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- Global economies can save $3.13 trillion annually using AI to detect and prevent money laundering and terrorist financing
- Napier AI / AML Index provides insights into 35 countries, highlighting which are stronger and weaker when it comes to stopping money flowing into the shadow economy
- Money laundering cost the global economy $5.2 trillion in 2023 alone
- Italy, the Czech Republic and Finland rank at the top, respectively, in terms of countries with the most effective spend on anti-money laundering
Global economies can save $3.13 trillion by using AI to detect and prevent money laundering and terrorist financing, which costs trillions of dollars annually, according to the inaugural Napier AI / AML Index. It was published this week by Napier AI in partnership with GlobalData, a leading data and analytics company.
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Industry’s most comprehensive insights
The Napier AI / AML Index provides the industry’s most comprehensive insights into the impact of AI on anti-money laundering and counter-terrorist financing (AML/CFT), it ranks 35 global markets based on their effectiveness in financial crime compliance.
Among the report’s many key findings:
- North America, the Nordics and Central Europe rank at the top, respectively, in terms of geographic regions with the smallest percentage of GDP lost to money laundering.
- Italy, Poland and the Czech Republic rank at the top, respectively, in terms of countries with the most efficient spend on anti-money laundering.
- Regulated firms like banks, payments firms, wealth & asset managers, telcos and insurance companies can save $138 billion USD on compliance costs by implementing AI into their AML strategies.
- 5% of global GDP—roughly $5.2 trillion—is funneled into the black market through money laundering.
- Worldwide, the US stands to gain the most from AI-powered financial crime compliance solutions, potentially saving financial institutions $23.4 billion on compliance costs, followed by Germany ($14.2 billion), and France ($11.08 billion).
- The United Arab Emirates has the highest GDP loss to financial crime globally, at 9.32%, followed by Brazil at 8.74%.
- While financial institutions spend heavily on crime compliance, the investment is justified when it recovers billions and strengthens safeguards. The focus should be on spending effectively, using AI tools to boost both compliance and economic recovery.
Technology can drive monumental change in financial crime prevention
Dr. Janet Bastiman, Napier AI’s Chief Data Scientist, says: “Financial hubs are much more vulnerable to financial crime. Mature economies like Canada, the US and UK have effectively balanced open banking and AI innovation with the cost of managing financial crime risks. Fast-growing economies with strong financial services industries are looking to find this balance to reduce financial losses to the black market.”
Greg Watson, Napier AI’s CEO, says: “We are at the tipping point where technology can drive monumental change in financial crime prevention. At Napier AI, we see AI not only as a tool but as a catalyst that can enhance compliance efforts globally, delivering better results with greater efficiency. The numbers from this year’s Index are a testament to what is achievable when AI is effectively AML, in what we call a compliance-first approach.”
Sam Murrant, Consulting Director at GlobalData, says: “One of the key findings in our research was that AML compliance, while seen almost unanimously as essential, is generally inefficient. It is clear from the findings of this report that there is huge potential to improve AML processes. According to our model, implementation of technological solutions to save valuable time among the experts working in AML can save banks billions annually in compliance costs alone.”
The Napier AI / AML Index uses GDP data, criminality data from UNODC and the Organized Crime Index, and compliance effectiveness from FATF and the Basel Index. It also includes insights from senior executives on the impact of AI on AML strategies.
Read the index here: https://www.napier.ai/ai-aml-index
