AML Transaction Monitoring: Issues and Keys to Success

Tools for AML transaction monitoring enable financial institutions to monitor their end-user transactions for suspicious activity. To generate a more complete view of the customer, these tools should also be able to incorporate analysis of historical activity and the customer’s account to determine the customer’s risk profile and predict future behavior.

AML transaction monitoring information can then be used in reporting as well as to flag and review suspicious behavior. The tool should be capable of monitoring more than simple financial transactions. It should enable the monitoring of a wide range of events that provide signals of possible money laundering.

Transaction monitoring tools give AML teams the control to make rule adjustments, reduce false positives, and minimize the reliance on engineering resources. Ideally, the tool provides baseline scenarios complete with pre-built rules, filters, and parameters to serve as foundation for more sophisticated models.

AML transaction monitoring is required for compliance with the Bank Secrecy ACT (BSA) and makes possible the mandatory reporting to FinCEN and other regulatory bodies worldwide.

Issues Associated with AML Transaction Monitoring

False positives: the number of cases flagged by a transaction monitoring solution that do not meet the criteria for review.

According to a recent global study by LexusNexus Risk Solutions, 57% of the total cost of compliance with financial crime-related regulations is labor, amounting to $103 billion. This is due, in part, to the fact that more and more analysts are needed to review the increasing volume of alerts from transaction monitoring.

False positives both increase labor expense, and distract analysts from focusing on the true cases that require thorough investigation in a timely manner.

Assuming that one size fits all: applying a single risk scenario to a broad range of customers or behaviors.

The oversimplification of risk scenarios in AML transaction monitoring can lead not only to an increase in false positives, but also to less granular monitoring. The lack of detail of a broad scenario can obscure visibility into the true activity and behavior of the company’s or individual’s transactions. It also limits the application of relevant rules to the scenario, leading to potential missed flags.

Too many scenarios: the opposite of the one-size-fits-all issue, it’s possible to have too many risk scenarios in an attempt to catch every suspicious activity.

Over time, as business conditions change, new scenarios are often introduced to address new perceived threats. However, having too many scenarios makes it difficult to maintain context among the scenarios, and to delineate which scenarios are addressing which activities/behaviors.

Depending on how these new scenarios are scoped and introduced, it can become difficult to manage which scenarios address which threats, as well as how and when analysts should invoke these scenarios. It also increases the risk of duplicate cases being generated off of different scenarios for the same threat.

The ability to review scenario effectiveness in-context on an ongoing basis is key to ensuring there are no gaps in AML transaction monitoring, nor overlap among scenarios that may result in redundant cases.

Key Components of Successful AML Transaction Monitoring

• Flag suspicious activity or events – this could be related to the financial institution or the individual customer

• Adjust rules to map to risk profiles – customize rules and models to reflect actual customer scenarios and risk profiles

• Optimize – continue to fine-tune models

• Build credibility with regulators – develop a track record of accurate reporting, minimal false positives, and full auditability

• Be independent – deploy quickly and customize without tapping engineering resources

Customers using Unit21’s fully customizable, no-code AML transaction monitoring solution have been able to reduce false positives by up to 50% while maintaining both the granularity necessary for detailed analysis, and the context to minimize redundancy among investigations.

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