Continuous screening challenges | Part 1 - Managing watchlist data

Continuous screening challenges | Part 1 - Managing watchlist data

Deep Pai

4 Mar 2024

Continuous screening has been an anti-money laundering and sanctions compliance requirement for some time now. However, many financial services firms struggle with the data management required to support this compliance process. Watchlist data is one of the two main data inputs to screening, the other being client or payment data. Firms face several challenges when seeking to manage their watchlist data, such as updating sanctions in a timely manner, ensuring high accuracy of watchlist data, integrating and de-duplicating multiple input watchlists into a single fit-for-purpose output and having sufficient transparency to understand how the watchlist data was obtained and how it is being used. Failure to tackle these kinds of challenges can lead to increased compliance and risk exposure.   

Today, regulators and financial institutions expect sanctions screening watchlists to be updated in minutes – and those updates then applied to both transaction screening and continuous client screening engine with minimal latency. They also want firms to manage their watchlist data better, and to understand the underlying information that is being used to make decisions.  

Speed versus quality 

A sanction is announced first in a press release, and later added to the sanctions list that is published by a government body. Commercial list creators then add this information to their own databases, which takes time. As a result, there can often be a 12 to 24 hours delay in the change in sanction information reaching the financial services firm. During that delay, a sanctioned entity could transfer money or complete other transactions leaving the financial firm with a potential sanctions breach leading to regulatory reprimand, fines, and reputational damage. This makes automating ingestion and management of watchlists of utmost importance. 

Balancing Speed and Accuracy in Watchlist Data Management

Although speed is now essential, it’s just as important that sanctions watchlist data is also of high quality. For example, the sanctions data could include a common name that could inadvertently trigger thousands of false positives. One recent trend among criminals is to choose names for ships that are very common – such as Bella, Patriot, Alert, Alpha, Amber, Christina, Dan, and many others. These names would result in thousands of alerts if they were put straight into a vessel screening system. High volumes of false positives would place a massive burden on the investigations team, result in cancelled or blocked customer transactions, and potentially result in increased compliance and risk exposure. 

It's also important for compliance teams to be able to understand how data is being used to make decisions by the system. If a piece of watchlist data does prove to be unexpectedly problematic, compliance teams need visibility into why the data is driving particular decisions. Without this transparency, it can be challenging to track down the data that caused the issue, resulting in higher levels of false positives and their associated costs.  

Ingredients for watchlist data management 

Such issues make managing watchlist data extremely important, particularly for continuous monitoring. To help ensure that watchlist data is of the highest quality, watchlist data management solutions should include:  

Essential ingredients for effective WLM
  • Automated watchlist updates – Today many firms only have manual processes in place to update their watchlist data, or work with list vendors who take considerable time to update their information. This results in unacceptable levels of latency, increasing compliance and risk exposure. Automating watchlist updates can dramatically reduce the time and resources needed to reach compliance.  

  • Robust data quality checks – A watchlist data management solution should have strong data quality checks that can detect problematic sanctions data and prevent it from being deployed. The system should send an alert so that the compliance team can review the problematic data. 

  • Transparent data decisions – To ensure high data quality, it’s essential that compliance teams can see how watchlist data is being used to make decisions – something that AI-based tools often struggle to deliver. Transparency enables compliance teams to understand if watchlist data is causing an issue and remedy the situation.  


With a good watchlist data management solution in place, compliance teams can significantly benefit by automating manual and semi-automated watchlist data operations. This will reduce time and resources required to reach compliance, improve the quality of their watchlist data and significantly reduce the potential exposure to compliance risks.

Stay tuned for our next blog where we cover data quality and data management principles for watchlists.

To find out how FacctList can revolutionise your watchlist management, write to us at 

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