Anti-money laundering (AML) compliance solutions rely on matching technology to compare records of individuals and entities against AML databases to identify true hits. In the Middle East and North Africa (MENA) region, where evolving regulations and complex cross-border transactions are prevalent, selecting the right matching technology is crucial.
While various AML technology companies offer different matching technologies to address these challenges, their effectiveness, transparency, and coverage can vary significantly. Understanding which technology best fits your business needs in the MENA context is essential for maintaining robust compliance and managing risks effectively.
Considering regulators’ needs is the first step to AML program effectiveness
Regulators require money laundering reporting officers (MLROs) and chief compliance officers (CCOs) to understand and explain how their matching technology works. Whatever matching technology you choose, you will need to demonstrate its effectiveness in tackling financial crime for your business.
With regulations in mind, you’ll begin to understand the key elements to consider when evaluating an AML tech provider’s matching technology. These include the solution’s effectiveness at identifying risk, the insights it provides into screening results, and its ability to deal with customers from different jurisdictions.
Identifying and mitigating risk starts with data quality
In the context of AML screening, your matching technology needs to effectively identify customers appearing in AML databases without generating too many false positives. If it can’t, the result is likely to be more risks and related costs for your organization. While configuration is important, your first priority is to understand how the matching technology copes with common data quality anomalies.
Inconsistent Name Spellings.
Transliteration Variations: Arabic names transliterated into Latin characters, often have multiple spellings for the same name. For example, the name محمد can be spelled as "Mohamed," "Mohammad," "Muhammed," etc.
Dialectical Differences: Names can be spelled differently depending on the dialect or region (e.g., Gulf vs. Levantine vs. Moroccan; Brahim vs Ibrahim).
Diacritics (Arabic harakat that act like short vowels)
Inconsistent or missing Arabic Diacritics or letters (أحمد <> إحمد <> احمد)
Usually these are omitted, and computer systems can often treat them as different characters, which can lead to ambiguity in name matching.
Name Order and Structure
Arabic names can have multiple components, such as tribal name (لقب) in addition to last name. Accounting the cultural aspect of names is needed.
Order Names where the first and last name are reversed or the first name itself consists of 2 words compounded. (Aliridha Saeed <> علي رضا سعيد)
Prefix Issues
Common Prefixes: Names often contain prefixes like "Al-" (الـ), "Ibn" (ابن, meaning "son of"), and "Bin" (بن, meaning "son of"), which may be inconsistently handled as part of the first name, last name, or discarded completely.
The inconsistent usage of these prefixes between client and compliance data may lead to mismatched results.
Use of Honorifics and Titles
Titles in Names: Honorifics like "Sheikh" (شيخ), "Sayyid" (سيد), and "Haj" (حاج) can be part of the name but might be treated inconsistently across different datasets.
Incorrect Interpretation: These titles are sometimes confused for first or last names.
Special Characters
Encoding Issues: Arabic client data might have special characters imported from Persian/Urdu names (which have additional Unicode characters) that might not be correctly supported by certain systems, leading to garbled names due to improper encoding. For example, (البرندكي <> برندگی)
Non-standard Characters: Some systems don’t properly handle the Arabic script’s special letters (like ء, which is used in many Arabic names) and might omit or misinterpret them.
Data quality is a big challenge for AML compliance and is often neglected by matching technology. Poor data quality is the single biggest contributor to missing true hits. Hidden names in joint accounts and addresses, name variations, name order and spelling mistakes, and date of birth variations can all impair the effectiveness of matching technology. For example, Apple was fined for not identifying entities on the OFAC list due to spelling variations and the presence of organizational identifiers in entity records.
Tailored matching optimizes the AML process
You should be able to tailor your matching configuration to the record type, whether it’s an individual, an entity or a transaction, and the source application to optimize your screening performances and meet your regulatory obligations. When fine-tuning your configuration, adjusting parameters will give you the best chance of identifying risk.
An AML compliance solution’s matching technology should provide:
Flexibility in selecting primary (name, address) or secondary (date of birth, passport number, ID number) matching criteria is essential in the MENA region, where complex linguistic variations, such as Arabic script, transliterations, and diverse naming conventions, can complicate AML screening
The ability to set fuzziness at the individual field level
Applying different rules across risk levels, jurisdictions, and data sets
Having the ability to adjust matching configurations yourself and simulating their impact on the number of alerts enables you to optimize your matching performances before pushing new configurations to your live environment
Understanding and explaining matching results can help optimize reviews
Regulators are encouraging CCOs to educate themselves on how their matching technology works and help their organizations fight financial crime. But understanding the underlying steps algorithms take to generate alerts is only half the equation. You also need to be able to interpret the matching results and use that insight to make data-driven decisions.
Essentially, you want to avoid the ‘black-box’ approach that lacks explainability. Regulators want to know why you configured the system the way you have, what you expected to achieve by doing so, and how you arrived at results.
The insight your matching algorithm provides is not just useful for regulators; it is an invaluable resource for your compliance team to understand alerts and review them faster. It will also make it much simpler for your internal audit team to review matches and better manage the volume and types of alerts you are getting.
Culturally-sensitive matching helps lower false positives
For example, names with Surname followed by Laqab or Tribal Origin:
Matching “Khalid Abdallah Salim Al’Umari Al-Qassem Al-Hijazi" to:
Al-Hijazi Khalid Abdallah Salim
Al-Hijazi Khalid Abdallah Salim Al’Umari
Al-Hijazi Khalid Abdallah Salim Al’Umari Al-Qassem
Organizations can have common words that may need to be ignored or taken as a whole:
Ignore Organization Words like "شركة" (company), مؤسسة (organization)
Consider compound tokens, such as: "Islamic Bank of", "Commercial Bank of", “للمقاولات والتجارة العامة” (meaning "for contracting and general trading")
If you have a multi-cultural customer base or a global business, your matching algorithm should consider language and cultural variations. Alternatively, if your business growth aspiration involves expanding into new markets, you’ll want to make sure you can comply with regulations in new jurisdictions.
Naming conventions vary across cultures, creating complexity for matching. For example, in Arabic Gulf countries, individuals often have prefixes like "Al-" (الـ), "Ibn" (ابن, meaning "son of"), and "Bin" (بن, meaning "son of"), which are not always consistent on international data compliance lists. These word tokens need to be screened efficiently to avoid false-negatives and have the optimal results with as minimum false-positives as possible.
How is the matching technology taking this into account to remain efficient?
Some organizations opt to use local solution providers to adapt to cultural specificities. This means compliance teams end up with multiple solutions and a fractured view of their global risk. There is more value in using a solution with advanced matching technology that can handle all cultures in a consolidated platform, including native character screening for both customer and list data. This will not only help you reduce costs but will provide more visibility and understanding of your organizational risk.
Selecting the right partner
Ask your AML compliance solution provider how their matching technology can improve the effectiveness of your screening in native environments and provide understandable and explainable results while supporting the different regions in which your business operates.
Partnering with an AML solution provider that uses a sophisticated matching algorithm to meet your requirements can give you a significant advantage as they will be able to adapt their technology to your needs.
If you would like to speak with someone about our matching technology, feel free to reach out.