10 Essential AML Rules for Compliance Teams

Introduction

In today's evolving financial crime landscape, effective Anti-Money Laundering (AML) transaction monitoring systems are critical for financial institutions to detect and prevent illicit activities. For financial crime professionals designing or enhancing AML programs, having robust rules in place can make the difference between detecting criminal activity and facing regulatory penalties. This blog post explores ten fundamental AML rules that every compliance team should consider implementing to protect their organization from common money laundering schemes. While each financial institution faces unique risk factors based on its business model, geographic footprint, product range, and customer base, these core rules provide a solid foundation for any AML monitoring system. Whether you're building a program from scratch or transitioning from manual to automated monitoring, these insights will help strengthen your detection capabilities.

Rule #1: Detection of Structuring

Structuring is a common money laundering technique where transactions are deliberately broken down into smaller amounts to avoid triggering reporting thresholds. This tactic is specifically designed to circumvent regulatory reporting obligations that many jurisdictions impose on financial institutions.

‍How to implement:

• Configure your monitoring system to identify multiple transactions that fall just below the reporting threshold (e.g., between $9,000-$10,000 if the reporting threshold is $10,000)
• Set appropriate time parameters (e.g., within a 60-day period) to capture patterns
• Look for repeated transactions with similar amounts from the same originator or to the same beneficiary

Effective structuring detection rules help identify customers who may be deliberately attempting to avoid scrutiny by staying below reporting thresholds while moving substantial aggregate amounts through the financial system.

Rule #2: Customer Details Updated Before a Large Transaction

This rule addresses a scenario commonly associated with account takeover or deliberate attempts to obscure transaction trails. When a customer modifies their personally identifiable information (PII) shortly before initiating a significant outbound payment, it should trigger an alert for further investigation.

‍Key reasons for implementing this rule:

• Helps detect potential account takeover by professional money launderers who gain access to dormant accounts
• Identifies possible "layering" activities where criminals are attempting to hide the money trail
• Flags deliberate attempts to dissociate transactions from account history

PII changes that should be monitored include address updates, phone number changes, email modifications, or any other identifying information that could be used to track the customer's identity.

Rule #3: Unusual Spending Patterns

Unusual spending pattern detection is crucial for identifying account takeover or externally driven activities. This rule should flag transactions that deviate significantly from a customer's established behavioral patterns.Factors to consider when implementing this rule:

• Customer's historical transaction patterns
• Income level and expected financial capacity
• Occupation type and associated spending behaviors
• Geographic considerations and typical spending locations
• Transaction volume and size relative to peer groups

Rather than implementing a single rule, financial institutions often deploy a set of complementary rules that collectively identify unusual spending behaviors across multiple dimensions.

Rule #4: Low Buyers Diversity

This rule applies primarily to merchant platforms where a normal pattern would involve multiple buyers interacting with a single seller. When a merchant only receives payments from a very limited number of consumers, it could indicate potential money laundering through collusion.

‍Implementation considerations:

• Allow new merchants sufficient time to build their customer base
• Apply the rule only to accounts older than a certain period
• Consider the industry norm for buyer concentration
• Evaluate the transaction amounts relative to the merchant's declared business type

Low buyer diversity can be a strong indicator of money circulation schemes where funds are being moved in a closed loop to create the appearance of legitimate commerce.

Rule #5: Disproportionate Flow-Through

This rule identifies accounts where the total amount of credits closely matches the total value of debits over a short period. Such behavior is particularly suspicious for business accounts like marketplaces for goods and services, where you would typically expect to see a significant difference between incoming and outgoing funds.

‍Red flags to monitor:

• Nearly identical credit and debit totals over defined periods
• Rapid movement of funds into and out of accounts
• High-velocity transactions with minimal account balance retention
• Transactions occurring outside normal business hours

The disproportionate flow-through rule helps identify shell companies or businesses that may be operating as conduits for money laundering rather than legitimate commercial enterprises.

Rule #6: High-Risk Countries

Transactions involving countries with elevated money laundering risks should be closely monitored. This rule should alert compliance teams to transactions that involve jurisdictions known for banking secrecy, significant financial crime exposure, or tax haven status.

