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When it comes to safeguarding your financial operations from illicit activities, understanding AML (Anti-Money Laundering) transaction monitoring is paramount. In this comprehensive guide, we delve into rule building, identifying red flags, and recognizing indicators of suspicious behavior in transaction monitoring.

Why Rule Building is Crucial

In the realm of transaction monitoring, rule building takes center stage. Rules serve a pivotal role in categorizing customers, enabling businesses to monitor their behavior effectively. Here at FACEKI, we are committed to providing valuable insights into the diverse scenarios that companies need to be prepared for in this ever-evolving landscape.

The Basics of Rule Building

To build robust rules for transaction monitoring, compliance teams compile lists of scenarios that could be considered “suspicious.” These scenarios are determined by specific parameters that we will explore in the following sections. The primary objective of these lists is to create alerts that notify staff members when suspicious transactions occur, empowering them to take swift action. These alerts go by various names, such as “rules” or “AML scenarios.”

Examples of AML Scenarios

For businesses subject to AML regulations, understanding the risk factors to consider when monitoring client activity is crucial. Some of the factors include:

· Client Behavior: This entails assessing actions like refusal to provide requested information, unusual transactions, and transactions that exceed predefined thresholds.

· Client Reputation: Understanding the reputation of the client is vital.

· Risk Associated with Assets or Services: Evaluating the inherent risk tied to the asset or service being acquired.

· Consistency of Client Profile Information: Ensuring that the information in the client’s profile remains consistent.

· Legitimacy of Funding Sources: Checking if the sources of funds are legitimate.

· Sanctioned Entities or Politically Exposed Persons (PEPs): Evaluating whether transactions involve entities on sanction lists or individuals with politically exposed status.

Effective Rule Creation: Practical Use Cases

Use Case: Transaction Monitoring

In the realm of transaction monitoring, several indicators are instrumental in crafting effective rules. These indicators include:

· Location: Examining multiple purchases made with the same credit card in different countries in a short span or activities conducted by multiple individuals using the same IP address.

· Sequence: This involves sequences of transactions that fall below a certain threshold, deposits followed by withdrawals in a short timeframe, or multiple high-value purchases followed by quick returns.

· Thresholds: These can be set on daily, weekly, monthly, quarterly, or yearly bases.

· Destination: Monitoring large sums of money transferred to high-risk countries or high-risk individuals/legal entities included in sanction lists or adverse media.

Use Case: Payment Errors and Refunds

Effective rules are also essential for detecting payment processing errors and initiating refunds when necessary. This includes:

· Incorrect Transaction Amounts

· Duplicate Charges

· Issues with Provided Personal Information

Use Case: Preventing Hacks

To prevent unauthorized access and other fraudulent activities, companies employ rules that focus on:

· Logins from Unusual Devices and High-Risk Locations

· Frequent Changes of Account Information or Shipping Address

· Multiple Failed Login Attempts in a Short Timeframe

· Creation of Multiple Accounts from a Single IP Address

Use Case: Compliance with Regulations

Rules play a pivotal role in ensuring compliance with regulations, including:

· Creating Lists to Block Transactions from Sanctioned Entities

· Adding AML Screening for Large Transactions

· Introducing Additional Checks, Such as Biometric Authentication, When Suspicious Behavior is Detected

· Leveraging Crypto Analytics and Travel Rule Checks for Crypto Transactions

Practical Example

Imagine a company that needs to report all transactions exceeding $3,500. To accomplish this, an AML rule is established to trigger an alert if a customer deposits or withdraws $3,500 or more within 24 hours. However, it’s important to note that criminals might attempt to evade detection by splitting their transactions into smaller ones, a tactic known as “smurfing.” This practice involves spreading transactions across various accounts to remain below regulatory reporting thresholds. To proactively combat smurfing, an AML rule is designed to compare incoming and outgoing transactions and identify cases where a withdrawal amount is 10% less than the original deposit amount. This is a key indicator of

money laundering, as third-party participants (such as money mules) typically receive a percentage for their involvement. This rule triggers automated actions, such as requesting proof of the source of funds from the customer or assigning a tag for further transactions.

AI for Advanced Monitoring

The integration of Artificial Intelligence (AI) has revolutionized transaction monitoring, enabling the identification of patterns that might elude human detection. AI technology processes vast amounts of data, uncovering suspicious transactions, including split transactions.

Automated Transaction Monitoring Software

As businesses expand, the resources required for transaction monitoring grow. Ultimately, manual efforts become inefficient. This is where automated solutions, such as the one provided by FACEKI, prove invaluable. Automated software streamlines the monitoring process by simultaneously checking multiple transactions. Complex cases can be flagged for manual review, while routine transactions are automatically processed. This approach maximizes the number of approved transactions, ensuring regulatory compliance.

FACEKI’s Transaction Monitoring Solution

FACEKI’s Transaction Monitoring algorithms employ advanced analytics to distinguish between legitimate and fraudulent activities. The system analyzes transactions based on predefined rules, directing potentially risky transactions for manual review. When a transaction is flagged for review, a webhook action alerts the company’s compliance team, ensuring that swift action is taken when needed.

In conclusion, AML transaction monitoring is a critical component of safeguarding financial operations. By understanding the nuances of rule building and recognizing indicators of suspicious behavior, businesses can take proactive steps to protect their operations. At FACEKI, we are dedicated to delivering automated transaction monitoring solutions that streamline the process while ensuring regulatory compliance.

Contact FACEKI to book a demo today.