Sanctions Compliance

Reducing False Positives in Sanctions Screening: Here's How

Reducing false positives in sanctions and anti-money laundering (AML) screening is paramount for compliance professionals. Why? Because false positives take up so much time and can also negatively impact the customer experience. This article will teach you all the crucial things to know to improve, from leveraging AI and machine learning to enhanced data quality - plus a lot more.

Paul Dixon
May 5, 2023

What Are False Positives in Sanctions Screening?

False positives in sanctions screening (and all screening in general for AML) are events where individuals or entities incorrectly appear as a match. And it's a problem. Why? As many of you reading know, it takes time and resources to investigate the 'match'. It can also harm the customer experience.

So reducing the false positive rate in a sanction check is all about working smarter. And freeing up more valuable time to focus on the genuine risks to the business. 

False negatives are also a big problem. This is when sanctioned individuals or entities, who may be involved in serious crimes such as terrorism and human trafficking, slip through cracks in the sanctions screening process. The tips revealed in this post also help prevent false negatives.

Why Reducing the False Positive Rate Is Challenging 

It's a significant challenge to reduce the false positive rate. And at the crux of the problem is name-matching. It should be simple - comparing a name on a global sanctions list with the name of a customer (or any business stakeholder) - but it's not. Here are some of the reasons why:

  • Similar names
  • Variations in name spelling
  • Cultural naming conventions
  • Incomplete or outdated data
  • Transliteration issues

And in this graphic, you can see how some of the above challenges manifest themselves in actual examples:

Name matching issues in sanctions screening graphic

You can learn more about name-matching issues in this detailed article

But in a nutshell, although there is no panacea, improved technology and processes that increases the name-matching accuracy score will reduce false positives in sanctions screening. For example,'s AI-powered software blends machine learning with traditional name-matching approaches.

Reducing False Positives In Sanction Screening: Here's How

You now know why reducing false positives poses such a challenge in sanctions screening programs. But now for the good news. There are many achievable ways to make big improvements.

1. Push for Resources That Enhance Data Quality & Design

One of the essential facets in reducing false positives in sanctions and AML screening is capturing data from customers and business partners clearly and effectively. In the most basic form, it means TitleFirst Name, and Last Name in three fields.

This sounds simple enough. But you might be surprised how many data sets suffer from inconsistencies, misspellings, or incomplete information. And remember this: Inserting a first name into a surname field may trigger a false positive.

This is where professional data managementdata capture design, and data quality come into play. 

Fortunately, compliance teams have some trends working in their favor. First, many compliance officers are becoming data-collection and management gurus. Why? Because data-protection regulations, such as GDPR, also require expertise and knowledge in the field. And secondly, companies of all sizes - from small, medium-sized, and larger with thousands of employees are transforming digitally

And the bottom line is this: Compliance teams must advocate for the resources that increase data quality within their organization to reduce false positives (and false negatives) in sanctions and AML screening. You can read more about it in this blog post.

2. Collaborate Closely With Sales and Marketing Teams

They say customer data is one of a business's most valuable assets. And sales and marketing teams can be excellent at leveraging customer data to drive growth. And this is where sales, marketing, and compliance teams can work together collaboratively - rather than being siloed off. 

Compliance teams should work closely with their colleagues to find ways to collect and structure more valuable data for compliance objectives - such as sanctions, AML, and criminal watchlist screening. Innovative compliance teams are now finding ways to integrate data from social media profiles, marketing surveys, and transactional sales information in their regulatory programs. 

And remember, sanctioned individuals and criminals may be involved in some of the most heinous crimes on the planet, such as terrorism and human trafficking. The more data you have about them (legally and compliant) - the more likely you will reduce false positives in your screening programs.

3. Utilize Effective AI-Powered Sanctions Screening Tools

The next way to rapidly reduce false positives in your sanctions and AML screening program is by embracing the incredible AI-powered tools that are hitting the market.

Nefarious individuals and entities, many of whom may be on global sanctions lists, already creatively use AI, such as ChatGPT, to spoof anti-fraud and AML screening systems. You can learn more about it in's ChatGPT and Financial Crime article. 

And if the criminals are using AI - then, as the saying goes, you can only fight fire with fire. 

Here are some of the benefits that AI and machine learning already bring to reducing false positives in sanctions screening:

  • Enhanced Accuracy
  • Improved Efficiency
  • Advanced-Data Analysis
  • Risk-Based Prioritization
  • Continuous Monitoring

Utilizing effective AI-powered screening tools to reduce false positives cannot be emphasized enough. Here is why: Name matching is at the crux of the false positives' conundrum. Before, fuzzy logic (although it is still used) was an innovative technique to overcome problems with bad quality data in sanctions and AML screening.

But just like the tractor replaced the horse plowing the field, AI is already significantly reducing false positives. 

And the reality is this: Eventually, everyone will be using AI in screening (like the tractor in farming). So the sooner compliance teams adapt and embrace AI in their screening processes, the better prepared they will be to mitigate future risks.

Final Thoughts and How Can Help

In this article, we looked into three principal areas that can be improved to significantly reduce false positives (and false negatives) in sanctions and AML screening. In summary: Get better at data quality (DQ), collaborate with your colleagues in sales and marketing to collect even more valuable data, and embrace the latest AI tools hitting the market. 

If you make inroads in all three, your screening program will improve. It's also important to remember that essential business processes, such as continuous improvement, should also be embraced. 

Free 7-day Trial with

If you'd like to start testing how AI can help your organization reduce false positives,'s industry-leading name-matching technology uses the latest AI and Natural Language Processing (NLP) research to solve challenges such as semantic and phonetic similarity, honorifics, and transliteration.

We offer a free 7-day trial (no credit card is required). 

You can get to know's service even with basic technology skills - for example, by simply uploading a .csv file with your client and business partner data. We would also be delighted to walk you through our service and answer all your queries regarding the API, integrations, and more. Book a free Discovery Call now is a highly reliable and cost-effective solution for sanction checking. AI-powered and with an enterprise-grade API with 99.99% uptime are just some reasons why customers globally trust us with their sanctions screening needs. 

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Paul Dixon
Paul is a RegTech content writer & strategist with extensive experience in digital marketing and journalism. His work has appeared in the Guardian newspaper. He also holds a degree in International Relations, where he studied global sanctions compliance and cross-border finance.‍
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