In 2025, financial crime is more sophisticated than ever, costing the global economy up to $2 trillion annually, according to the UN Office on Drugs and Crime. As criminality evolves, so too must the tools and tactics used to stop it.
For compliance and risk professionals, AML is a strategic priority—essential for safeguarding institutional integrity and meeting global regulatory expectations. AML compliance is undergoing rapid and ongoing transformation, driven by factors such as adoption of artificial intelligence (AI), real-time monitoring, and regulatory innovation.
This article explores how global regulatory shifts, development of technology, and wider use of artificial intelligence (AI) are reshaping the way financial institutions manage AML risk—offering a perspective on emerging trends and best practices.
From real-time monitoring and perpetual KYC to the evolving role of AI and regulatory technology (RegTech), we look at how institutions around the world are adapting to stay ahead of financial crime and meet compliance expectations.
Anti-Money Laundering (AML) refers to the frameworks, controls, and regulatory obligations designed to prevent the flow of illicit funds through legitimate financial systems.
Effective Anti-Money Laundering (AML) programs are more critical than ever. Programs are designed to combat the three key stages of money laundering—placement, layering, and integration—by implementing robust controls such as Know Your Customer (KYC), Customer Due Diligence (CDD), and continuous transaction monitoring.
While the core principles of AML often remain consistent, regulatory frameworks and enforcement priorities often vary across jurisdictions. Below is an overview of AML regulations and focal points in key global regions:
Regulators worldwide are continuously reviewing and strengthening AML frameworks to keep pace with evolving threats as well as new technologies. International bodies like the Financial Action Task Force (FATF) work towards greater alignment of AML standards and improved information sharing. And the Anti-Money Laundering Authority Regulation (AMLAR) in the European Union marks a step-change in cross-border cooperation and harmonization of AML standards in the bloc.
Other regulatory developments globally include:
In an evolving landscape, AML activities need to continue to be agile, data-driven, and coordinated. Risk leaders are increasingly turning to AI and advanced analytics to meet this need to stay ahead of criminals while meeting regulatory expectations.
AI is transforming AML tools and practices from reactive to proactive. It is reshaping how financial institutions detect and respond to suspicious activity.
Unlike traditional rule-based systems, AI models can analyze vast, complex datasets to uncover hidden patterns and anomalies that may indicate money laundering and other forms of suspicious financial activity.
In 2025, advanced behavior analytics can also help institutions move beyond static rules. By establishing behavioral baselines and applying contextual risk modeling, systems can assist institutions to better differentiate between legitimate and illicit activity.
AI-led systems also have the potential to reduce false positives in relation to AML risks, allowing compliance teams to focus on genuine threats and accelerate investigations. AI can also help automate other time-consuming tasks, like generating suspicious activity reports (SARs).
However, the use of AI in AML is not without its challenges. Concerns around bias, transparency, and explainability have prompted regulators to demand that AI models are built with guardrails to help mitigate against bias, be auditable, and compliant with ethical standards. In 2025, responsible AI governance is a top priority for compliance leaders.
The shift toward dynamic continuous monitoring is redefining how institutions assess and respond to customer risk. Perpetual KYC (pKYC) for instance uses automation to continuously monitor customer risk profiles, triggering alerts when significant changes occur—such as a sudden spike in cross-border transactions or a change in beneficial ownership—so further screening can occur.
A proactive approach through pKYC empowers institutions to make better use of resources while they detect and respond to emerging threats.
Regulatory technology (RegTech) is helping compliance teams do more—faster, smarter, and with greater precision. These solutions can automate workflows, integrate disparate data sources, and provide advanced analytics for risk management.
As regulatory expectations grow more complex, RegTech can continue to play a pivotal role in supporting institutions to scale compliance efforts efficiently—turning regulatory activity into strategic advantage.
Financial institutions can use Moody’s Maxsight™ to unify risk intelligence across sanctions, beneficial ownership, and third-party relationships—so teams can build a holistic view of risk and respond proactively.
For information about Moody’s data and workflow automation solutions designed to support AML activity, please get in touch—we would love to hear from you.