Skip to main content

AI Enabled Anti-Money Laundering Systems

Anti-Money Laundering (AML) systems are rapidly evolving to compensate for the constant increase in volume and complexity of online financial crime. AML solutions help combat the sophisticated methods criminals use to avoid detection.

AML includes a set of policies, procedures, and technologies that prevent the process of taking illegally obtained money and making it appear to have come from a legitimate source.

The AML term arose from regulatory standards, specifically to detail the concealing of financial movements for underlying crimes ranging from tax evasion, drug trafficking, public corruption, and the financing of terrorist groups.

Intelligent AML Systems Market Development

According to the latest worldwide market study by Juniper Research, by 2028 the total spend on third-party AML systems will have grown by 80 percent -- that's up from $28.7 billion in 2024.

This significant market growth will be driven by the use of artificial intelligence (AI) technologies to assist AML analysts and reduce false positives from transaction data.

Extensive research has found that AML systems are increasingly using AI capabilities in an assistive role. These automated AI co-pilot systems can improve financial risk assessment.

Juniper analysts anticipate that this co-pilot role will remain popular, due to ongoing concerns from government regulators around the explainability of fully automated decisions using AI.

Juniper Research identified that AML system vendors are increasingly expanding the scope of industries they cover beyond the traditional global financial services market.

For example, the total spend on third-party AML systems by professional and other businesses, such as the legal, real estate, and non-profit sectors, will reach $6.3 billion globally by 2028 -- growing by 170 percent from 2024.

To successfully reach these new high-growth industries that aren't covered by existing solutions, Juniper has recommended that AML system vendors tailor their capabilities and partnerships to better serve these segments.

"AML system vendors should extend partnerships with data providers, to allow coverage in different sectors, such as gambling and professional services," said Daniel Bedford, research analyst at Juniper Research.

Outlook for AML Systems Applications Growth

This expansion will allow compliance teams across a broader range of markets to identify high-risk transactions or customers and minimize the impact of financial crime.

That said, I believe the fight against financial crime is getting a much-needed tech boost. The global AML market is advancing as intelligent systems race to keep pace with the growing volume and complexity of online criminal activity.

 These sophisticated AML solutions are crucial for combating the evolving methods used by criminals to launder money.  By combining policies, procedures, and new technology, more organizations may stop criminals from legitimizing funds derived from illegal activities.

Popular posts from this blog

How AI Transforms Financial Decision-Making

Artificial intelligence (AI) has emerged as a transformational force, reshaping business processes and unlocking new possibilities for efficiency and innovation in corporate finance. The latest Gartner survey on AI usage in finance provides evidence of this emerging trend, offering valuable insights into the future growth trajectory of AI in finance. The Gartner survey reveals a significant milestone. As of 2024, 58 percent of finance functions actively use AI technology -- that's a substantial increase from previous years. Artificial Intelligence Market Development Perhaps even more telling is the projection that by 2026 more than 80 percent of finance functions are expected to be leveraging AI solutions. The survey sheds light on the use cases of AI in finance: AI is being deployed to enhance forecasting accuracy and provide deeper insights into financial trends. Automation of routine tasks and improved accuracy in financial reporting are key benefits observed. AI algorithms are