‍Critical implementation factors:

• Maintain up-to-date lists of high-risk jurisdictions based on FATF guidance
• Consider country-specific risk scores and thresholds
• Monitor both incoming and outgoing transactions
• Adjust scrutiny based on transaction purpose and customer relationship

Remember that country risk lists evolve frequently based on geopolitical developments and regulatory changes. For example, in June 2021, FATF updated its surveillance list to include Haiti, Malta, the Philippines, and South Sudan, while removing Ghana.

Rule #7: Immediate Withdrawal to Private Wallets

This rule targets accounts that consistently withdraw funds immediately after receiving them. Criminal organizations often move illicit funds rapidly through the financial system to obscure their origin, making this pattern a strong indicator of potential money laundering.

‍Pattern examples to monitor:

• Large deposits followed by same-day withdrawals
• Consistent pattern of deposits and immediate withdrawals every few days
• Withdrawals to private wallets or accounts with minimal KYC requirements
• Multiple withdrawal requests shortly after funds clear

Immediate withdrawal patterns could signal that an account is being used as a pass-through mechanism rather than for legitimate financial needs.

Rule #8: Cash Transactions

Despite the rise of digital payments, cash remains a preferred method for criminals due to its anonymity. Monitoring cash-related activities is essential for AML programs, especially when cash usage is inconsistent with the customer's profile or expected behavior.

‍Key monitoring parameters:

• Large cash deposits incompatible with customer profile
• Frequent cash transactions just under reporting thresholds
• Cash transactions from customers with online-only business models
• Unusual cash deposit patterns from low-risk customer categories (e.g., students)

Cash transaction monitoring rules should be customized based on customer risk profiles, with enhanced scrutiny for high-risk customers or unusual transaction patterns.

Rule #9: Dormant Accounts Monitoring

Dormant accounts that suddenly become active present a significant money laundering risk. This rule should trigger alerts when new activity emerges on accounts that have been inactive for a substantial period.Suspicious patterns to flag:

• Accounts inactive for years suddenly showing transactions
• Dormant accounts reactivated with transactions to high-risk jurisdictions
• Unusual transaction types compared to pre-dormancy behavior
• High-value transactions immediately after reactivation

The dormant account monitoring rule helps identify potential account takeovers and deliberate use of inactive accounts to avoid scrutiny that regularly active accounts might receive.

Rule #10: Frequent Conversions Between Cryptocurrency and Fiat Currency

As virtual assets (VAs) aren't yet widely used for everyday transactions, frequent conversions between cryptocurrency and traditional currency can indicate attempts to conceal the source of funds. Criminals often convert proceeds from illicit activities into cryptocurrencies to obscure their origin, despite facing higher transaction fees.

‍Monitoring considerations:

• Multiple small conversions between fiat and cryptocurrency
• Transactions involving privacy-focused cryptocurrencies
• Conversion patterns inconsistent with customer profile
• Rapid conversion cycles between different cryptocurrencies and fiat

These conversion patterns can be a sign of "layering" in the money laundering process, where criminals are working to distance funds from their original source.

Conclusion

Implementing these ten fundamental AML rules provides a solid foundation for identifying potentially suspicious activities and protecting your institution from money laundering risks. However, these rules should be tailored to your specific risk profile, customer base, and regulatory environment. Effective AML monitoring requires continual refinement and evolution as criminals develop new techniques to circumvent detection. Regular testing, tuning, and updating of your rule set is essential to maintain effectiveness against emerging threats. Financial crime professionals should view these rules as starting points rather than comprehensive solutions. Your institution's unique risk factors will dictate additional rules and monitoring approaches necessary for a robust AML compliance program. By establishing these core monitoring capabilities, your financial crime team will be better positioned to detect suspicious activities, file timely suspicious activity reports, and protect your institution from regulatory penalties and reputational damage.

What other AML rules do you consider essential for effective transaction monitoring? Share your thoughts in the comments below to help build a more comprehensive understanding of effective AML monitoring approaches.