What is transaction monitoring?

The practice of transaction monitoring is crucial to risk management because it protects organizations and their customers from crimes like money laundering, fraud and terrorist financing while ensuring compliance with legal and regulatory requirements. By analyzing transaction patterns, transaction monitoring helps safeguard a financial system, mitigate institutional risk and prevent costly penalties and reputational damage.

Many organizations—especially traditional financial institutions (e.g., banks, credit card companies, money transfer services) engage in transaction monitoring. The practice also applies to other entities within the broader financial ecosystem (e.g., fintech platforms, cryptocurrency exchanges) and in non-financial sectors (e.g., insurance firms, real estate companies). Regulatory bodies also rely on transaction monitoring to detect illicit activity, enforce compliance and uphold the integrity of financial markets and institutions.

Prior to the 1970s, banks mainly relied on manual oversight and internal controls, with little regulatory guidance. This changed significantly with the introduction of the Bank Secrecy Act (BSA) in the United States in 1970, which marked the beginning of formal requirements for financial institutions to track and report suspicious transactions. Over the next few decades, other major regulations followed, including the establishment of the Financial Action Task Force (FATF) in 1989.

By the 2000s, especially following the implementation of the USA PATRIOT Act, transaction monitoring became more sophisticated and technology-driven. Financial institutions moved from manual checks to rule-based software systems capable of flagging suspicious behavior in near real-time.

Today, transaction monitoring processes have evolved to incorporate advanced technologies like machine learning (ML), artificial intelligence (AI) and behavioral analytics. These advanced, automated systems enhance detection accuracy and reduce false positives (legitimate transactions flagged as suspicious) in real time. They also allow financial institutions and other entities to handle increasingly large transaction volumes with greater efficiency and sustainability.  

The following are some of the most common use cases for transaction monitoring:

• Anti-money laundering

• Fraud prevention

• Regulatory compliance

• Cryptocurrency and digital assets

• Ecommerce and payment processors

• Banking and fintech

• Law enforcement

One of the most prominent and regulated use cases for transaction monitoring is anti-money laundering (AML). According to the United Nations Office on Drugs and Crime, money laundering accounts for an estimated 2–5% of the global GDP, or approximately USD 800 billion to USD 2 trillion annually.¹

In response to the scale and complexity of modern financial crime, financial institutions are investing heavily in advanced technological solutions. The Business Research Company projects the global anti-money laundering (AML) software market to grow to USD 3.2 billion in 2025, driven by the increasing need for real-time detection and compliance with stricter regulations.2 Trends show a marked increase in the use of AI-powered solutions. Additionally, cryptocurrency monitoring is becoming a key area of focus, with the rise of digital currencies making it crucial for financial service institutions to track blockchain transactions to prevent illicit activities.

Today’s AML transaction monitoring systems are designed to identify behaviors associated with known money laundering typologies, such as structuring or “smurfing,” where large transactions are broken into smaller amounts to avoid reporting thresholds. These systems also flag unusual transaction patterns, such as sudden spikes in activity or behaviors that deviate from a customer’s typical profile. Monitoring for transactions involving high-risk jurisdictions is also crucial, as these may indicate attempts to move funds through countries that have weak AML regulation controls or are under sanctions.

Fraud detection is one of the most active areas of transaction monitoring. Systems monitor credit card transactions for signs of misuse, such as purchases made from unusual locations or sudden high-value activity. They also help detect account takeovers by identifying suspicious login attempts followed by risky transactions. Internal threats like employee-led fraud can also be flagged when irregular internal transfers or system access patterns are detected.

Financial institutions must meet various regulatory obligations, and transaction monitoring supports this by screening transactions against sanctions lists—such as those maintained by the Office of Foreign Assets Control (OFAC) or the United Nations—and identifying interactions with politically exposed persons (PEPs). Systems ensure the reporting of transactions that exceed legal thresholds (e.g., cash deposits over USD 10,000 in the US) and help confirm that customer behavior aligns with Know Your Customer (KYC) and Customer Due Diligence (CDD) profiles.

