One of the most important requirements in preventing money laundering is that all financial institutions comply with and develop a risk-based AML program against laundering and terrorist financing risks.

A risk-based AML program includes Customer Risk Assessment , Customer Due Diligence , Enhanced Due Diligence , transaction monitoring, a dverse media screenings , and various monitoring and screening operations.

Transaction monitoring systems generate warning alarms in case of any risk. With these alarms, companies can detect customers' money laundering and terrorist financing activities and take the necessary precautions against such risks. However, every alarm may not indicate money laundering. More specifically, transaction monitoring shows a high sensitivity to all unusual activities. This situation often causes alarms to be generated even in situations that do not pose any risk.

What are False Positive Alarms in Transaction Monitoring?

One of the challenges of transaction monitoring is the occurrence of false positive alarms . False positive alarms occur when the AML software generates an alert or alarm based on a transaction that appears to be suspicious but in reality, it is a legitimate transaction. False positives can be costly for financial institutions as they can result in the unnecessary allocation of resources to investigate the transaction and cause delays in legitimate transactions.

The issue of false positives is not unique to transaction monitoring but is prevalent in many detection systems. In transaction monitoring, false positives can occur due to several reasons. One of the reasons is when a transaction has a common pattern that appears in legitimate transactions, such as regular payments to a vendor or regular salary payments. The system may flag such transactions as suspicious, even though they are legitimate.

Another reason for false positives is when the system lacks proper data integration. Data integration is crucial in transaction monitoring as it enables the system to correlate transactions across various accounts and transactions. If the data integration is not done correctly, it can lead to inaccurate alerts. Moreover, false positives can occur when the system has outdated or inadequate rules. Financial institutions must update their rules regularly to reflect changes in the regulatory environment and to adapt to new emerging risks.

The impact of false positives can be significant, resulting in increased costs and reduced efficiency. To mitigate the risk of false positives, financial institutions can adopt several strategies. One of the strategies is to review and analyze historical data to identify patterns and adjust the system's rules accordingly. Financial institutions can also invest in advanced technologies such as artificial intelligence and machine learning to improve the accuracy of the system.

Causes of False Positive Alarms in AML Transaction Monitoring

Several factors can contribute to false positive alarms in AML transaction monitoring. Here are some of the main causes:

• Incomplete or outdated data: AML transaction monitoring systems rely on accurate and up-to-date data to identify suspicious activity. If the system has incomplete or outdated data, it may generate false positive alarms.
• Unusual but legitimate transactions: Some legitimate transactions may appear unusual to the AML transaction monitoring system. For example, a customer may make a large cash deposit as part of a legitimate business transaction. However, if the AML system is not configured to recognize this type of activity, it may generate a false positive alarm.
• Lack of context: AML transaction monitoring systems rely on context to identify suspicious activity. If the system does not have enough context around a transaction, it may generate a false positive alarm. For example, if a customer suddenly makes a large transfer to a foreign account, the system may flag it as suspicious. However, if the customer has a legitimate reason for the transfer, such as paying for a vacation, the system may not have the context to recognize this.
• System configuration: The way an AML transaction monitoring system is configured can also contribute to false positive alarms. If the system is configured to be overly sensitive, it may generate more false positive alarms. On the other hand, if it is not sensitive enough, it may miss suspicious activity.

Why Should Businesses Prevent False Positives?

Businesses are required to constitute a Suspicious Activity Report for every suspicious transaction. Therefore, false positives increase the suspicious activity report requirement by creating alarms for unsuspecting transactions. This situation causes a loss of time for businesses and prolongs the actual criminal activities' reporting period.

Although not guilty, no customer wants to be treated as if they were guilty. However, false positives generate alarms for innocent customers, leading to many negative customer experiences. Many businesses lose good customers because of false positives.

Reduce False Positives

Transaction Monitoring and Screening solutions require the processing and analysis of company data. To distinguish correct AML alerts, the data processed should be as simple and straightforward as possible. Therefore, companies that organize data based on specific customer information rather than under a single heading will greatly reduce false positives.

Transaction with different names is one of the criminals' main methods to prevent AML checks. Therefore, businesses must create customer profiles suitable for customer data and correctly implement these profiles' verification processes. Accordingly, businesses' failure to update customer data (such as name or residence change) may result in false-positive alarms.

AML regulations are updated annually. Therefore, businesses need to be aware of current AML regulations and establish their rules in line with current regulations. Otherwise, profiles and AML applications not created according to existing regulations may cause false positive alarms. Also, institutions that do not comply with the regulations may be subject to various criminal sanctions.

How does Sanction Scanner help to Reduce False Positives?

Sanction Scanner Transaction Monitoring Software offers businesses the option to write dynamic rules on a wide scale, from the best-known scenarios to customized scenarios. Moreover, businesses can create these rules and scenarios that best suit their needs without coding knowledge. So businesses can focus on the right alarms and significantly reduce false positives.

However, name similarities can cause false positives to be generated. In this case, false-positive alarms will be inevitable for a customer with the same name as a criminal on the sanction list. Therefore, Sanction Scanner allows searching by a customer's date of birth, ID number, or passport number. In this way, customer scans are made with more specific information, and false-positive alarms caused by name similarities are largely prevented.