Financial Crime World

Adopting Risk-Based Approaches in Anti-Money Laundering and Financial Crime Management

As the financial sector continues to evolve, the importance of effective anti-money laundering (AML) and financial crime management has never been more pressing. In this article, we explore the adoption of risk-based approaches, machine learning, and digital transformation in AML and financial crime management, as highlighted by industry experts from Citibank’s Global Investigations Unit (GIU), JPMorgan Chase, and Standard Chartered.

The Importance of Risk-Based Approaches

A risk-based approach to AML and financial crime management involves collecting intelligence and data internally and externally, and leveraging advanced technology such as machine learning to drive efficiency and effectiveness. This approach is critical in identifying and mitigating potential risks, ensuring compliance with regulatory requirements, and protecting the integrity of financial institutions.

Key Points from Industry Experts

Citibank’s GIU: External View on Financial Crime Lifecycle Elements

  • The GIU provides an external view on the typical financial crime lifecycle elements of:
    • Transaction monitoring
    • Prevention
    • Detection
    • Reporting
    • Response

Public-Private Partnerships and Liaising with Law Enforcement

  • The GIU has been busy with public-private partnerships and liaising with law enforcement in various jurisdictions to identify risk across multiple countries and ensure an enterprise-wide approach to customers and groups of customers.

Machine Learning: Collecting External Unstructured Data

  • Machine learning is being used to collect external unstructured data and quickly come to a clear position on its assessment, allowing intelligent people to focus on identifying the highest risks and managing and mitigating them.

Governance Risks and Regulatory Challenges

  • Governance risks should remain front of mind when considering adopting advanced technology such as machine learning tools.
  • Models need to work as intended once the human touch has been removed.
  • Communicating with regulators about the introduction of AI into models marks another challenge, ensuring they remain comfortable with changes to the risk-based approach.

Challenges in Adopting Risk-Based Approaches

Data Quality, System Performance, and Model Validation

  • Data quality, system performance, data privacy, and model validation require governance and dedicated expertise.
  • Roll-out will take time and testing, particularly for large banks.
  • Potential for “big black-box models” is some way away, and the governance and regulatory challenges are manageable.

Conclusion

The article emphasizes the importance of adopting a risk-based approach to AML and financial crime management, leveraging advanced technology such as machine learning to drive efficiency and effectiveness while managing governance risks and regulatory challenges. As the financial sector continues to evolve, it is essential for institutions to stay ahead of emerging threats and adapt their approaches to remain compliant with changing regulations.