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How to approach sanctions screening

The latest chapter in our blog series on sanctions screening, where we outline best practices for approaching the sanctions screening process.

Luca Primerano
August 10, 2021

Welcome to the fourth part of our blog series on the challenges of sanctions screening, which seeks to highlight the issues with and solutions to common, costly pitfalls in screening processes.  

In last week’s blog we looked the causes of false positives in sanctions screening processes, focusing on legacy systems and processes, and the costs of these problems for organisations if not addressed.

In this blog, we explain how best to approach sanctions screening to identify risk accurately and minimise the number of false results.

6 things you should aim to do in your sanctions screening approach  

The overriding aim in sanctions screening must be to identify sanctions risk correctly within customers, suppliers, employees, and transactions to minimise false positives and false negatives.  

1. Capture data in a clear, structured way  

With so much data to be processed and analysed, it is essential systems capture data in a clear, structured way. This means, for example, gathering title, first, second, and last names in different fields. Not only does this avoid any potential ambiguity, but it becomes easier to match against data in an external sanctions/data list.  

With careful data capture design, the risk of a data capture error should be minimised, such as inserting a first name into a surname field.  

For an example of structured data, see this OFSI resource.

2. Use as much data as possible  

The more relevant data you have for verification, the lower the risk of false positives and false negatives occurring. Of course, data is not always available and the desire of criminals and terrorists to remain undetected will be high. This drives tactics like changes of name and moving from one country to another, to remain under the radar.  

3. Improve matching algorithms  

Organisations should use different matching algorithms that account for different cases. These should be weighted based on the scenario and contribute to the overarching score that determines the sanctions risk.  

For example, for a company name a higher importance can often be placed on the first name in comparison to the other names, which are often more common and less relevant when it comes to matching. Consider, for example, Test123 Management Consulting Limited and Test360 Management Consulting Limited.  

The ability to calculate average scoring and standard deviation is also beneficial as it may give important information on whether different approaches “agree” in considering whether an alert is a false positive or a true hit.  

To reduce false positives and false negatives, care should be taken when combining matching algorithms and configuring them based on language, scenarios, and company policies. High order correlations between scores are also problematic because they are difficult to detect without using advanced analytics.

4. Continuously improve the screening process  

Reviewing the output of the screening process, including reviewing hits and false positives, may help improve the rules and scenarios that have been configured in the system, or potentially help improve the policies being adopted.

5. Use whitelists to your advantage  

The opposite of blacklists, whitelists can be used to support sanctions compliance efforts by identifying and saving repetitive matches that materialised as a false positive.  

Whitelists can help reduce the occurrence and cost of false positives but can increase the risk of false negatives. In the case of a sanctions breach, it can be difficult to explain satisfactorily to regulators why further investigations were not made. Whitelists should therefore be used with caution, and only as a means of supplementing screening processes.

6. Use alternative scoring  

Using traditional distance-based scoring alone can lead to excessive levels of false positives because thresholds are set manually. Using alternative scoring in addition to distance-based scoring, however, can complement and enhance the screening process, leading to more reliable results. For example, machine learning generated classification scores can consider different dimensions within a match, such as average length of words, average similarity score, and maximum similarity score.

Sanctions screening: how to reduce false positives in client and transaction screening

So far, we have looked at the principles of sanctions screening and why it is important for organisations to comply with screening regulations.

Today’s blog has outlined best practices for sanctions screening processes, from the use of relevant and clear data to the use of whitelists and alternative scoring methods.

In the next and last chapter in this series, we will look further at how technology, like that of Napier, can revolutionise the sanctions screening process to minimise false results, help compliance officers and analysts work more efficiently, and protect organisations from costly penalties.

This article is the fourth in a series of a larger paper authored by Napier.  

If you would like to read the full paper, you can download it here.

Luca is the Chief AI Officer at Napier, where he focuses on running artificial intelligence programmes partnering with tier-1 universities and clients. Luca has extensive experience in decision automation and digital transformation that he gained at Goldman Sachs, Deutsche Bank and Deloitte and has an MS in engineering from Politecnico di Milano specialising in anomaly detection, data correlation and pattern identification.
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