Transaction Monitoring: Rule Building

Transaction monitoring is a crucial aspect of financial institutions to identify and prevent money laundering activities. Rule building is a key component of transaction monitoring, where rules are designed to examine customer activity on a profile basis. It's important to note that the rule design should differ according to customer profiles, as each customer has unique characteristics and behavior patterns. In financial institutions, the profiles of the customers can be determined according to their income level, but other segments such as the types of products or services they use and their transaction history should also be considered when building the rule. By taking a comprehensive approach to rule building and analyzing customer behavior, financial institutions can effectively identify suspicious activities and mitigate potential risks.

Before Establishing Rules

Creating Effective and Optimized Rules

• Build your own rules: Customer groups may differ in financial companies. The same rules cannot be applied to customers in different segments. For this reason, companies may need to create their own rules. Compliance departments in companies divide customers according to some segments. As a result of these segments, customers are grouped. It is crucial to create rule sets for grouped customers. For example, a student's account statement is not the same as a businessman's. The same rule sets cannot be set for these two customers.
• Determine customer segments: Compliance teams should understand the customer base and segment customers based on factors such as income level, transaction history, and risk profile.
• Create rule sets for each segment: Once customers are segmented, compliance teams should create rule sets specific to each segment to effectively monitor and detect unusual or suspicious activity.
• Use Sandbox: Sandbox allows organizations to create new rules in the system or to change existing rules. Within the Sandbox, compliance officers can test the new rules. If they want to make changes to an old rule, they should test it on Sandbox. This environment is the environment for monitoring the scenario of the rules. You can try the rules in the Sandbox and check if it works the way you want.
• Test new or updated rules: Compliance officers should use the sandbox environment to test the effectiveness of new or updated rules before deploying them to the live system.
• Optimize rules and rule sets according to results: Rules must be chosen effectively to get better results. According to sandbox outcomes, officers can select the most effective scenarios.
• Update the rules: Compliance departments must keep the rules up to date. The rules may need to be updated as there is a constant flow of new customers in financial institutions. Compliance officers need to check if the rules are working and add new ones where necessary. These new additions must first be tested in the Sandbox. The entire compliance department should be aware of the newly added rules.
• Establish a process for updating rules: Companies should have a defined process for reviewing and updating rules on a regular basis to ensure they remain effective and up to date.
• Test new rules in Sandbox: Compliance officers should simulate new or updated rules in the sandbox environment before deploying them to the live system.
• Communicate updates to the compliance team: The entire compliance team should be aware of any newly added or updated rules to ensure consistency in monitoring and detection efforts.
• Use Machine Learning and AI: Building rules are very effective in controlling financial crime. But criminals will seek new ways to launder money without breaking the rules. Artificial intelligence should be used to prevent this. With machine learning, you can identify out-of-the-norm anomalies and behavior patterns.
• Incorporate machine learning and AI: Companies should leverage machine learning and AI to identify unusual or suspicious activity that may not be captured by traditional rule-based monitoring.
• Train models regularly: Machine learning models need to be trained regularly with new data to ensure they remain accurate and effective in identifying potential financial crime.
• Continuously monitor and adjust models: Companies should monitor the performance of machine learning models and adjust them as necessary to improve accuracy and reduce false positives.
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Sample AML Compliance Rules

• Profile change before huge amount transaction: When a customer modifies their personal details just before a major transaction, an alarm is triggered as per this regulation. Such an occurrence may suggest an account takeover or potential "layering" tactic to obscure the flow of funds.
• Abnormally high transaction: This particular rule is designed to pinpoint parties who have an unusually high volume of payment transactions. It's a good fit for a peer-to-peer payment network that allows funds to be withdrawn to an external account.
• High increase in overall transaction volume: This rule pertains to an increase in the overall transaction volume. Specifically, it identifies parties that have conducted recent transactions with a significantly higher value than their average over the last 7 days. The rule excludes parties with short-standing histories, low balances, and low outgoing transaction values.
• Suspicious transaction: If a customer's behavior deviates from their usual patterns, it may indicate an account takeover or an externally influenced transaction. This is particularly relevant in the case of high-value transactions.
• High transaction from a new user: This rule pertains to transactions made by new users with high volume. Traders become alert when they notice a large portion of their trading activity comes from recently created accounts, which can be a potential warning sign.
• Geographic anomaly: This rule detects transactions that originate from locations that are geographically inconsistent with the customer's known location. It's a good fit for companies that have customers who travel frequently or operate from different locations.
• Split transactions: Split transactions involve dividing a large transaction into smaller ones to avoid detection. This rule identifies customers who have conducted multiple transactions that are just below a specified amount limit within a short period.
• Structuring transactions: This rule identifies customers who frequently structure transactions in a manner that is intended to evade reporting requirements or hide the true nature of the transactions.
• Round sum transactions: Round sum transactions are often used to disguise the true value of a transaction. This rule identifies customers who have made transactions that are rounded to a specific value or that have a repeating pattern.
• Unusual activity during non-business hours: This rule identifies customers who engage in unusual activity outside of normal business hours. This may indicate that the account has been compromised or that the customer is attempting to avoid detection by conducting transactions during off-hours.
• Unexplained customer behavior: This rule is a catch-all for any unusual customer behavior that cannot be explained by other rules. It may include suspicious patterns of transaction types, unusual transaction times or volumes, and transactions with high-risk countries or counterparties.

Dynamic Fields for Rule Building

Sanction Scanner Transaction Monitoring software provides its customers with an innovative feature that allows them to create their own rules through dynamic fields. With this feature, customers can easily customize and tailor the software to fit their specific needs and requirements.

This means that customers can determine all the indicators they want to monitor and set rules based on them. For example, a company may want to monitor transactions from certain countries or flag any transactions that exceed a certain amount. With Sanction Scanner's dynamic fields, customers have the flexibility to create rules that suit their business objectives. The process of creating rules is straightforward and user-friendly. Customers can easily name their rules and modify them as needed. 

The ability to create custom rules is a significant advantage for Sanction Scanner customers. It allows them to stay ahead of potential risks and ensure compliance with regulations. By using this feature, customers can enhance their due diligence process and streamline their compliance efforts.

Rule-Based Transaction Monitoring Software

Rule-Based Transaction Monitoring Software is an essential tool for financial institutions to monitor customer transactions and identify potential financial crimes. The sheer volume of transactions processed daily makes it impossible for compliance officers to manually review every transaction. That's where the Transaction Monitoring Software comes into play.

This software enables institutions to control transactions based on pre-set rules, generating an alert whenever a rule violation occurs. These alerts are then displayed on the compliance team's screen for review. Compliance officers can then determine whether the transaction is suspicious or not.

Creating and managing rules manually can be a time-consuming and complicated process. That's why many financial institutions prefer to purchase third-party software to streamline the process. Sanction Scanner's Transaction Monitoring Software provides a user-friendly solution for compliance teams, enabling them to apply pre-set rules or create their own, and choose from a range of ready-made rules to integrate into their system. This software helps compliance officers efficiently manage alerts and ultimately protect their institution from financial crime.