In the crypto space, transaction monitoring focuses on blockchain analysis to trace the movement of digital assets. This includes identifying wallet clustering (linking related addresses), tracking funds through mixers or privacy coins, and flagging interactions with wallets known to be associated with illegal activity. Crypto exchanges rely on both on-chain and off-chain monitoring to maintain AML compliance and detect suspicious behavior involving both digital currencies and traditional fiat money (e.g., dollars, euros).

Ecommerce platforms and payment processors use transaction monitoring to prevent fraud and detect misuse of their services. This includes identifying high-risk merchants (e.g., high refund volumes), monitoring for transaction velocity (e.g., rapid-fire purchases) and spotting geolocation anomalies that might indicate card testing or bot activity from a flagged region.

Banks and fintech platforms monitor for various red flags that indicate potential misuse or compliance risks. Monitoring includes tracking dormant accounts that suddenly become active with large transfers, scrutinizing cross-border payments with unclear sources of funds and identifying customer transaction behavior that deviates from their established patterns.  

While transaction monitoring is crucial for the prevention of financial crimes, it also plays a key role in supporting ongoing investigations. Law enforcement agencies and compliance teams use these systems and tools not only to detect suspicious activity in real-time but also to manage case workflows and link transactional evidence. Advanced techniques like network analysis help uncover relationships between accounts and expose layering methods often used in complex money laundering schemes.

Transaction monitoring adheres to a risk-based approach, using a series of interconnected methods to detect suspicious financial activity and ensure compliance.

The core functionalities in this process typically include the following:

1. Data collection and integration
2. Transaction screening
3. Risk-based scoring
4. Pattern detection and analysis
5. Alert generation
6. Investigation and case management
7. Suspicious activity reporting
8. Continuous monitoring and improvement

A transaction monitoring system (TMS) is a specialized software solution used by financial institutions to automatically track, analyze and assess financial transactions for signs of suspicious or unusual activity (e.g., transactions linked to data breaches). A TMS relies on robust transaction management processes, which form the foundation for accurate risk detection, regulatory compliance and data compliance.

Transaction monitoring software platforms also feature built-in dashboards and analytics tools that track key performance indicators (KPIs), such as alert volumes, false positive rates and SAR conversion rates. Monitoring these metrics allows compliance teams to evaluate the system's effectiveness, optimize workflows and ensure regulatory requirements are met efficiently.

To further enhance detection, today’s transaction monitoring solutions also integrate with cybersecurity tools, such as threat intelligence platforms and cybersecurity risk assessment tools. These integrations help identify cyber-enabled financial threats (e.g., account takeovers, data-driven fraud attempts).

Large enterprise organizations across industries rely on mainframe systems as a core part of their IT infrastructure, with mainframes handling almost 70% of the world’s production IT workloads. These systems are typically integrated within a broader IT landscape, including cloud computing environments, to handle high volumes of transaction processing with speed, stability and security. As a result, mainframes are an ideal environment for supporting both transaction processing systems (TPS) and feeding data into transaction monitoring systems..

Mainframes support mission-critical operations, ensuring that large-scale transaction data is reliably captured and processed for analysis by the transaction monitoring system (TMS). This data supports real-time risk detection and enables in-depth financial analysis within the monitoring system, such as identifying patterns in fund movement or uncovering inconsistencies that could signal financial crime.

In the context of a TMS, mainframes:

• Process and store transactional data in real time, enabling accurate and up-to-date information for monitoring.

• Serve as the source system for transaction feeds that are analyzed by the TMS, providing essential data like account details, transaction amounts and time stamps.

• Enable high-throughput, low-latency data processing, which is crucial for real-time monitoring and ensuring that suspicious activities are flagged without delay.

• Support integration between legacy systems and modern TMS platforms through APIs, middleware or data pipelines, ensuring smooth data transfer and compatibility.

• Enhance transaction security through comprehensive encryption and access control mechanisms.