Latest AML Trends: 8 Trends and How to Modernize Compliance

As financial crime becomes more sophisticated, the financial services industry is under pressure to develop equally sophisticated, AI-driven solutions. Know Your Customer (KYC), financial crime, and fraud prevention teams must be equipped with the latest advanced technologies to detect modern threats and stay compliant with regulations.

EY's 2024 Anti-Money Laundering (AML) Transaction Monitoring Survey, published in November found that the drivers behind monitoring initiatives are diverse, but are heavily influenced by regulatory compliance requirements and third-party reviews. "Over 80% of institutions cited regulatory-instigated changes in the past two years," according to the EY survey. However, many now aim to shift focus to improving operational efficiency.

The survey found that the financial sector struggles with slow change execution, often due to underestimated data integration challenges. EY found that almost 65% of financial institutions reported data quality as an obstacle when it comes to executing change.

Disconnected data and processes make spotting financial crime risks inefficient and unreliable, with slow response times and weak risk management leaving institutions and their clients vulnerable.

Both external and internal factors shape compliance process change.

External drivers:

• Evolving tactics of criminal networks

• Rising client expectations and demand for innovation

• Increasing global regulatory requirements

• Changing business operations

• Emerging technologies like AI, automation, and data analytics

Internal drivers:

• Siloed data and fragmented processes

• Outdated legacy technology

• High false positive rates

These challenges have spurred the development of specialized anti-money laundering solutions to help financial firms analyze and flag potential risks across customers, counterparties, investors, and transactions.

From AI-driven transaction monitoring solutions to cryptocurrency-related crime, today’s industry trends are pushing financial institutions and regulatory bodies to intensify their efforts to combat financial crime. 

Financial institutions leverage AI and machine learning to detect suspicious activity faster and reduce false positives, improving the accuracy of threat detection.

Criminals exploit decentralized finance (DeFi) platforms for money laundering activities. Regulators are tightening oversight of crypto exchanges, enforcing Travel Rule compliance, and using blockchain analytics.

Newer legislation, such as the EU’s AML Authority (AMLA) and the U.S. AML Act of 2020, improve cross-border collaboration around financial crime. The Financial Action Task Force (FATF) continues to pressure non-compliant jurisdictions to adhere to regulatory standards. 

Criminals manipulate trade transactions through over- or under-invoicing. AI-driven transaction monitoring helps detect anomalies in trade data, improving TBML detection.

Instant payments heighten cyber-enabled fraud risks. Financial institutions deploy real-time monitoring and behavioral analytics to detect suspicious activity.

Money laundering activities linked to wildlife trafficking, illegal logging, and other types of environmental crime is a growing concern. Regulators are pushing financial firms to track these financial transactions.

Governments are implementing Ultimate Beneficial Ownership Registries to prevent shell company abuse. The EU’s AML directives and the US Corporate Transparency Act reinforce transparency efforts, though the US Treasury Department recently announced that it will "not take enforcement actions against companies that do not file beneficial ownership data with the agency," according to AP.

Increased sanctions against countries like Russia have led to more shadow banking, trade-based forms of money laundering, and illicit crypto transactions. Financial institutions are investing in advanced sanctions screening tools that integrate AI and data fabric capabilities to improve compliance practices.

Modernizing doesn’t require expensive, high-risk system overhauls. Instead, financial institutions should focus on an agile, iterative approach that targets user experience and automation opportunities. With fast, flexible technologies, KYC, AML, and fraud teams can detect and mitigate risks more effectively.

Rather than relying on manual workarounds, screen-switching, and disconnected workflows, business teams with modern anti-money laundering technology solutions automate key decisions and processes wherever possible. Addressing critical challenges first and reusing integrations across processes lets financial institutions realize benefits quickly while reducing project risk and costs. This modular approach keeps compliance teams agile and responsive to new threats, evolving criminal activities, and regulatory demands.

A unified platform fosters collaboration across KYC, AML, and fraud teams, enabling shared data, faster decisions, quicker investigations, and more accurate risk detection. Key capabilities such as data fabric, API and RPA integration, process orchestration, case management, and generative AI power these changes, making them effective and scalable.

Financial crime threats are constantly evolving, and compliance strategies must keep pace. A flexible, AI-powered platform helps financial institutions strengthen their defenses and adapt to new threats and regulations. By embracing automation and AI, financial firms can build a proactive, scalable compliance program that leads to better risk management and operational resilience.