Quality in, quality out: The role of high-quality data for effective AML compliance

Just as an engine requires high-grade, contaminant-free fuel to optimize performance, anti-money laundering (AML) systems rely on accurate, complete, and timely data to effectively detect suspicious activities. Without quality data, even the most sophisticated compliance systems are vulnerable to inefficiencies, missed red flags, and regulatory non-compliance.

This article considers the role of data quality in AML efforts, discussing its importance, challenges, and the steps necessary to ensure data integrity, accuracy, and compliance with privacy laws.

How does poor data quality impact AML efforts?

Consultancy Gartner estimates that poor data quality costs businesses an average of $12.9 million annually. However, the cost can be much greater. 

In 2012, a large UK-based commercial bank was fined £56 million after it experienced a catastrophic system failure due to corrupted and incomplete data during a software update. This incident disrupted millions of customer transactions for weeks, including missed payments and inaccessible funds. The Financial Services Authority (FSA) identified poor data governance as the root cause of the failure.

Examples like this highlight how poor data quality can expose FIs to operational breakdowns, regulatory penalties, and reputational harm. When data governance fails, the ripple effects extend beyond immediate financial loss, undermining trust in the firm’s ability to effectively manage customer and transactional data.

Other key ways bad data can directly impact AML efforts include:

• Reduced team efficiency through increased false positives: Inaccurate or incomplete data often triggers unnecessary alerts, flooding compliance teams with false leads. This results in an excessive workload for analysts who have to investigate transactions that are ultimately legitimate manually. These false positives waste time and resources and reduce the effectiveness of monitoring systems.
• Regulatory vulnerability through missed suspicious transactions: The system may fail to detect suspicious activities when customer profiles or transaction histories are incomplete or outdated. This creates blind spots that could allow illicit financial activities to go unnoticed, leaving FIs vulnerable to regulatory violations and financial crime.
• Higher compliance costs: Inconsistent and erroneous data requires additional verification, validation, and remediation efforts. These extra steps increase operational costs, divert compliance teams’ focus from high-priority tasks, and delay reporting to regulators, contributing to inefficiency and rising expenses.
• Poor risk management: FIs can struggle to assess and mitigate risk without accurate and comprehensive data. Inaccurate data undermines risk profiling and transaction monitoring, potentially allowing high-risk activities to slip through the cracks. This failure to manage risk properly increases the likelihood of regulatory penalties and reputational damage.

Benefits of high-quality data in AML compliance

On the flip side, when data is accurate and reliable, FIs are better equipped to detect and prevent illicit activities, streamline their processes, and maintain trust with regulators and clients alike. Other benefits include:

• Enhanced detection capabilities: High-quality data allows AML systems to function optimally, leading to advanced anomaly detection and fraud identification. With a solid data foundation, FIs can configure more sophisticated detection algorithms to pinpoint unusual behaviors in transactional patterns more accurately.
• Streamlined client onboarding and due diligence: High-quality data accelerates know your customer (KYC) processes by providing a clearer view of client information from the start. This clarity enables faster onboarding while ensuring thorough due diligence, reducing the risk of onboarding clients linked to money laundering or other financial crimes.
• Consistency across jurisdictions: High-quality data allows global institutions to maintain consistent AML standards across different regions. This consistency is critical for banks and financial services firms operating in diverse regulatory environments. With accurate, standardized data, institutions can apply compliance measures uniformly, ensuring they meet the varied regulatory demands of each jurisdiction.
• Enhanced brand reputation and market trust: Maintaining strong data integrity and compliance practices builds trust with clients, investors, and partners. FIs that commit to high data quality standards are viewed as more reliable and transparent, enhancing their competitive standing in the market.
• Proactive threat management: With unified and standardized data sets, AML teams can adopt a more proactive approach to risk management. These data sets enhance the ability to identify emerging threats early by connecting seemingly weak risk signals that, in isolation or across siloed systems, might otherwise go unnoticed.

Balancing data quality with data sovereignty and global privacy laws

Another significant benefit of high-quality data is its ability to help FIs navigate the growing complexities of global privacy and data sovereignty laws. Privacy regulations such as the General Data Protection Regulation (GDPR) in the European Union and the California Consumer Privacy Act (CCPA) in the United States impose strict controls on data collection, storage, and sharing. At the same time, data sovereignty laws stipulate that data collected within a given jurisdiction must be stored and processed within that jurisdiction. 

Having access to accurate and comprehensive data enhances an institution’s ability to comply with these laws. By leveraging clean, well-structured data, FIs can more effectively classify and segregate information, ensuring data is shared appropriately and fully compliant with regulatory requirements.

For example, when data is accurate and well-organized, FIs can confidently classify sensitive information, determining what requires protection under privacy laws. This classification allows them to apply measures like anonymization or pseudonymization without accidentally omitting crucial data or misclassifying information.

Essential data quality factors to consider for an AML program

High-quality data is the backbone of effective AML compliance, yet many organizations struggle to maintain it. A 2021 survey of over 1,000 global executives found that 79 percent of companies frequently have to rework data analytics projects due to poor data quality, wasting valuable time and resources. 

