Data governance refers to the framework of policies, processes, and technologies that ensure data is accurate, secure, and effectively managed across an organisation. In compliance contexts such as anti-money laundering (AML), strong data governance is essential for maintaining data integrity, meeting regulatory obligations, and supporting effective risk management.

Definition of Data Governance

Data governance can be defined as the practice of establishing clear ownership, accountability, and quality standards for organisational data. It involves setting rules for how data is collected, stored, enriched, shared, and used, with oversight mechanisms to ensure compliance with both internal policies and external regulations.

This structured approach ensures that information used in compliance systems, from customer screening to transaction monitoring, is reliable and auditable.

Why Data Governance Matters in AML Compliance

Financial institutions rely on accurate and consistent data to detect financial crime risks. Poor governance often leads to fragmented records, duplicate entries, or missing information, which weakens the effectiveness of AML systems.

The FATF Recommendations outline that countries should implement comprehensive measures, including robust customer due diligence and continuous monitoring, to combat money laundering and terrorist financing. This underscores how reliable, timely data is foundational to effective AML systems.

Impact on Risk-Based Approaches

A strong governance framework enables firms to implement a risk-based approach, ensuring higher-risk customers and transactions are monitored with greater scrutiny. Without consistent data standards, firms may struggle to justify their risk models to regulators.

Support for Supervisory Reviews

Regulators expect firms to demonstrate how data feeds into compliance systems. With governance controls in place, firms can show that customer and transaction records are consistent, current, and traceable, which builds supervisory confidence.

Key Components of Data Governance

Effective data governance frameworks typically include the following elements:

Data Quality Management

High-quality data is fundamental to compliance. This includes ensuring accuracy, completeness, and timeliness. The Basel Committee on Banking Supervision, via the Bank for International Settlements (BIS), highlights that effective risk management depends on banks’ ability to aggregate and report data accurately and in a timely way. Strengthening data quality and governance not only improves supervisory oversight but also supports more resilient financial systems

Data Ownership and Stewardship

Assigning clear accountability for datasets reduces duplication and ensures that compliance-critical data is properly maintained. Data stewards play a key role in enforcing standards and preventing governance gaps.

Data Lineage and Traceability

Firms must be able to demonstrate where data originated, how it has been transformed, and how it is used in compliance processes. Traceability not only supports internal audits but also enables firms to meet regulatory reporting obligations.

Applications of Data Governance in AML Systems

Data governance provides a foundation for a range of compliance processes.

Customer Screening and Monitoring

Strong governance ensures that sanctions and watchlist data used bys such as FacctList (watchlist management) and FacctView (customer screening) is accurate, consistently updated, and applied across all business units.

Alert Adjudication

Governed data feeds directly into Alert Adjudication processes. If alerts are generated on poor-quality or incomplete data, compliance teams waste time chasing false positives rather than focusing on real risks.

Transaction Monitoring

Solutions like FacctGuard (transaction monitoring) rely on high-quality inputs to flag unusual activity. Data governance frameworks ensure that transaction data is harmonised and consistent, enabling models to identify suspicious behaviours more effectively.

Challenges in Implementing Data Governance

While governance frameworks offer significant benefits, implementation is not without obstacles.

• Data silos: Many firms struggle with fragmented systems that limit visibility across business lines.

• Legacy infrastructure: Outdated databases and platforms make harmonisation complex.

• Regulatory expectations: Supervisors expect governance to be embedded, not an afterthought, requiring continuous improvement and auditability.

The European Banking Authority (EBA) requires that institutions have robust internal governance arrangements, including clear organisational structures, transparent responsibilities, and effective risk-management processes that match the institution’s nature, scale, and complexity

Best Practices for Data Governance in Compliance

To overcome challenges, firms can adopt several best practices:

• Establish a dedicated governance framework with clear policies and accountability.

• Invest in data management technologies that enforce quality and traceability.

• Integrate governance into compliance processes rather than treating it as a separate function.

• Conduct regular reviews to ensure governance structures align with regulatory changes.

Future of Data Governance in Financial Services

As compliance systems increasingly adopt advanced analytics and AI, the role of governance will expand. Reliable and traceable datasets will be necessary to ensure AI models remain explainable and fair, a key regulatory expectation.

Firms that embed governance deeply into compliance will not only strengthen regulatory trust but also unlock competitive advantages by improving operational efficiency and risk intelligence.