Financial institutions are required to have robust Anti-Money Laundering (AML) programs to detect and prevent money laundering and other financial crimes. A key component of any effective AML program is monitoring transactions and customer behavior for suspicious activity.
AML monitoring scenarios are used by compliance officers to identify transactions that may be indicative of money laundering or other financial crimes. By setting up and monitoring specific scenarios, financial institutions can proactively detect and investigate suspicious activity, helping to prevent the flow of illegal funds.
In this article, we'll explore what AML monitoring scenarios are, how they work, and why they are essential in protecting the integrity of the financial system.
Let's dive in.
What Are AML Monitoring Scenarios?
As mentioned, AML monitoring scenarios are specific scenarios that AML compliance officers use to monitor transactions and customer behavior for signs of money laundering or other financial crimes, and typically involve a set of parameters that are used to identify unusual or suspicious activity, such as transactions that exceed a certain threshold, transactions involving high-risk countries or individuals, or transactions that are inconsistent with a customer's profile or history.
AML Monitoring Scenario Examples
Five examples of AML monitoring scenarios include:
- Structuring: Monitoring for transactions that involve structuring, where a customer breaks up larger transactions into smaller ones to avoid detection or scrutiny.
- Unusual geographic activity: Monitoring for transactions or patterns of transactions that are inconsistent with a customer's geographic location or history, such as transactions originating from high-risk countries.
- Excessive cash deposits or withdrawals: Monitoring for transactions that involve large cash deposits or withdrawals that are inconsistent with a customer's profile or history.
- Round-tripping: Monitoring for transactions where funds are sent offshore and then immediately sent back to the originating country, which is a common method used to hide the source of funds.
- Transactions with sanctioned individuals or entities: Monitoring for transactions involving individuals or entities that are on government sanctions lists.
These scenarios can be customized to suit the specific needs of a financial institution, based on its products, services, and customer base. By using AML scenarios, compliance officers can detect and investigate suspicious activity, which can help prevent money laundering and other financial crimes.
How Do AML Scenarios Work?
Transaction monitoring systems give financial institutions the ability to watch over their clients' or customers’ transaction behavior. By providing current scenarios and/or transaction monitoring rules, the monitoring system can alert institutions of suspicious activity that may be linked to money laundering or other financial crimes.
Investigators then determine if these alerts are actually linked to unusual activity and if so, they will fill out suspicious activity reports (SARs) or another type of report for financial authorities.
However, several financial institutions don’t re-assess the effectiveness of the alerts and whether or not there is a need to adjust the current threshold or even create new monitoring scenarios. This absence of tuning happens if:
- When going back into the transaction monitoring system, there is not a feedback loop from the alert investigations phase. This means that the information collected during the alert investigation stage can’t be leveraged by the automated transaction monitoring system
- There’s not a repeatable process in place requiring the financial institution to continuously re-evaluate thresholds and scenarios, or a process that analyzes to determine if changes are needed.
The lack of intermittent scenario tuning frequently results in several false positives, which results in a delayed alert investigation. This ultimately results in missed reporting deadlines and fines.
Challenges of Utilizing AML Monitoring Scenarios
Financial institutions tend to face several challenges regarding consecutive scenario tuning.
The availability of alert investigation information isn’t obtained for use in future scenario tuning phases and even if the alert investigation information is obtained, it wouldn’t be in a data structure that works for data analyses or management information reporting.
In situations where the use for scenario tuning is identified, it is mainly concentrated on the problematic scenario/s at hand rather than in-scope scenarios, resulting in inconsistent performance of the scenario tuning process and ultimately won’t be maintained by consistent documentary evidence in case of regulatory scrutiny.
Measuring Tuning Effectiveness or Success
Only confirmed positive cases result in SARs being sent to financial authorities, meaning that it’s harder to tune the transaction monitoring system on solely the alert-to-SAR ratio in addition to measuring the overall effectiveness of the transaction monitoring system. Sufficient measurements of success include red flag coverage and as few criticisms of the transaction monitoring symptom by regulators as possible.
Opportunities of a Systematic AML Monitoring Scenario Tuning Process
When connected to an anti-money laundering (AML) system, a systematic scenario tuning process gives a financial institution to get over the challenges listed above, and will ultimately help the system in the following ways:
- Reducing false positives – By administering a systematic scenario tuning cycle, the institution will be capable of determining more specific thresholds, since these values are coming from previous information collected at the investigation level in addition to conducting advanced data analyses.
- Better alert scoring –Alert scoring is done to further efficient alert assignment to investigators. A fine-tuned scenario process will be more likely to produce real positives, therefore, bettering the effectiveness of alert scoring.
- Identifying Redundant Scenarios – The financial institution can identify redundant scenarios (meaning they are ineffective) by ensuring a constant information feedback loop. This analysis then gives real data for getting rid of nonproductive scenarios from the production environment.
- Measurement of Success – By having an official tuning process that considers risk management, institutions can then present success factors other than heightened cases and filed SARs. These factors include clearly explaining which known money laundering red flags are moderated by each implemented scenario. This makes it easier to identify activity that may be referred to by law enforcement as well as to present an effective tuning method that works well for regulators.