Financial News
AI Is the Most Effective Method for Tackling Money Laundering Threats, According to AML Professionals
- New Feedzai research of anti-money laundering (AML) professionals unveiled the most significant threats and solutions to AML processes
- 46% state that increasingly sophisticated money laundering techniques, including generative AI technology, is one of the biggest threats their business faces
- 53% note that the majority of money laundering activity they encounter is linked to cryptocurrencies
- AI is the preferred solution to tackle financial crime, with 60% of respondents already seeing its positive effects in driving efficiencies in their AML processes and 51% viewing it as a critical element of future AML programs
AI is the most effective method for improving anti-money laundering (AML) processes and efficiencies according to AML professionals, research from Feedzai, a leading provider of financial crime and risk management solutions
Feedzai’s The State of Global Anti Money Laundering Compliance Report 2023 surveyed compliance professionals to discover what the biggest threats and opportunities are in the AML ecosystem. It found that 46% of AML professionals are concerned about increasingly sophisticated money laundering techniques, including generative AI. The threat from generative AI and linked technology is now the top challenge for AML professionals, overtaking regulation, cryptocurrency and blockchain, the top threats cited in 2022.
Despite no longer occupying the top spot, crypto still remains front of mind for AML professionals. Over half (53%) of those surveyed said that the money laundering activities within their scope are predominantly linked to cryptocurrency transactions. Cryptocurrencies' anonymous and decentralized nature is being exploited to conceal the origins of illicit funds, exacerbating the challenges in detection and monitoring.
As criminals utilize increasingly sophisticated technology, so too do those seeking to protect consumers. A third (33%) of AML professionals say that AI and machine learning are the most effective method for preventing money laundering. Of those already adopting AI, 60% said that the technology has already been effective in driving efficiencies in their organization’s AML processes.
Looking ahead, over half (51%) of respondents believe that an increased use of AI and machine learning is the future of AML and KYC (Know Your Customer) programmes. With the potential to instantly analyze huge data sets, AI is already helping investigators identify bad actors and diffuse threats. As a result of AI integration, we’ll see improved KYC processes, reduced false positives and also a reduced cost burden on compliance professionals.
Nick Parfitt, Principal AML SME at Feedzai said, “Fraudsters are capitalizing on fast-developing technologies to trick existing AML programmes, in many cases using generative AI to their advantage. Our latest report reiterates the demand from AML professionals for their organizations to adopt AI in combination with human insight as a defensive weapon to improve their compliance process and efficiencies.
“Banks and financial institutions are sitting on a wealth of data that they can use to better protect their customers and business. By taking a RiskOps approach, they can put this data to good use by creating a 360-degree view of customer risk that addresses the entire lifecycle of financial crime and compliance helping to stop criminals in their tracks before they can do any damage.”
About Feedzai: Feedzai is the world’s first RiskOps platform, protecting people and payments with a comprehensive suite of AI-based solutions designed to stop fraud and financial crime. Feedzai is trusted by leading financial institutions to manage critical risk and compliance processes, safeguarding trillions of dollars of transactions while improving the customer experience and protecting the privacy of everyday users. For more information, visit feedzai.com.
View source version on businesswire.com: https://www.businesswire.com/news/home/20230815117261/en/
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