Culture, geography, politics, and media reporting all create challenges for name matching in a KYC or due diligence process, even when software is used to perform the matches. In this article, we show how intelligent screening processes harness entity data and machine learning to solve challenges around finding true alerts.
A fundamental theme underpins know your customer (KYC) risk screening: names.
A person's name is their key identifier and in screening processes names are of course vital, but names are very rarely unique – unlike the people who claim them. In the US, for example, topping the chart for most popular first and last name combination is James Smith. And more than 70 million people share the surname Devi in India the most populous country in the world.
In relation to adverse media screening, which is used to find risk associated with negative news stories pertaining to an individual, the driving force behind generating accurate alerts is matching based on a name. And as this process can generate many (many) results, Moody's KYC solutions consider several identifying components, including things like an individual’s location and date of birth, to refine results.
Names can be complicated. Name matching is especially difficult in a global dataset of names captured by sources including government watchlists and adverse media from different countries — including names in more than 70 languages and numerous scripts. The complications grow when the names include those of risk-relevant persons and organizations, some of whom may be deliberately trying to avoid detection.
To manage this process, Moody’s has established an intelligent screening solution supported by a team of cross-functional screening specialists. The team combines qualitative anthro-linguistic expertise with access to tailored quantitative data science algorithms and machine learning techniques to overcome name-matching challenges and deliver accurate results.
Accommodating the complexities of our individual identities requires several considerations.
Naming conventions
Some cultures maintain significant or symbolic names for generations, leading to names that are common to vast numbers of people. For example, middle names in Vietnamese cultures tend to correspond with birth order, offering less identifying information than an additional name component generally would. Such customs raise questions around how best to capitalize on distinct elements of names in name-matching software design to avoid overwhelming screening results with false positives.
Moody’s has three main avenues for identity to dismiss ambiguity among common names:
(Un)popular names and name matches
Names are subject to trend. Certain names might become popular due to famous figures, waves of migration, or baby booms. Just as easily, names can significantly decrease in popularity. The notorious ‘bad guys’ of today might deter the popularity of names in the future. It only takes one bad reputation to cause trouble for their namesakes, which may be why some jurisdictions outlaw some names.
Name recording
Recording a name from one medium to another, from paper to computer for instance, can easily introduce errors — especially when the name is transliterated. Who audits how an entity name went from point A to point B? Audio-to-text transcription adds many problems but relying on a signature to spell a name can also be problematic.
Luckily, with modern word processing and AI technology, we don’t often have to rely on handwriting to spell a name. However, the adoption of digital records, literacy rates, and access to computers remain uneven between and even within countries. Name recording may rely on optical character recognition (OCR), which gives near-accurate, yet sometimes imperfect interpretations of written documents and letters. Even where digitization is widespread, software can introduce errors — for example, automatically ‘correcting’ names - ‘Kara’ to ‘Lara’ or ‘Park’ to ‘Mark’ or 'Teh' to 'The'.
Identifying a misspelling across languages and scripts makes disambiguation less intuitive than it would appear to be. The range of keyboard templates adds to unpredictability. Languages that use the same alphabet may use unique keyboard layouts, like French and English, whereas logographic scripts might be subject to the ‘Wubi Effect’, where several competing keyboard configurations are in common use among a community with a single common language.
Our Review software incorporates targeted intelligence on appropriate name variants to accommodate transliterations, as well as misspelling frequency data to interpret possible typographic errors and prioritize relevant alerts.
Geopolitical impacts
Languages, scripts, writing standards, and cultural pressures differ between regions and change over time. Political power plays a surprising role in how names are written, recorded, and transliterated. For example, imposing the Russian language and Cyrillic alphabet in the Soviet republics, as a tool for social cohesion.
In other cases, it’s about cultural revitalization. The once near-extinct Gaelic language was revived after Ireland became independent in 1922. As recently as 2005, a law came into force that mandates Gaelic versions of place names in parts of the country’s west on road signs and official maps.
Issues of identity
What makes name matching even more complicated is when the names of one person don’t match — and by all linguistic means should not match. Some people may not want to be identified, but name changes also have other justifications. Legal name changes are more common in some places than others and are often subject to regulation. In 2005, South Korea’s supreme court allowed name changes under certain conditions, such as if the name resembles an offensive word or is the name of a famous criminal. Under this law, a person cannot change their name if they have a criminal record or history of bankruptcy. Elsewhere, an individual might go by a different name due to cultural pressure or a desire to assimilate, or perhaps to make it easier for others to pronounce their name.
Moody's Grid profiles capture all available aliases as recorded in adverse media coverage to ensure identification of risk entities. Further research is included with our high-risk premium content sets, such as politically exposed persons (PEPs) + family or close associates. Review screening also includes critical watchlist logic and optional OFAC-search style algorithms to detect high-risk sanctioned entities.
Going from many names to a few and then arriving at a final alert can be done with intelligent entity screening processes in the following five steps:
This process is automated, leverages our screening data base with more than 21 million profiles, harnesses machine learning, and keeps humans in the loop for decision-making. We have designed the process with the aim of reducing the number of false positives generated during screening and ensuring the alerts raised for further review are those most likely to be true matches.
Moody’s intelligent screening around name matching is designed to make compliance and risk management easier for clients. Our automated, AI-enabled KYC solutions help organizations make better decisions faster and reduce barriers to working with counterparties worldwide.
Our experts analyze data, identify reporting trends in name variants across regions and changes over time. And those insights continue to guide the development of our solutions. The name-matching teams constantly innovate and develop software and expertise to overcome the complex, cross-cultural realities customers face each day.
We rely on software intelligence and development, robust governance processes and support, and anthro-linguistic expertise and insights for a customizable and consultative approach to risk-screening best practices.
Get in touch to find out more about our intelligent screening solutions and name matching capabilities - we would love to hear from you.