To avoid these pitfalls, compliance teams should focus on several essential data quality factors:

Completeness

Incomplete customer profiles or transaction histories may obscure red flags and result in undetected illicit activities. For example, if customer identification details or transaction information are missing, it can prevent AML systems from recognizing suspicious patterns or transactions that are not aligned with a customer’s typical behavior.

The Financial Action Task Force (FATF)’s recommendations emphasize the importance of capturing all relevant transaction data, including the originator’s and beneficiary’s details, to improve traceability. FIs should monitor the quality of the transaction data they receive and take appropriate action when essential details are missing, ensuring all information is complete for effective risk analysis.

Accuracy & consistency

Data errors or discrepancies, such as mismatched customer names or inconsistent transaction records, can lead to suspicious activities being misidentified. For example, if a transaction is flagged for review but is not consistently linked to the correct customer profile due to inaccurate data, it may result in false positives or missed alerts.

Consistency across multiple data sources is particularly important for global FIs. When data from various jurisdictions or systems is inconsistent, it can create gaps in monitoring, leading to compliance failures. Therefore, the FATF encourages institutions to establish internal controls to ensure the accuracy of the data used for AML efforts, reinforcing the need for consistency across systems to avoid mistakes in risk assessments.

Timeliness

If transaction data or updates to customer profiles are delayed, suspicious activities may go undetected, leaving FIs vulnerable to financial crime. Real-time data enables AML systems to alert compliance officers immediately when there is an unusual activity, allowing them to respond quickly to mitigate risk.

Timely reporting is also crucial for compliance with regulatory requirements. Many jurisdictions impose strict reporting deadlines for suspicious activity reports (SARs), and any delay could result in penalties. Real-time or near real-time data ensures institutions meet regulatory timelines and avoid costly fines or reputational damage.

Relevance

For data to be useful in AML compliance, it must be contextually relevant to the particular risks an institution is monitoring. For example, an FI should focus on data related to unusual transactions, large international transfers, or activity in high-risk jurisdictions. Relevance ensures that the data used for detection and monitoring is appropriately aligned with the specific risks being assessed, reducing noise and improving the effectiveness of the monitoring system.

Secure

Secure data management practices prevent unauthorized access or tampering with sensitive customer and transactional information. This is especially important given the high value of financial data to cybercriminals. AML systems depend on secure data to protect information from breaches, which can lead to financial and reputational damage.

How to improve data quality for AML

Compliance teams can enhance their AML efforts by seeking partnerships with vendors that prioritize data quality at every stage. In an increasingly complex regulatory landscape, external data providers are crucial in delivering the high-quality data necessary for effective AML programs.

When evaluating potential vendors, FIs should focus on those who excel in the following areas:

• Establishing strong data governance frameworks is foundational to improving data quality. This includes setting clear policies for data management, defining data ownership, and ensuring data handling practices adhere to legal and regulatory requirements.
• Data cleaning is a critical step in ensuring the accuracy and completeness of information. This process involves identifying and correcting errors or inconsistencies in the data, such as missing values, duplicated records, or incorrect data formats. Routine data cleaning practices help maintain high-quality customer profiles and transaction records, which are vital for effectively monitoring suspicious activities.
• Data validation ensures the data being used is accurate, complete, and conforms to predefined standards. In an AML context, validating customer information (such as names, addresses, and transaction histories) and transaction details helps detect inconsistencies and verify that the data aligns with regulatory requirements.
• Incorporating third-party data sources, such as sanctions lists, politically exposed persons (PEP) databases, and other global watchlists, significantly enhances the quality of AML data. By integrating these external sources into their internal systems, FIs gain a more comprehensive view of their customers and associated risks.

Improve AML efficiency and reduce false positives with high-quality data solutions

At ComplyAdvantage, we understand the critical importance of leveraging high-quality data to enhance AML efficiency and minimize false positives. By providing comprehensive and real-time solutions, we enable FIs to streamline their compliance processes, reduce unnecessary manual interventions, and improve the effectiveness of their AML programs. Here’s how our proprietary data can make a difference:

• Fewer false positives: Our award-winning machine learning model understands and weighs global distributions of names and other identifiers, ensuring we maximize true matches while minimizing false positives. 
• Faster remediation: With detailed, rich profiles that consolidate identifying information from global sources, we enable firms to rapidly discount false positives and confirm true positives with greater confidence. 
• More timely and comprehensive risk coverage: At ComplyAdvantage, we provide one of the fastest sanctions data update processes in the market. Our consolidated entity profiles are continuously updated, ensuring our clients have access to the latest, most relevant data as soon as it becomes available.
• Improved risk-association detection: Our solutions go beyond basic screening by continuously mining family, corporate structure, and trading relationships data from various global sources. This enables our clients to better detect risks by association – such as indirect sanctions evasion or connections to PEPs and their relatives or close associates (RCAs) – helping them stay ahead of emerging threats and make informed decisions.