What is AML transaction monitoring?

Suspicious transactions identified by an AML transaction monitoring system (TMS) must be further investigated. This investigation helps differentiate between illegal activities that need to be elevated to law enforcement in suspicious activity reports (SARs) and potential false positives that look like illicit activities, but are not.  

At a high level, AML transaction monitoring:

• Provides sophisticated real-time monitoring of customer transaction data, scanning and flagging any patterns that might align with known money laundering typologies.

• Identifies red flags, such as unusual transaction volume and velocity, activity involving high-risk countries. It also identifies entities that might appear on watchlists and sanctions lists, activity that doesn’t align with a customer’s typical risk profile, and more. 

• Incorporates or integrates with a range of sophisticated fintech (financial technology) automation solutions and data compliance best practices—such as transaction processing systems (TPS), fraud-detection tools, risk assessment and risk scoring. It also integrates with case management and transaction management methodologies to help compliance teams optimize workflows, meet rising customer demand for secure financial services, and satisfy strict regulatory requirements. 

Most, if not all, financial institutions (for example, banks, credit unions, cryptocurrency exchanges and other organizations offering financial services) are commonly legally compelled to perform some degree of transaction monitoring. This procedure is a part of a broader AML strategy for maintaining regulatory compliance.

As financial crimes increase in scope and severity, providers offering solutions for AML compliance continue to increase their offerings to meet rising demands. According to a recent MarketsandMarkets report, the transaction monitoring market is anticipated to reach USD 6.8 billion by 2028. 

Money laundering refers to several illegal methods for disguising profits derived from illegal activities as legitimate funds earned through legal means.

In essence, “dirty money” obtained from criminal activity is “laundered” or cleaned through an intermediary. Laundered monies can then be used for legitimate purchases, investments or transactions.

Money laundering is a common practice among criminals and criminal or sanctioned organizations seeking to avoid prosecution and taxation, while also facilitating further criminal activities, such as smuggling or terrorist financing.

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. 

Money laundering typically involves three steps: 

1. Placement: “Dirty” money, acquired through illegal or illicit means, is entered into the legitimate financial system, such as through a large cash bank deposit.
2. Layering: To obscure the origin of the illicit funds and put distance between the theoretically identifiable account holder, the funds are moved through a series of complex financial transactions. Layering can be accomplished by moving money between various different bank accounts, converting cash into other assets, or moving funds through various shell companies and offshore accounts. 
3. Integration: Layered funds are reintroduced into the legitimate economy through various methods, such as real estate, luxury items or other asset types. These assets can in turn be sold or converted back into cash to continue the layering process or simply used or consumed. 

Money laundering as a category can refer to any method that obscures the illegitimate source of funds. Criminals use various tactics to launder money, including:

• Smurfing: Dividing large cash deposits into several smaller deposits to avoid triggering reporting requirements.

• Shell companies: Money launderers set up or use existing shell companies (organizations that exist only to own other shell organizations with little to no legitimate business) to pass illicit funds through many different entities. With this tactic, they create a complex and confusing chain of ownership. 

• Cash-intensive businesses: Investing or starting businesses that handle large volumes of cash, such as laundromats or restaurants, creates opportunities for money launderers looking to mix illegitimate revenue with legitimate revenue. As it can be difficult for regulators to verify whether the reported profits of such businesses correlate to actual sales or services rendered, money launderers can add illicit funds to profits and falsely claim a legitimate source for the total sum.

• Cryptocurrency: While frequently not entirely anonymous, the decentralized nature of blockchain technology and virtual currencies like Bitcoin and Monero creates opportunities for criminals to obscure the origin and ownership of their funds. While most legitimate crypto exchanges have their own AML policies, some exchanges have more lax standards that attract a criminal element.      

• Gambling: Both online and traditional casinos can be used for money laundering. The process includes converting illicit funds into chips, gambling with a small percentage of those chips and then cashing out the remaining chips to be reported as legitimate winnings. 

The economic and social consequences of money laundering are many. Organizations that fail to properly protect against money laundering, either intentionally or unintentionally, erode general trust in financial institutions.

