AML Transaction Monitoring Rules: Best Examples (2025)

Financial crimes are becoming more advanced, especially with the rise of AI-driven scams and deepfake technology, making it tougher for financial institutions to keep up. This evolving threat is revealing weaknesses in many businesses’ usual compliance frameworks and the need for more robust and flexible monitoring systems.

Fraud and money laundering exploit AML control vulnerabilities, as recent fines show. Just this month, payments company Block was required to pay a $40 million fine for anti-money laundering and virtual currency compliance failures related to its Cash App.

Last year, the UK’s Financial Conduct Authority (FCA) fined Metro Bank £16 million ($21 million) for financial crime failings. According to the regulator, between June 2016 and December 2020, Metro Bank lacked the necessary systems and controls to adequately monitor over 60 million transactions, worth more than £51 billion ($67.3 billion), for money laundering risks.

Companies, especially those handling high volumes of transactions, must implement transaction monitoring across the entire customer lifecycle to avoid fines, license loss, or even potential jail time. 

This article delves into the latest rules, scenarios, and best practices that financial institutions should adopt to stay ahead in the fight against fraud and money laundering. 

What are AML transaction monitoring rules and scenarios?

AML transaction monitoring is a process that can help businesses analyze user activity and transactions to detect suspicious patterns or anomalies that could indicate fraudulent behavior and criminal activity.

Transaction monitoring rules are sets of instructions and conditions to be considered during monitoring. These rules flag unusual patterns, such as transactions that exceed a certain threshold or deviate from a customer’s normal activity. Every time a transaction is made, the enabled rules are automatically applied to determine whether the transaction matches the rule parameters. Conditions and parameters could vary depending on the industry, region, and risk appetite.

For example, a customer who typically makes small domestic transactions suddenly wires $15,000 to an offshore account in a high-risk jurisdiction and then immediately receives a similar amount from a different source. This pattern could trigger a rule for structuring or layering, suggesting potential money laundering.

By combining rules and scenarios, businesses can prioritize alerts, investigate high-risk cases, and file Financial Intelligence Units (FIU) reports, including Suspicious Activity Reports (SARs) and Suspicious Transaction Reports (STRs) where appropriate.

Suggested read: The Three Stages of Money Laundering and How Money Laundering Impacts Business

Why are AML transaction monitoring rules important?

AML transaction monitoring rules are essential for automating the process of finding any suspicious patterns of transactions that should be stopped. In some jurisdictions, there are precise requirements for making additional checks based on different factors (for example, a company must verify the identity of every user who made transfers during 24 hours for a total of $10,000 or more). According to the Chainalysis 2025 Crypto Crime Report, illicit crypto addresses received a total of $40.9 billion in 2024. This likely marks a record year for inflows to illicit actors, although the figure represents a lower-bound estimate based on the addresses identified to date.

AML guidelines for effective transaction monitoring in 2025

Global regulations

Effective AML transaction monitoring follows key global standards such as those issued by the FATF (Financial Action Task Force) and the European Union’s AML Directives. FATF’s latest priorities highlight using technology and public-private collaboration to improve detecting and reporting suspicious activity.

As of 2024, the European Union adopted a unified AML framework through the Anti-Money Laundering Regulation (AMLR) and the Sixth AML Directive (AMLD6), in order to harmonize rules across member states. 

Also, the EU’s newly established Anti-Money Laundering Authority (AMLA) will begin operations in 2025. It will directly supervise high-risk entities (like crypto firms and high-risk banks) and enforce stricter compliance standards.

In the United States, the Bank Secrecy Act and its subsequent updates, including the Anti-Money Laundering Act of 2020, require financial institutions to implement robust monitoring systems that support SAR filings and comply with evolving guidance from FinCEN.

Across the Asia-Pacific (APAC) region, countries are tightening AML rules in alignment with FATF recommendations. Jurisdictions like Singapore and Hong Kong have implemented advanced AML/CFT frameworks requiring ongoing monitoring, transaction filtering, and immediate reporting of suspicious behavior to regulators like MAS and HKMA.

Risk-based approach

The risk-based approach (RBA) is widely regarded as best practice. It’s a core recommendation of the FATF and is widely adopted by its member countries in their national AML/CFT regulations.

