How to Reduce False Positives in AML

Increasing number of false positives in AML has been a point of concern for many financial institutions. Such false alarms can lead to time-consuming and costly processes and defeat the purpose of efficiency and effectiveness in the anti-money laundering regulations. In addition to positives, false negatives can also have a drastic effect on the working conditions of a financial institution. These concerns typically get worse as the firm expands. However, with software like Focal by Mozn, you can curb the situation and look towards a better, more efficient future.

Key Highlights

• False positives are a point of concern for many organizations.
• Such false alarms can be time-consuming and costly.
• Companies should not decrease chances of false positives if it will compromise on accuracy.
• When the system detects an unsuspicious activity and marks it as suspicious, it is called a false positive.
• Structuring the data, increasing the data relevancy, reviewing the AML process, and integrating AI and the latest software can curb the problem of false positives.
• In MENA countries, false positives can be linked to the inaccurate name matching capabilities of the software.
• Focal by Mozn was specifically designed for Arab countries- to reduce their risk of missed hits.

What are False Positives?

False positives are when the system detects an unsuspicious activity and reports it as a suspicious one. The alerts sent out thus require a Suspicious Activity Report or SAR, which can be unnecessarily time-consuming and costly. However, these alarms are often linked to actual financial crimes, which could be of interest to law enforcement. So, decreasing the number of false positives is important. However, compromising on the accuracy of the system is highly unacceptable.

So, how does one decrease the number of false positives without compromising on the safety and security of the institute? Let’s find out!

Four Ways to Decrease False Positives

1. Structuring the Data

The data entries in your systems should be structured so that searching for and matching the transaction is easy. As you expand your business and number of clients, it might become more difficult to extract data if it is not properly organized.

Avoiding simple errors, such as inserting the surname in the name title, will also help greatly. False positives can be linked with such errors and make the process more lengthy and complicated.

2. Keep Relevant Data Records

Inquiring the customers about their area of residence and source of income doesn’t cut it anymore. Criminals will try their best to cheat the system and go undetected. You must also acquire relevant data, such as utility bills, credit card statements, and other bank account’s histories.

Performing due diligence and keeping relevant records of the client in your systems will also help prevent false negatives and positives.

3. Review the Screening Process

It is best practice to evaluate the current screening process at the end of each year. Check the efficiency of the current practices and update your process based on the results. Think of different scenarios where criminal activity may go unnoticed and update the rules and policies accordingly.

4. Use of Latest Technologies

Using the latest technologies and software will help you increase the accuracy of your institute’s AML process. In addition, adding elements of machine learning and artificial intelligence technologies will also prove beneficial.

With the latest technologies, your systems will detect and scrutinize false negatives and positives in a more time-efficient manner. In addition to being quick, these integrations reduce the chances of human errors. They will also improve the data structure and make it easier for you to evaluate the alerts.

False Negatives and Positives in MENA

The Arabic language is extremely complex; For example, the letter A has two alternatives in the Arabic language, thus making the translations more complicated. Similarly, the letter H also has two alternatives. Needless to say, many of the false positives and negatives could be a consequence of the language structure.

Translating names from Arabic to English is harder than most languages, and you will not find any Software that can translate Arabic names into English as accurately as Focal by Mozn.

Focal by Mozn

We saw a gap in the market through poor name matching capabilities of AML process softwares around the globe. This is the reason why most MENA countries face more false negatives and positives, as compared to other countries. For this reason, Mozn created software integrated with accurate name-matching capabilities.

If you want to make your sanctions screening process better and reduce the risk of false positives, contact us now. Our software is scalable, easy, and customizable. So, what are you waiting for? Join our growing family now.

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