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Finding the right methods to detect money laundering and terrorist financing (ML/TF) is becoming more and more important to financial institutions across the globe. Recognizing the pivotal role financial institutions play in stopping the movement of money for criminal and terrorist organizations, governments and regulatory bodies are stepping up both the requirements and the fines for non-compliance.


The Problem with Finding Money Laundering Using Traditional Techniques


Performing ML/TF detection on existing customers is limited by a lack of knowledge of exactly how to find these rare activities in the vast amounts of data that flow through the global financial systems. Traditional approaches use methodologies that have been somewhat successful against fraud and cyber intrusions; however, there are some key differences between those activities and ML/TF that require a new approach, one developed specifically for ML/TF suspicious activity detection. The primary key differences relate to two questions:


How rare is the event?

How likely is detection?


Most cyber attacks and fraud are committed by actors outside the financial institution, who will continuously attempt, usually in an automated fashion, to steal money or information through credential spoofing, internal network infiltrations, fraudulent transactions, or any number of other external attacks. Money laundering and terrorist financing, quite differently, are directed by customers of the financial institution using the institution's infrastructure; importantly, they do not aim to cause direct harm to the institution. ML/TF activity is not only more rare, being performed manually and by fewer actors, it is much more difficult to detect – fraudulent transactions or account actions will be detected by the institution’s legitimate customers, who are each monitoring their own accounts, whereas ML/TF activity must be discovered by investigators.


Building AML-Optimized Technologies

To better detect ML/TF activity, institutions are learning that they must invest in innovative technologies. This tech needs to

  • Take a holistic approach to detecting suspicious accounts

    • individual transactions are rarely in themselves indicative of ML/TF

    • investigators need better tools as they are the primary line of defense

  • Adapt to changing criminal techniques

    • empowering analysts to easily implement and deploy new models and analytics as needed

  • Incorporate artificial intelligence

    • enabling the system to learn & perform the day-to-day tasks such as data gathering and initial data mining, freeing investigators and analysts to focus on future threats.


Focus on AML Software

The fight against money laundering and terrorist financing is a difficult one. But investments in the right technology can pay dividends many times over. At Clovis Technologies, we're building innovative technology for detecting money laundering and terrorist financing. Contact us today to find out more about how we can help you develop and implement an AML-focused solution.