Large investments or asset trades made with the intention to launder illicit funds can distort economic growth models, creating false or inflated asset values, and diverting funds from more productive investments. Worse still, money laundering encourages further crime and corruption. Jurisdictions with lax AML policies can be more likely to see increased crime rates.

To combat global money laundering efforts, local and international law agencies work cooperatively with governments and financial institutions to enforce AML laws, regulations and suspicious activity reporting requirements.

Responding to the sheer scale and complexity of modern financial crime, both financial institutions and private sector solutions are investing heavily in AML transaction reporting solutions and other types of AML software.

The Business Research Company projects the global AML software market (including transaction monitoring software) to grow to USD 3.2 billion in 2025. This growth is driven by the increasing demand for real-time detection to comply with stricter AML regulations, with trends indicating a significant increase in AI-powered solutions.

The foundation of AML transaction monitoring is built on the ongoing analysis of vast amounts of transaction data. By establishing regular transaction patterns, banks and financial institutions can identify deviations from standard legal behavior. 

Depending on size, purpose, location and various other factors, transaction monitoring solutions can require unique calibration to suit any given specific financial or banking institution’s needs. While AML transaction monitoring system requirements are not strictly defined, most AML transaction monitoring systems include the following key components.

Modern AML transaction monitoring systems combine:

• Rule-based logic: Systems operate according to predefined rules (for example, activity thresholds or patterns, like deposit amounts or transaction locations) that trigger an alert. 

• Advanced analytics: Data-driven AML transaction monitoring systems are increasingly relying on advanced analytical tools, such as AI and ML algorithms that can analyze large datasets and spot patterns that might otherwise go undetected. 

Because not all transactions require as much scrutiny, adopting a risk-based approach to AML transaction monitoring helps organizations better delegate resources toward customers and transactions that can present higher risk levels. Considerations like customer profile, geographic location and transaction types help AML transaction monitoring systems assess customer risk and prioritize alerts.

Transaction monitoring is only one part of a complete AML strategy. Here are some other best practices:

• Know your customer (KYC): KYC laws and regulations require financial institutions to verify the identity of their customers and the nature of their accounts. 

• Customer due diligence: Customer due diligence requires financial institutions like banks or credit card companies to continuously verify the legitimacy of customer information. If any suspicious activity requires further investigation, customer due diligence proves to be an essential practice for creating a reliable audit trail. 

• Sanctions screening: As part of the transaction monitoring process, AML strategy best practices require organizations to screen transactions not only for unusual behavior, but also for sanctioned individuals and entities. 

• API integration: Modern AML transaction monitoring systems are designed for API (application programming interface) integration to facilitate data sharing between various software, applications and interfaces.  

Modern AML transaction monitoring systems leverage a wide range of technologies and methods associated with rule-based systems and advanced analytics. While not all AML monitoring systems are alike, these are some of the more commonly featured technologies.

• Rule-based operation enables AML systems to create transaction monitoring rules that flag any transactions that might exceed a predefined limit, either in value or frequency. 

• These types of controls are effective and easy to set up. However, they can potentially generate false positives, requiring extra investigation to verify whether a specific transaction is in violation of AML laws.

• Statistical behavioral analysis allows institutions to establish regular transaction activity baselines. Anomaly detection tools use these baselines to flag behavior that deviates from the norm.

• Behavior modeling methodologies allow AML transaction monitoring systems to track activity trends over time, adapting to evolving conditions as they grow and change. Customer segmentation helps establish a risk-based approach for different types of clients. 

• AML transaction monitoring systems employ various types of AI and ML.

• Supervised learning models use historical data with known outcomes to train algorithms to detect money laundering.

• Unsupervised learning models are also employed to identify new suspicious patterns without the need to rely on prelabeled data. 

• Combining multiple forms of AI and ML allows AML transaction monitoring systems to continuously improve over time, adapting to new and emerging threats. 

• Enabled by easily scalable cloud-based solutions, organizations can perform real-time monitoring on massive volumes of data. 

• Visualization tools help organizations get a better picture of the accounts and activity happening on their networks.

• Relationship-mapping techniques help visualize connections between accounts and entities and reveal any potentially hidden networks.