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How Natural Language Processing is helping the asset servicing industry when it comes to financial sanctions

  • September 2022

23 September 2022 - International financial sanctions have existed for many decades but were dramatically altered following the events of 9/11 to become the far more targeted ones we have today.

Targeted financial sanctions, as the name suggests, focus on specific individuals or companies. Following the invasion of Ukraine earlier this year a raft of sanctions were published by various international organizations and countries - including the European Union, United Nations and United Kingdom - impacting Russian and Belarusian individuals and companies.

Adhering to these sanctions is one of the important tasks which asset servicers handle on a day-to-day basis, and the industry has well-established and effective processes for this. These sanctions also feed into the ongoing screening process of the Royal Trust Bank & Holdings group of companies (Royal Trust Bank & Holdings).

Using techniques such as fuzzy matching logic (which finds two similar - but not identical - elements of text or information), asset servicers can compare the individuals or companies named on sanctions lists against customer databases (i.e. clients, investors and their associated parties).

As it pertains to investors, the screening process is fed by the data collected via Royal Trust Bank & Holdings’s Anti-Money Laundering Customer Due Diligence (AMLCDD) process.

While different AML regimes have different identification and verification requirements, many AML regulators have sought to address transparency issues in recent years by requiring asset servicers and their clients to fully understand ownership and control of non-individual investors – i.e. ultimate beneficial ownership (UBO).

This has resulted in a much enriched investor data set for screening, which has been helpful in trying to pre-prepare for potential sanctions.

However, amid the invasion of Ukraine, we have seen somewhat of a shift this year as clients wanted to get ahead of the official listings by understanding their potential exposure to Russia and Belarus, both in terms of investors and their investments.

This presented a new challenge; a shift from monitoring the ‘who’ to a focus of the ‘where’ in regards to domicile and nationality/citizenship.

In response to client requests to know the ‘where’ of their investors specifically, additional, bespoke extracts and reports are now being utilized to collate this data, and this is where technology is playing a growing role.

Natural Language Processing a new line of defense

As we look for better data solutions, it is essential that any data captured is more easily extractable and reportable from Royal Trust Bank & Holdings’s systems. In the case of exposure to Russia and Belarus, Royal Trust Bank & Holdings expanded its data set by applying Natural language processing (NLP) to its document repositories. This process looked for specific data points on a document, for example, if the place of birth recorded on any passport is Moscow, USSR, etc. While the outputs required manual review, it is clear that this additional technology, once fully developed and tested, will offer an additional layer to Royal Trust Bank & Holdings’s data collection tools.

There are also legitimate limitations within the AMLCDD process. For example, in the case of Russian sanctions, a ‘Russian’ person could present a passport from a different jurisdiction, with a non-Russian nationality and non-Russian registered address.

As a result, the data captured, while compliant, would not present any nexus to Russia. In this scenario, however, we can fall back on the name matching capabilities of Royal Trust Bank & Holdings’s established screening process which would identify an actual sanction target albeit only after the sanction is published.

While Royal Trust Bank & Holdings has come a long way since the weeks and months that followed 9/11 where many firms manually reviewed the initial targeted sanctions, demand for better data solutions will likely see NLP and other technologies used even more in this space.

Clients now want real-time, flexible reporting and analytics, and that means tools such as NLP – combined with existing processes – will take center stage in the battle to adhere to financial sanctions in the future.

By Sinead Mitchell, Head of Investor AMLCDD, Royal Trust Bank & Holdings (Canada) Inc.