Money laundering schemes are almost impossible to detect if a financial institution doesn’t have a proper anti-money laundering compliance regulations program. Money launderers leverage internal systems of businesses like FinTechs, banks, insurance companies, cryptocurrency dealers, gaming platforms, casinos, and other financial institutions to move illegal money around to make the money look legit. The flow of money laundering can be disrupted following AML compliance rules.
The primary goal of anti-money laundering rules is to uncover abnormal patterns between millions of transaction data, generated every day with financial accounts. By implementing regulations that have been outlined by AML laws in the Bank Secrecy Act (BSA) and the USA Patriot Act, financial institutions and related service providers can help regulatory bodies and federal law enforcement agencies and prevent the flow of money laundering. In this article, we’ll discuss the top 10 AML rules for compliance programs.
What AML Compliance Rules Do I Need to Consider?
While building a successful AML compliance rule program, firms need to meet a minimum standard set forth by the federal government. If a financial institution, does not meet these standards, government agencies such as:
- Financial Crimes Enforcement Network (FinCEN)
- Financial Action Task Force (FATF)
- Financial Industry Regulation Authority (FINRA)
If financial institutions fail to follow through on the rules and regulations, these agencies can fine the institutions.
Compliance teams need to make sure that all the regulations apply to a financial institution and its specific business type and locality. Businesses need to develop proper methods and internal controls, including risk assessment and customer identification programs, to fulfill the due diligence requirements.
Anti-Money Laundering Rules for Compliance Program
Complying with anti-money laundering rules can be challenging for businesses of all scales. As all businesses have different risk factors and appropriate thresholds. However, there are some basic rules that every financial institution needs to follow.
Below, we have mentioned 10 rules for anti-money laundering compliance programs, and these rules are the first point in building a successful compliance program.
1. Structuring Over Time
Structuring is a money-laundering activity that involves splitting the transactions into multiple smaller transactions to avoid reporting requirements. This rule should detect an excessive proportion of transactions below the reporting limit. Financial institutions are required to report transactions over $10,000, so banks need to look for transactions that are just below $10,000.
2. Profile Change Before a Large Transaction
This rule is for identifying instances where customers make profile changes to PII (personally identifiable information) shortly after making a huge transaction. This often signifies account takeover or potential “transaction layering” activity to obscure the path of the funds.
3. Suspicious User Financial Behavior
Another common rule for anti-money laundering is keeping track of suspicious financial behavior. Financial institutions should look forward to identifying transactions that are different from an individual’s usual spending behavior. You should also look for behaviors that are not common for a financial party’s financial profile.
4. Increase in Transaction Volume/Value
This rule for anti-money laundering should help in identifying parties with high pay-out transaction volumes or a significant increase in the value of a party’s outgoing transactions compared to their recent average.
A rule like this is perfect for a P2P payment network with the capability to withdraw funds to an external account. The rule should filter out entities that have their bank accounts for a short amount of time and parties with a low balance and low outgoing transaction value over the relevant time window.
5. Circulation of Funds
Circulation of funds happens when individuals pay themselves using different accounts. This rule should detect situations where:
- The party deposits casino checks
- Purchase of bank drafts that are used at casinos
- Casino checks whose memo indicates that the funds aren’t the result of casino winnings
This rule should also look for transfers between parties that have the same IP address.
6. Excessive Flow-Through Activity
This rule for anti-money laundering should help in identifying parties where the total value of the credit is similar to the total value of debits in a short period. A rule like this should be perfect for a financial service that offers a collection of funds where there won’t be comparable spend activity.
7. Low Number of Buyers
For platforms that see several buyers, interacting with a single seller, the rule should detect merchants that only receive from limited buyers. This can help regulatory bodies uncover collusion and circulation of funds. This rule for anti-money laundering should only look for accounts older than a specific time period.
8. Low Communication Between Buyers and Sellers
Platforms that keep track of the frequency of communication between buyers and sellers on the service, this rule can also identify merchants with high earnings but very few sent messages, which can indicate money laundering instead of normal business activities.
9. High-Risk Jurisdiction
This rule for anti-money laundering compliance relies on geographic-based risk factors for countries and regions where money laundering is common. Some examples of risk categories include high banking secrecy, high financial crime, high drug trafficking, and known tax-evading countries.
It’s important to keep this AML program rule updated based on the latest information. For example, in June 2021, the FATF updated its list of the geographical locations under monitoring to also include Haiti, Malta, the Philippines, and South Sudan. Ghana was removed from the list after new information.
10. Anonymous Source of Funds
The last AML Program rule should look out for situations where the party sends funds into decentralized exchanges and then extracts the funds, which is used to anonymize the funds.
It can also help in identifying when the party converts the currency into gaming tokens and then withdraws them for money laundering purposes.