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Transaction monitoring: Data quality in banking makes the difference

Jessica Corey, CAMS

January 27, 2025

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Transaction monitoring ensures more than just compliance

Without reliable client and transactional data coming into your monitoring system, either manually or automatically, you could miss crucial suspicious activity. This could put your institution at regulatory and reputational risk.

Transaction monitoring in financial institutions

Financial institutions operate in an environment where even the slightest data discrepancies can create outsized risks. Accurate, complete, and reliable data isn’t just a cornerstone of compliance—it’s essential for identifying suspicious activities, safeguarding clients, and protecting institutional integrity. Ensuring high financial crime data quality within your transaction monitoring system is a proactive step toward reducing exposure to money laundering and fraud while maintaining regulatory compliance.

What is transaction monitoring?

Transaction monitoring is the cornerstone of a successful suspicious activity detection and reporting strategy. Without reliable client and transactional data coming in, either manually or automatically, you could miss crucial suspicious activity. This could put your institution at regulatory and reputational risk.

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Defining data integrity: More than quality control

Data integrity goes beyond simply having “good” data. It means ensuring your data is accurate, consistent across platforms, and complete in its context. While data quality in banking ensures reliability, data integrity adds a layer of trust by ensuring data is complete, logical, and practical for decision-making.

This distinction matters for financial institutions. Transaction monitoring software depends on data integrity to accurately flag potentially illicit behavior and meet compliance requirements. A lack of integrity can lead to overlooked suspicious activities or errors in compliance reporting, which can carry both financial and reputational consequences.

Data integrity and quality: Why both matter for compliance

When transaction monitoring systems work with incomplete or inaccurate data, they risk more than just inefficiency. Poor data quality can derail processes critical to regulatory compliance, such as generating alerts, filing suspicious activity reports (SARs), and accurately aggregating currency transaction reports (CTRs). These issues can result in penalties, reputational damage, or missed opportunities to stop money laundering, terrorist financing, and fraud.

High-quality data with strong integrity processes enables financial institutions to:

Detect abnormal patterns in transactions accurately.

Assess risks associated with customer activities effectively.
Maintain compliance with anti-money laundering (AML) regulations.

Challenges in maintaining data integrity

Financial institutions often face recurring challenges that undermine data quality and integrity. Common pain points include:

Incomplete integrations: When data sources are not fully synchronized with transaction monitoring systems, gaps in monitoring occur, leaving vulnerabilities in your suspicious activity monitoring program.

Duplicate records: These distort transaction patterns and waste the time of valuable resources. Investigations should be free from duplicate transactions.

Costly reconciliation efforts: Correcting discrepancies between systems can divert teams from more critical tasks like suspicious activity monitoring and fraud detection. In the AML world, there is little time without spinning wheels on reconciling faulty data.

Emerging technologies: Enhancing data integrity and monitoring

Resources are few and far between in the world of AML and fraud detection. Advanced technologies like machine learning (ML) and artificial intelligence (AI) are transforming transaction monitoring and making the most of these valuable human and technological resources. These tools enhance data integrity by analyzing vast datasets, identifying inconsistencies, and spotting anomalies faster than traditional methods. Predictive models powered by AI can even anticipate illicit activity trends, giving institutions a critical advantage against increasingly sophisticated threats.

Real-world impact: The cost of incomplete data

Transaction monitoring systems rely on timely and accurate data to function effectively. A single missing transaction code or customer identifier can allow illicit activities, such as check fraud or money mule operations, to slip through unnoticed. Real-world cases show that poor data quality has resulted in millions of dollars in losses and regulatory fines, underscoring the need for robust data management practices. In the recent unprecedented TD Bank consent order, data integrity was a key finding. The bank neglected to link monetary instrument purchases to customer accounts, failed to import and monitor ACH transactions, remote deposit capture, and P2P transactions such as Zelle, Venmo, or Paypal, and did not monitor check transactions. TD Bank’s lack of top-down governance created an environment where these important data monitoring points were viewed as too expensive for their compliance program. In hindsight, that would have been much less costly than the more than $3 billion in fines and forfeitures imposed on the bank.

Data integrity: How to ensure your data is valid and complete

Effective transaction monitoring starts with rigorous daily data management and extends to long-term validation processes. Best practices for data integrity checks include:

Daily reviews: Validate incoming data feeds, check for import failures between your core and automated systems, and promptly correct any issues. This should be performed daily to ensure no suspicious activity goes undetected.

Transaction reviews: Ensure all relevant transactions are correctly coded and included in your monitoring system. Missing data points, like transaction types or codes, can disrupt your ability to detect suspicious activity.

Customer records audits: Verify that customer profiles include accurate and complete data, such as addresses, phone numbers, and account identifiers. This ensures that any suspicious activity detected will be associated with the appropriate subjects.

Scheduled data integrity audits: Conduct independent reviews every two years—or sooner if significant system changes, like mergers or acquisitions, occur. An independent third-party auditor experienced in Bank Secrecy Act systems can perform this function.

Risk-based data decisions: Based on a thorough risk assessment, evaluate which transactions, such as fees or transfers, should be imported into monitoring systems. If a risk-based decision is made not to import certain transaction types, document your reasoning for not monitoring these transactions.

Education and customer trust: A critical human element

Building a culture of data integrity requires investment in senior leadership and employee training. Leadership should understand the critical nature of the data and be willing to provide necessary resources to ensure data integrity. Employees must be aware of the risks posed by poor data and trained to address discrepancies proactively. The ultimate goal is to prevent money laundering, terrorist financing, and fraud, which builds customer trust and retention.

Conclusion: Building trust through data integrity

Data quality and integrity are strategic assets that enable financial institutions to detect money laundering and fraud, meet regulatory requirements, and protect their clients. Leveraging advanced technologies, conducting regular audits, and fostering a culture of accountability will ensure institutions remain resilient against evolving threats. By prioritizing data integrity, financial institutions can reduce risks, safeguard their reputation, and build trust with their stakeholders.

Abrigo Connect is a banking intelligence solution that connects the data flowing through Abrigo and other systems in one platform—without expensive data scientists or complex technical infrastructure.

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