Recently, the Australian Sanctions Office (ASO) notified the public about the amendments to the Consolidated List. The Consolidated List got lengthier with the inclusion of a few more individuals and entities as a result of changes made to the United Nations Security Council Resolutions 1267 (concerning ISIL (Da’esh), Al-Qaida and associated individuals), autonomous sanctions on Russia and Ukraine, Myanmar and thematic sanctions.
Here, we look at five questions to consider about PEP and sanctions screening.
1. Is screening for adverse media necessary apart from PEPs and sanctions?
From an AML perspective, sanctions pose the highest risk followed by PEPs. Why? The repercussions of failing to abide by the obligations to implement sanctions could lead to serious consequences. The PEPs are high-risk individuals, for whom businesses need to adopt risk-sensitive measures including enhanced customer due diligence.
So, what about adverse media? Negative news on both persons and businesses can appear on multiple channels frequently. Some of these sources cannot be trustworthy as well. Businesses need accurate adverse media scans to determine the risks their customers may pose.
Opening an account for a customer with a bad reputation, or continuing to do business with such customers could add significant reputational risks to your business. Such negative media might even warrant a review of the customer risk profile. Therefore, screening customers against adverse media from reliable sources significantly improves the AML/CTF risk management processes.
2. There are too many false positives. Is there a way to minimise them?
Businesses should not only be concerned about false positives, but false negatives too. Reviewing hits can take considerable time and with limited resources at hand, some may struggle to go through all the matches.
Many matches turn out to be false positives. So, how can false positives be minimised while also not missing out any positive matches? Data quality plays a pivotal role to minimise erroneous matching, which is often neglected.
Providing as much relevant data as possible for matching could provide accurate matching. The matching algorithms are the key to reduce false positives and false negatives. The matching algorithm should provide adequate transparency as to how it works and it should also be flexible.
You probably need to spend some time familiarising yourself with how the algorithm works and which configurations suit best for different scenarios. Finally, you should continuously refine the configurations until you find the perfect balance.
3. How to optimise ongoing monitoring
In order to determine the frequency of ongoing screening of the customer base, a risk-sensitive approach is required. Setting up multiple monitoring schedules based on customer risk, screening sources (sanctions, PEPs, adverse media, etc.) and screening thresholds provides better management compared to having just a single schedule.
Further, if the screening solution allows, configure different matching parameters for the schedules. For example, sanctions screening poses a higher risk – and missing out on a positive match can have an adverse impact on a business.
Hence, less strict matching parameters, but with higher screening frequency is more suitable. In contrast, strict matching parameters with less screening frequency is better for adverse media. In this manner, businesses can manage the generated alerts following a risk-sensitive approach with the limited resources available.
4. How does name matching work?
The heart of PEP and sanctions screening is the matching algorithm and how effective it is. This is where fuzzy logic matching helps. Fuzzy logic matching is a technique of finding text fields that match a pattern approximately. This helps to match similar names, particularly when there are names with spelling differences.
After the matching, fuzzy logic generates a score or some ranking to illustrate the level of matching. This helps to determine whether it is a false positive or a true match. Although these matching algorithms are proprietary in nature, they should provide adequate transparency on how the matching works. Further, the screening solutions should have the ability to change the matching thresholds.
5. Why does KYC data quality matter?
A key element often overlooked in conducting PEP and sanctions screening is the data quality. If the names have not been captured properly or if the data is missing, it would not return better matching results. Therefore, it is paramount to ensure that customer KYC details, such as name, address, date of birth and country of residence are kept accurately, and up to date. Otherwise, even the most sophisticated PEP and sanctions screening solutions available would not be able to solve these problems for you.
Learn more here AML and KYC compliance requirements.
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