Best Practices for Implementing Effective AML Transaction Monitoring

Best Practices for Implementing Effective AML Transaction Monitoring
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Money laundering has always been a hot topic for financial institutions. None of the financial institutions wants to lose their reputation by becoming the center of money laundering activities. Even though financial institutions do not wish for it, around 90% of money laundering activities go unnoticed. Authorities recover only 0.1% of the total funds during anti-money laundering operations. In such a case, AML Transaction Monitoring is essential for financial institutions to mitigate the risks. Some financial institutions need to implement effective practices for transaction monitoring. 

Read on to understand the best practices for implementing effective transaction monitoring. 

Determining Rules for Transaction Monitoring 

Selecting money laundering detection rules is the first step for any financial institution. Money laundering detection tools rely on some pre-decided rules. The transaction monitoring system generates an alert when a financial activity exceeds the pre-defined rules. The AML rules must be effective and help the financial institution uncover money laundering attempts. The organization’s analysts and money laundering experts must sit and decide on the models and rules for transaction monitoring. The chosen models and AML rules must cover all areas of suspicious activity detection. 

Here are some rules for financial institutions that can help detect suspicious activities:

Data Preparation

Data is the main factor behind the success of a transaction monitoring platform. A transaction monitoring system cannot perform to its fullest potential when the data quality is poor. Many financial institutions rely on AI-led transaction monitoring systems for detecting money laundering attempts. The algorithms will never become smart if you do not feed quality data to AI-led systems. Ensuring a continued supply of high-quality data is a severe challenge for financial institutions. It might be the sole reason why your anti-money laundering strategies are failing. Focus on building a holistic data environment with data quality controls. Financial institutions can invest in data cleaning systems that forward high-quality data to monitoring systems.

Must Read: The Role of Transaction Monitoring in Combating Financial Crime

Segmentation of Customers

Segmentation is a process in AML that allows financial institutions to classify customers into different groups. A group will consist of customers with similar financial behavior. Once all groups are assigned, monitoring their financial activities and generating alerts will be easier. Since financial transactions have become more complex, financial institutions must believe in activity-based segmentation. Financial institutions must explore customers’ financial habits to create segments. 

Let us understand the concept of smart segmentation with an example.

Assume an organization creates customer segments for AML transaction monitoring. In one segment, it places all customers with transactions worth more than a million. For an individual, the amount of a million might be suspicious for a bank. The same amount might not be suspicious for a large-scale corporate entity. The financial institution will have individuals and corporate entities as its customers. It needs to develop better segmentation rules and place similar customers in the same group. Creating a segment with similar customers makes rules for detecting suspicious activity the same for every customer.

Using Monitoring Systems with Alert Hibernation Properties

Anti-money laundering experts within a large financial institution might experience huge alerts. Employees feel demotivated due to the investigative burden. They have to go through every single one of the alerts manually. On the other hand, they can use AML transaction monitoring systems with alert hibernation properties. Such a monitoring system will automatically snooze alerts that are likely to be false, reducing the investigative burden on employees. As a result, anti-money laundering experts will have the time to focus completely on red flags that are likely to be caused due to a money laundering attempt. Reducing the manual burden on employees will make your anti-money laundering strategies successful. In most cases, anti-money laundering teams need more time to focus on actual cases due to redundant tasks.

Invest in Proactive Suspicious Activity Detection

Most financial institutions are reactive when it comes to anti-money laundering operations. It means they react after a money laundering incident has occurred or come to notice. The aim for financial institutions should be to become proactive in identifying money laundering threats. It can happen when a financial institution invests in AI-led AML transaction monitoring systems, which use predictive analytics models for determining suspicious activity ahead of time. With AI-led pattern detection, predictive modeling, anomaly detection, and statistical analysis, a financial institution can become proactive in its anti-money laundering operations.

In Conclusion

A financial institution might need an updated AML strategy to get desired results. It is time to introduce anomaly detection tools controlled by AI/ML for anti-money laundering operations. A financial institution can seek support from a third-party research firm. The third party can help the financial institution adopt the right AML strategies. Implement effective AML strategies right away!

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