The Challenges Ahead for Sanctions Screening Companies

The United States alone oversaw a plethora of sanctions programs that addressed anything from cyberthreats to geopolitical issues in certain areas to drug trafficking. The breadth and complexity of these initiatives present challenges for companies striving to stay compliant. Additionally, after  Russia’s full-scale invasion of Ukraine, there has been a rise in sanctions. While companies have updated their sanction screening tools to cater to client needs, challenges still remain. Between the financial crisis and the close of 2019, global financial institutions (FIs) accumulated $36 billion in penalties due to failures in adhering to anti-money laundering (AML), know-your-customer (KYC), and sanctions rules, as reported by Insider Business. These stats explain how imperative sanctions screening is and how crucial it is to devise ways to overcome the challenges faced by sanctions screening companies. This will allow the companies to streamline their sanctions screening procedures and ultimately hedge themselves from the massive fines.

To reach this objective, it’s important to understand the real challenges first. Let’s see what those hurdles are which the companies face and the solutions proposed by the Financial Action Task Force (FATF).

Understanding the Challenges

Targeting Individuals Over Countries

The recent trend of targeting individuals instead of entire countries has added complexity to tracking cross-border payments for FIs. This requires banks and FIs to meticulously match customer identities with sanction lists. For example, AML measures also involve sanctions against specific high-risk individuals, often referred to as Politically Exposed Persons (PEPs), who hold prominent public positions and are at a higher risk of corruption or illicit financial activity.

Identity Similarities

Additionally, one of the most significant challenges is the similarity in identities. It’s not uncommon for individuals or entities to share names with those on sanction lists. Differentiating between a genuine match and a false positive requires careful verification. To explain this, let’s take this example; multiple individuals with the name “Mohammed Ibrahim” exist worldwide. Suppose a financial institution conducts sanctions screening and identifies a “Mohammed Ibrahim” on its customer list. This name also has the probability to exist in many other government sanction lists due to some other person’s involvement in illicit financial activities.

Thus, the challenge emerges to differentiate between two individuals possessing the same name:

Actual Mohammed Ibrahim – is an individual on the customer list with no record or connection with illicit activities.

False Positive of Mohammed Ibrahim – is a completely different individual whose name might sound similar but this person would be involved in illicit financial activities.

Type of Human Errors in Sanctions

Screening

Typos, spelling mistakes, and other human errors can lead to challenges such as either flagging an innocent party or overlooking a sanctioned entity. To understand how to surpass this challenge, consider an example of a financial institution that is conducting sanctions screening on its customers. In the database, a legitimate candidate exists named “Hassan Ahmed.” However, due to data flaws, this customer’s name is mistakenly recorded as “Hasan Ahmed” without an extra “s” making it challenging to find accurate and exact matches.

This challenge is due to the following reasons:

Actual Hassan Ahmed – is a law-abiding individual but due to a minor error or typo in the database, might appear as a red flag when he is not.

False Positive of Hasan Ahmed – is an individual with a different name and subject to sanctions but appeared against the search query of actual “Hassan Ahmed”

Contemporary Data Collection Measures:

Outdated data collection leads to fragmented and incomplete data, making it difficult to comply with mandated regulations. The lack of structured data also fosters the risk of generating more false positives.

In 2018, a Dutch bank, ING Groep N.V., had poor and insufficient data collection measures which resulted in the Dutch Financial Supervisory Authority (DNB) imposing a penalty worth a fine of €775 million on the bank for violations of AML and sanctions regulations.

Integration of Data Source 

Adding onto that, integrating external data sources can introduce inconsistencies in format, datasets, and geographical coverage, making reliable comparisons difficult. In 2015, Commerzbank AG, a major German bank, faced regulatory challenges and scrutiny.

The case involved the bank’s efforts to enhance its AML and sanctions compliance program by integrating external data sources for enhanced screening. However, integration also poses a challenge when the external data sources are not aligned with the existing system and workflows.

Here are the key insights to help you overcome these challenges:

Key Solutions from Expert Insights

According to FATF, these are the prescribed solutions to help you screen better.

Radar on High-Risk Profiles – While deploying AML measures, strong attention must be given to high-risk profiles such as politically exposed individuals by regularly ensuring ongoing monitoring and correct matches against national, regional, and governmental sanction lists.

Precise Identity Verification-

By using multiple points of analysis, apart from names, one can empower the system to detect accurate matches by comparing the query against parameters including DOB (date of birth), address, EMEI number and much more.

Accuracy of Data – Combat typos, and inconsistencies in data by abiding with FATF which mandates that financial institutions don’t have to undergo repeated screening, but should invest in developing intelligent screening which must be able to detect everything in one go unless there are doubts about the information’s veracity.

Efficient Data Management – Integrate advanced data matching and management systems to ensure that your business remains compliant, updated, and organized through regular audits and timely system upgrades.

Standardized Data Integration – Certain protocols must be deployed to ensure consistency while merging data sources. This may involve harmonizing diverse data sources, to ensure data integrity and consistency across business units

Final Wrap Up

Despite efforts to refine screening tools, challenges like individual-centric sanctions, name similarities, human errors, and outdated data methods persist. Between financial crises and 2019, global institutions faced a whopping fine in penalties for non-compliance.

The FATF has outlined solutions to tackle the challenges which persist amongst the sanctions screening companies. Enter AML Watcher, your premier solution provider. We offer an advanced Anti-Money Laundering system, emphasizing high-risk profiles like PEPs, and harnessing multiple data points for precise identity checks. Furthermore, these data points are updated in real-time. Additionally, our platform ensures data accuracy with automated validation checks and adopts modern data management systems for organized, up-to-date information. With protocols for data integration, AML Watcher stands at the forefront of ensuring compliance and efficiency. Ready for a hassle free sanctions screening process? Contact us today.