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Improving Anti-Money Laundering Efforts with Machine Learning: Best Practices for Financial Institutions

Financial institutions can significantly enhance their anti-money laundering (AML) efforts by adopting machine learning (ML) technology in transaction monitoring. However, this requires careful planning and execution to ensure successful adoption. Here are the three main takeaways for financial institutions to improve AML efforts with ML:

1. Align Stakeholders on Vision and Design

Effective implementation of ML technology in transaction monitoring requires collaboration among various stakeholders. To achieve this, follow these best practices:

• Engage multiple stakeholders: Involve data, technology, line-of-business, MRM, and compliance teams from the beginning of the project.
• Consider business-as-usual activities: Ensure that ongoing regulatory actions and all business-as-usual activities are considered in the planning process.
• Align on vision and design: Gather multiple perspectives and align on the vision, design, and trade-offs for using ML to improve transparency across the enterprise.

2. Develop a Safe Technology Transition Plan

A well-planned transition is crucial for successful adoption of ML technology in transaction monitoring. Follow these guidelines:

• Focus on the transition plan: Be intentional about the approach (focus on the transition plan, not just the end state).
• Collaborate on business and technology goals: Adopt a collaborative mindset that fuses technology and business goals.
• Execute with transparency and rigor: Proceed with rigorous and transparent execution tailored to the realities of modern technology systems.
• Run parallel scenarios: Run existing rule- and scenario-based risk scenarios in parallel with ML-based scenarios to build confidence among stakeholders.

3. Enhance the Model Risk Management Framework

ML models require a robust framework for development, validation, and monitoring. Follow these best practices:

• Collaborate with data science team: Expand capabilities to work closely with the data science team in the model development and validation process.
• Define validation standards: Shape validation standards, policies, and frameworks to address specific risks associated with ML models (bias detection and explainability).
• Set precise performance requirements: Define precise performance and monitoring requirements, including below-the-line testing, out-of-time testing, and recalibration of ML models.

By following these best practices, financial institutions can successfully adopt ML technology in transaction monitoring, improve effectiveness, efficiency, and reduce risks.