RBA involves prioritizing higher-risk clients and transactions for closer monitoring. This approach ensures that institutions can focus their resources on areas that pose the highest risk. 

Integrating Customer Due Diligence (CDD) and Know Your Customer (KYC) processes is also crucial for building a solid foundation for transaction monitoring. This includes verifying customer identities and assessing risk continuously.

Modern AML strategies also rely on ongoing monitoring and dynamic risk scoring, where customer risk levels can shift in real time based on new behaviors, transaction patterns, or external data. To support this, institutions increasingly turn to automated tools and AI-driven analytics to detect complex money laundering schemes that would otherwise go unnoticed.

Suggested read: AML Transaction Monitoring in 2025: The Complete Guide

Use case: Transaction monitoring

Effective transaction monitoring requires identifying specific red flag indicators that may signal potential money laundering or terrorist financing activities. Based on the CBUAE’s guidelines, financial institutions should consider the following indicators when establishing transaction monitoring rules:​

1. Geographical risk indicators

• Transactions involving high-risk jurisdictions: Transfers to or from countries with poor AML/CFT regimes or high exposure to corruption.
• Use of offshore jurisdictions: Transfers to numerous offshore locations without a clear business rationale.​

2. Transaction behavior indicators

• Structuring transactions: A series of transactions just below reporting thresholds to evade detection.
• Rapid movement of funds: Deposits followed by immediate withdrawals or transfers, especially when inconsistent with the customer’s known profile.
• Frequent transactions: Significant and/or frequent transactions that contrast with known or expected business activity or employment status.​

3. Customer profile indicators

• Inconsistent information: Ambiguous or inconsistent explanations regarding the source and/or purpose of funds.
• Shared contact details: Multiple customers providing the same home address, phone number, or IP address.
• Use of virtual offices: Companies registered with virtual or shared spaces addresses.​

4. Emerging technology indicators

• Use of virtual currencies: Unjustified transactions to and from cryptocurrency platforms and digital asset exchanges.
• Mobile payment anomalies: Significant and/or frequent transactions using mobile telephone services or excessive use of stored value cards.

Use case: Payment errors and refunds

Companies should also create rules to detect payment processing errors and initiate refunds if needed. This includes:

• Incorrect transaction details. Mismatched payment information (e.g., wrong account numbers, currencies, etc.) can lead to failed or misrouted payments. Systems should verify payment data consistency before processing and flag any discrepancies.
• Duplicate charges. Customers may be charged more than once due to technical glitches or retry mechanisms. Automated detection of repeated transactions within a short time frame helps prevent overcharging and enables prompt refunds.
• Issues with the provided personal information. If personal data (e.g., name, address, or tax ID) is incomplete or invalid, it can interfere with processing or compliance checks. In such cases, companies should trigger verification requests or pause transactions until the data is corrected.

Use case: Preventing hacks

Companies can also use rules to prevent unauthorized access to users’ accounts and other fraudulent activity, zeroing in on:

• Logins from unusual devices and high-risk locations 
• Frequent changes in payment information or shipping address
• Multiple failed login attempts within a short period of time
• Creation of multiple accounts using a single IP address

Use case: Compliance with regulations

Companies can also protect their users and revenue by taking additional measures to comply with regulations:

• Creating lists to block transactions from sanctioned entities
• Adding AML screening for large transactions
• Introducing additional checks, such as biometric authentication, if suspicious behavior is detected
• Leveraging crypto analytics for crypto transactions
• Adding Travel Rule functionality where applicable to support compliance in the US, EU, and Singapore

For example, in the US, a financial institution has to report all transactions exceeding $10,000. It should therefore set an AML rule that is triggered if a customer deposits or withdrawals $10,000 or more in 24 hours. 

It should be noted that criminals can split their transactions into several layers to avoid being caught. To prevent this, it’s possible to use an AML rule that, for instance, compares ingoing and outgoing transactions to see if the withdrawal is 10% less than the original deposit. 

