The term KYC (Know Your Customer) refers to the checks carried out by banks and other organizations subject to AML (Anti Money Laundering) regulations to protect them from financial crimes or other nefarious activities. These checks are performed at the beginning of a customer relationship to identify and verify who they are and assess potential risks.
KYC is part of the so-called Customer Due Diligence (CDD). It is a complex process, which starts with collecting and documenting various details about the customer and identifying the ultimate beneficial owner (UBO or just BO). As part of this process, after the UBO is identified – whether a person of a legal entity – it is assessed for exposure to negative news such as corruption, human trafficking, drug trafficking, convictions, etc.
The Goal
Multinational investment banks and other institutions in the Financial Services sector need a smart KYC process automating the workflow of identifying relevant negative news items that match UBOs of interest.
The existing workflow requires bank analysts to review daily hundreds of news articles provided by a third party database in order to confirm if a piece of news is a match for their UBO and, if so, to present evidence on the match. As a result, the banks’ adverse media analysis process is both time and effort consuming and not very efficient.
The Challenge
Experience shows that the bigger part of the news provided by a third party database is about an incorrect UBO (lack of disambiguation) or about the right UBO, but the negatively mentioned parties are other players in the article (false positives).
Therefore, banks need an automated solution that would be able to:
- filter out references about irrelevant entities with the same or similar names;
- ignore news where the UBO of interest is mentioned, but the reported adverse event is about another person or organization;
- provide ranking of the filtered relevant news with highlighted sentences about the relation between the UBO of interest and a negative event.
Our Solution: A Knowledge Graph-Powered Adverse Media Analysis
The solution that Ontotext can provide extends the automation and optimization across any adverse media analysis bank operations.
Knowledge Graph-based: to trace down UBOs of interest, Ontotext leverages rich knowledge graphs built from the commercial database used by a bank and enriched with public resources such as OpenCorporates and CrunchBase.
Categorization of reputational risk: the solution can classify the identified sentences within each news that contain relations between the UBO and a negative event against a bank’s taxonomy with several categories of reputational risk.
End-to-end automated workflow: the solution covers the whole process: from loading content exports from the news dumps, through filtering out irrelevant news and ranking the rest by relevance and confidence score, to highlighting specific line items with relevant content for a quicker review.
Why Choose Ontotext?
Ontotext’s smart KYC Adverse Media Monitoring solution can ensure that banks comply with the regulatory requirements and are protected from reputational risk.
With Ontotext’s technology, banks could also:
- cut the time for UBO analysis in the news from several days to less than an hour;
- boost the quality and efficiency of the checks by reviewing only representative samples;
- participate in further fine-tuning the process by providing initial feedback.
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