. It made note of the generally weak application of AML/CFT preventative measures by lawyers, accountants, real estate agents, and other designated non-financial businesses and professions non-financial entities (DNFBPs). As such, there is a significant risk that businesses and professions like these remain open to abuse by criminals and corrupt individuals wishing to launder their money. Regulators are concerned that some DNFBPs are advising and assisting criminal clients with hiding and laundering illicit funds. They are also concerned that accountants are being used as intermediaries to avoid scrutiny.

How Financial Institutions Need to Respond

The key takeaway for financial institutions from the Basel AML Index is that none should rely on jurisdictions for strong action and guidance in the global fight against money laundering. As things stand, it is essentially a case of each to its own – especially when dealing with high-risk countries. To reduce exposure to risk and costly penalties, the financial services industry needs to responsibly put systems in place to expose entities who may be laundering money.

Historically, financial institutions have relied on ineffective rules-based monitoring systems that lead to excessive false positive alerts requiring expensive and time-consuming manual investigations. Loopholes created by the inconsistent application of global standards are being exploited by criminals, which makes matters worse. AML programs that simply check boxes are fast becoming outdated and highly ineffective when it comes to preventing financial crime and driving bad actors out of the global banking system.

It makes sense then that the U.S. government and regulators around the world are promoting technological innovation for AML purposes. Innovation and automation clearly offer a path to improvement. Advanced technologies like RPA and machine learning are improving efficiencies and reducing costs. Adjudication and reporting consistencies are being improved by automation, and financial institutions that deploy these new technologies report better compliance and reduced risk.

QuantaVerse was founded to effectively address the serious global problem of financial crime. In 100% of the programs where the QuantaVerse platform was applied – it identified suspicious activity that was missed by TMS and existing processes.

QuantaVerse helps companies manage financial crime risks more effectively and affordably using powerful AI, machine learning, and data analytics tools. QuantaVerse puts data to work — both structured and unstructured data from internal and external sources — serving up detailed, actionable insights that are turning the tables on financial crime and the criminals behind it.

To learn more about QuantaVerse’s AI-powered AML solutions and how you can automate your financial crime investigations, please visit: