Blogs | 5 Essential Rules for AML and Transaction Monitoring Teams

5 Essential Rules AML and Transaction Monitoring Teams should follow: (With examples)

Anti-Money Laundering (AML) compliance is a critical aspect of maintaining the integrity of the financial system. Transaction monitoring, powered by robust AML rules, plays a crucial role in detecting and preventing illicit activities. To ensure effective transaction monitoring, consider incorporating these 10 critical AML rules into your team's workflow:

1. Detection of Structuring

Structuring involves breaking down large transactions into smaller ones to avoid reporting thresholds. AML rules should be capable of identifying a high volume of transactions slightly below the reporting limit within a specified period.

Scenario:

Reporting Threshold: In a particular jurisdiction, financial institutions must report any cash transactions exceeding $10,000 to the authorities.
Customer's Intent: A customer has $35,000 in illicit funds that they want to deposit into their bank account without triggering any alarms.
Structuring in Action: Instead of depositing the entire amount at once, the customer decides to break it down into smaller transactions:

Day 1: Deposits $9,500 in cash.
Day 3: Deposits $9,800 in cash.
Day 7: Deposits $9,200 in cash.
Day 10: Deposits $6,500 in cash.

AML Rule in Play: The bank has an AML rule set up to monitor for potential structuring activity. This rule flags any instances where a customer makes multiple cash deposits that are just below the reporting threshold ($10,000 in this case) within a defined period, let's say 30 days.
Detection: The system would detect the pattern of these cash deposits and raise an alert for potential structuring. The compliance team would then investigate further to determine the legitimacy of these transactions.

Key Takeaways:

Structuring is a common tactic used by criminals to launder money.
AML rules that monitor for patterns of transactions just below the reporting threshold are essential for detecting structuring attempts.
By defining parameters such as the transaction amount range and the time period to consider, these rules can effectively identify suspicious activity.

2. Customer Details Updated Before a Large Transaction

Changes to personally identifiable information (PII) just before a large transaction can signal account takeover attempts or layering activities. Monitoring for such patterns helps identify suspicious behavior.

Scenario:

Account History: John Doe has had a savings account with XYZ Bank for several years. The account has been relatively inactive, with only occasional small deposits and withdrawals.
Sudden Change in PII: One day, John Doe logs into his online banking and updates several pieces of his personally identifiable information. He changes his phone number, email address, and even his residential address.
Large Transaction: Immediately after updating his PII, John Doe initiates a wire transfer of $50,000 from his savings account to an overseas bank account he's never used before.
AML Rule in Action: XYZ Bank has an AML rule that monitors for significant changes to customer PII, especially when followed closely by large or unusual transactions.
Red Flag: The system detects this combination of events and raises an alert for potential account takeover or layering activity. The compliance team investigates further.

Possible Explanations:

Account Takeover: A fraudster may have gained access to John Doe's account, updated the PII to avoid detection, and is now attempting to steal his funds.
Layering: John Doe might be involved in money laundering, trying to obscure the origin of the funds by transferring them to an unrelated account after altering his PII.

Key Takeaways:

Changes to PII, especially before large or unusual transactions, can be a strong indicator of suspicious activity.
AML rules that monitor for such patterns are vital for detecting account takeovers and money laundering attempts.
By analyzing the combination of PII changes and transaction details, financial institutions can identify potential threats and take appropriate action.

3. Unusual Spending Patterns

This rule detects transactions that deviate from a customer's usual activity, considering factors like income, social status, and occupation. This can also flag potential account takeovers or external manipulation.

Scenario:

Customer Profile: Jane Smith is a school teacher with a modest income. Her bank account typically shows regular transactions related to her salary, utility bills, groceries, and occasional dining out.
Sudden Change in Spending: One month, Jane's account shows several high-value transactions at luxury stores, international travel bookings, and large cash withdrawals - activities that are significantly out of character for her.
AML Rule in Action: The bank has an AML rule that monitors for unusual spending patterns by comparing a customer's current transactions to their historical activity and considering factors like income and occupation.
Red Flag: The system detects this sudden change in Jane's spending behavior and generates an alert. The compliance team investigates further.

Possible Explanations:

Account Takeover: A fraudster may have gained access to Jane's account and is now using it for their own lavish spending.
Money Laundering: Jane might be involved in illicit activities and is using her account to launder money by making purchases that don't align with her income level.
Unexpected Windfall: While less likely, it's possible that Jane received a large sum of money (inheritance, lottery win) that explains the change in spending. The investigation would confirm this.

Key Takeaways:

Drastic deviations from a customer's typical spending habits can indicate suspicious activity.
AML rules that consider factors like income, occupation, and transaction history are crucial for detecting such anomalies.
By analyzing unusual spending patterns, financial institutions can identify potential account takeovers, money laundering schemes, or even instances where customers might need financial guidance.

4. Low Buyer Diversity

For merchants, this rule identifies cases where payments come from a limited number of buyers on platforms where multiple buyers are expected. It helps detect money laundering via collusion or circulation of funds.

Scenario:

Online Marketplace: ABC Online Marketplace is a platform where various sellers offer their products to a wide range of buyers.
Merchant Account: Seller XYZ has recently joined the platform and starts receiving payments for their products.
Unusual Pattern: Over several months, the transaction monitoring system at ABC Online Marketplace notices a peculiar trend in Seller XYZ's account. Despite offering a variety of products, almost all of their sales are coming from just two or three buyers.
AML Rule in Action: The platform has an AML rule that monitors for low buyer diversity, especially for new merchants. This rule flags instances where a seller receives a disproportionately high percentage of their revenue from a very limited number of buyers.
Red Flag: The system generates an alert for Seller XYZ's account due to the low buyer diversity. The compliance team initiates an investigation.

Possible Explanations:

Collusion: The seller might be colluding with a few buyers to create fake transactions, potentially to launder money or inflate their sales figures.
Circulation of Funds: The seller could be receiving payments from a small group of individuals who are repeatedly buying and reselling the same products, possibly to obscure the origin or destination of funds.

Key Takeaways:

Low buyer diversity, especially for new merchants on platforms expecting a wide customer base, can be a red flag for potential money laundering.
AML rules that monitor the concentration of buyers for each seller are crucial for detecting collusion and circulation of funds.
By analyzing the distribution of buyers and transaction patterns, platforms can identify suspicious activities and take appropriate action.

5. Disproportionate Flow-Through

This rule flags accounts where the total credits and debits are nearly equal over a short period. It's particularly relevant for businesses like marketplaces, where a significant difference is expected.

Scenario:

Online Marketplace: "ABC" is a thriving online marketplace where various sellers list their products, and buyers make purchases using the platform's payment system.
Merchant Account: Seller "XYZ" has been on ABC for a while, selling electronics.
Unusual Activity: Over a two-week period, ABC's transaction monitoring system notices something unusual with XYZ's account. The total amount of money coming into the account (credits, i.e., payments from buyers) is almost exactly equal to the total amount of money going out (debits, i.e., withdrawals to XYZ's bank account).
AML Rule in Action: ABC has implemented an AML rule that flags accounts exhibiting disproportionate flow-through, especially within short timeframes. For a typical marketplace seller, one would expect the credits (sales revenue) to significantly outweigh the debits (withdrawals to their own bank account), as they need to keep some funds in their ABC account to facilitate refunds, handle disputes, etc.
Red Flag: The system raises an alert for XYZ's account due to this unusual pattern. The compliance team begins an investigation.

Possible Explanations:

Pass-Through Account: XYZ's account might be used as a pass-through account for money laundering. Illicit funds are coming in from various sources (disguised as payments for goods), and then quickly withdrawn, effectively cleaning the money.
Transaction Manipulation: XYZ could be engaging in fraudulent activities, artificially creating matching credits and debits to obscure the true nature of their transactions.

Key Takeaways:

In a marketplace setting, a near-perfect balance between incoming and outgoing funds over a short period is often suspicious.
AML rules designed to detect disproportionate flow-through help uncover potential money laundering schemes or fraudulent activities.
By analyzing the flow of funds within merchant accounts, marketplaces can protect their platform's integrity and contribute to a safer financial ecosystem.

Disclaimer:

These rules are a starting point; customize them to align with your institution's risk profile and regulatory requirements.
Regularly update and refine your rules based on emerging threats and typologies.
Leverage technology and data analytics to automate monitoring and improve efficiency.

By implementing and adapting these AML rules, transaction monitoring teams can significantly enhance their ability to detect and prevent money laundering activities, contributing to a safer and more secure financial ecosystem.

Disclaimer: This blog is intended for informational purposes only and should not be considered as financial or legal advice. Consult with a qualified professional for advice tailored to your specific situation.

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