KYC AML Guide: the Clock shows the average reeding time of the blog06 min Read


KYC AML Guide: the Clock shows the average reeding time of the blogMay 8, 2023

Machine Learning and Effective AML Ongoing Monitoring

Anti-Money Laundering (AML) in financial services and financial institutions requires close monitoring of the transactions and other procedures. It is done to detect and prevent money laundering and other financial crimes well before time. AML Ongoing Monitoring is crucially important for ensuring streamlined compliance with AML regulations. This article is a knowledge-based concept builder on how effective AML Ongoing Monitoring has become with the use of Machine Learning.

M Abd'al Bari

Research Associate

What is AML Ongoing Monitoring?

In simple words, Ongoing Monitoring or Continuous Monitoring is an iterative but systematic process of refreshing and updating clients’ data for KYC (Know Your Customer). It reflects the current trends of KYC and AML data and ensures that AML compliance regulations are fully met.

It is crucially important for Financial Institutions to have ongoing monitoring of the AML system. Generally, the AML Ongoing Monitoring Frequency can vary as per the requirements of the AML compliance program. Financial institutions such as banks might conduct daily, monthly, weekly, or quarterly monitoring. It depends on the customer’s risk level and suspicious activity.

Why is Ongoing System Monitoring Important?

General Importance

Generally, ongoing system monitoring is crucially important for any compliance system. For enhanced system performance and security, ongoing monitoring helps in reducing downtime and increases the reliability of systems. It also provides authentic and updated data which builds foundations for future improvements and updates.

AML’s perspective

Specifically in the Anti-Money Laundering (AML) system, ongoing monitoring plays a vital role in updating and enriching the customer’s database according to the risk profiles. Through continuous & systematic monitoring of transactions, customer profiles, and behavior, AML systems can timely identify money laundering and terrorism funding trend. AML Ongoing Monitoring can trigger the red flags and report for further investigations and actions based on robust monitored activity reports that are commonly known as SAR (Suspicious Activity Reports).

How Machine Learning has affected AML Ongoing Monitoring?

Since Ongoing Monitoring essentially embraced Machine Learning over the past few years, thus the accuracy and effectiveness of AML Ongoing Monitoring Systems have increased over time.

Here are the main concepts that paint a clear picture of the effects of Machine Learning in Ongoing Monitoring as per AML systems:

Anomaly Detection Algorithms

These algorithms have helped in identification of the suspicious transactions mainly. Such patterns of transactions that depict money laundering activities are identified through predictive analysis. This approach can help in anticipating potential money laundering risks and prioritizing ongoing monitoring activities. Additionally, Natural Language Processing (NLP) and Sentiment analysis are also used to identify the potential risks and raise red flags.

In Furtherance, the following is a list of concepts that have paved the way for Machine Learning to become an essential part of AML’s Ongoing Monitoring.

  • Concept Drift Detection: concept drifts occur when data updates change over time. It causes outdated data and reduces its accuracy over a period of time. The concept drift techniques identify and adapt to periodic changes and continuously monitor the data input, data distribution, and update the data accordingly.
  • Automated Model Retraining: AMR is a technique that ensures that the accuracy of the data is maintained above a certain threshold through Model Retraining through Machine learning.
  • Dynamic Model Selection: Multiple Machine Learning techniques and models are also used sometimes to tackle specific and complex problems. Based on the input data, these techniques and models can be switched and the most-suitable ones are automatically selected.
  • Adversarial Attack Detection: Compliance systems are vulnerable to deliberate attacks for system & data manipulation or bugging the machine learning. Adversarial Attack detection techniques monitor a system’s performance and detect abnormal or suspicious behavior that indicates an attack.

Machine Learning is considered a technological breakthrough in AML Ongoing Monitoring. However, Machine Learning Algorithms have their limitations. They can only perform as well as they are trained logically. It means that an Ongoing Monitoring System in AML will be having access to high-quality and accurate data but will be limited to only taught knowledge. It won’t be able to detect new human behavior. According to many AI experts, the limitations of machine learning can lead to the duplication of the same data sets in abundance. Similarly, the issue of false positives and false negatives can lead to unnecessary investigations and missed suspicions. This will increase compliance costs and decrease the reliability of the Ongoing Monitoring system

Final Thoughts

AML Ongoing Monitoring has certain long term benefits. Undeniably, the customer’s data sets recorded, updated, and stored through Machine Learning in Ongoing Monitoring will combine two robust concepts in compliance. This has increased the speed, and accuracy and reduced the overall cost of securing financial institutions from Money Laundering and Terrorism Funding. But again, this is not a final solution. Compliance teams including the MLROs and other professionals need to ensure the necessary data and implementation of the algorithms with processing through the human eye.

Lastly, adequate planning and execution are important in machine learning and guidelines by professionals can play a key role in guiding AML Ongoing Monitoring.


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M Abd'al Bari
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Muhammed Abd'al Bari is a certified Research Professional of KYC/AML Guide. Connect with Muhammed on LinkedIn