This rule can then trigger one or both of the following automated actions: 

• Customer is asked to provide source of funds
• Customer is assigned a tag that will show if further transactions are made

AML rules in action: Real-world scenarios in AML compliance

Firms subject to AML rules need to first understand what specific risk factors they should take into consideration when conducting ongoing monitoring of client activity. Some of these include: 

• The client’s behavior, such as refusal to provide requested information, unusual transactions, exceeded thresholds. 
• The client’s reputation
• The risk inherent to the asset or service being acquired
•  Unusual transactions or exceeded thresholds
• The consistency of client profile information
• Whether sources of funds appear legitimate
• Whether transactions involve sanctioned entities or Politically Exposed Persons (PEPs)

It is crucial for financial institutions and law enforcement to work together and keep a close eye on possible risk factors and suspicious activities. In 2024, TD Bank was hit with a massive $3 billion penalty by FinCEN and the DOJ for not catching and reporting suspicious activities tied to criminal networks. At the same time, international efforts like Interpol’s Jackal III and Operation Destabilise uncovered extensive money laundering operations that were leveraging cryptocurrencies and cross-border tactics.

You can learn more about AML red flags here.

Best practices for AML: CDD, KYC, and transaction monitoring

To ensure a robust AML framework, institutions must adopt several best practices:

• KYC/CDD: Institutions must gather and verify customer data, update profiles, and assess risks periodically. Ongoing due diligence is a must-have in the current state of online banking
• Transaction Monitoring: Automated monitoring systems analyze transaction patterns in real-time, flagging suspicious activities. Machine learning algorithms can help reduce false positives and increase detection accuracy.

Combining these practices ensures comprehensive coverage and helps financial institutions maintain regulatory compliance.

Choosing a reliable AML Transaction Monitoring Solution in 2025

Modern AML transaction monitoring systems are powered by automation and AI. These tools work in tandem with rules to analyze vast amounts of data and detect suspicious activity in real time. AI-driven systems can learn from historical data, continuously improving their accuracy and reducing false positives. They analyze various aspects of transactions, including transaction frequency, amounts, and customer behavior.

The larger a company gets, the more resources it needs to allocate to transaction monitoring. And sooner or later, it simply becomes inefficient to use manual work. That’s when automated solutions come into play.

Sumsub’s AML Transaction Monitoring system leverages advanced AI and machine learning to analyze vast volumes of transactional data, identifying suspicious activities with precision.

• Comprehensive rule library. You can access over 300 pre-built rules tailored for various industries, including fintech, crypto, trading, and iGaming, as well as customize rules without coding to fit your specific needs. ​
• AI-driven anomaly detection. The system utilizes AI to uncover complex fraud patterns beyond standard screening parameters, greatly improving detection capabilities. ​
• Integrated case management. Streamlines investigations with unified case management, analytics, and reporting tools, facilitating efficient FIU reports (i.e., STR) generation and audit readiness. ​
• Real-time alerts. You can receive instant notifications via webhooks for transactions flagged for manual review, which helps with timely compliance actions.

The iGaming sector also faces unique challenges, with fraud increasing by an average of 64% year-over-year between 2022 and 2024. Sumsub addresses these challenges by identifying and preventing activities like bonus abuse, arbitrage, and multi-accounting, while staying compliant with the evolving regulations.

FAQ

• What are transaction monitoring rules?

  Transaction monitoring rules are a set of criteria that allow companies to spot suspicious transactions. Each company can create its own set of rules, as long as it allows them to comply with regulations.

• What are scenarios in AML transaction monitoring?

  Some of the most common scenarios considered in transaction monitoring include:

  • The client’s behavior, such as refusal to provide requested information, unusual transactions, exceeded thresholds.
  • The client’s reputation
  • The risk inherent to the asset or service being acquired
  • Unusual transactions or exceeded thresholds
  • The consistency of client profile information
  • Whether sources of funds appear legitimate
  • Whether transactions involve sanctioned entities or Politically Exposed Persons (PEPs)

• What are transaction monitoring alerts?

  Transaction monitoring alerts notify companies about suspicious activity. Whenever such an alert is triggered, transactions should be blocked, followed by an investigation. The employee detecting the suspicious activity should escalate the incident to a compliance/AML officer or senior management to decide whether a Suspicious Activity Report (SAR) should be filed to the relevant Financial Intelligence Unit (FIU). If it is decided not to file a SAR, the reasons for doing so must still be explained.

• What is a false positive AML alert?

  False positives occur when legitimate transactions are marked as suspicious. To avoid this, companies should diligently build out their AML scenarios and hire a reliable solution provider.