Adverse media checks, also known as negative news checks, is the process of screening a financial institution’s client (individual or corporate) against news articles, legal prosecution or similar content that may affect the customer’s final risk by revealing their involvement in money laundering, terrorism, fraud, tax evasion, or other types of crimes.
The result of the analysis can lead to the non-acceptance of the customer, the increase of a client’s risk, the filing of an SAR, or the termination of the relationship with the client.
What is the legislation behind adverse media?
In the international sphere, the Financial Action Task Force (FATF) in its Risk-Based Approach Guidance identifies adverse media searches as a part of Enhanced Due Diligence (EDD) practice concerning individual customer risk assessment.The Wolfsberg Group puts forth a similar concept in its Correspondent Banking Due Diligence Questionnaires, making negative news screening a part of the risk-based approach for sectors or groups that pose a higher risk to financial institutions such as correspondent banking and politically exposed persons (PEPs).
In the United States, FinCEN’s final rule commentary of its Customer Due Diligence Requirements for Financial Institutions states that financial institutions should develop risk-based procedures to determine if additional screening, particularly “negative media search programs,” would be appropriate.
What sources are used for negative news screening?
Negative news can be derived for a variety of official news sources or unstructured data sources such as social media, internet forums, or databases.However, it’s always important to check the quality, credibility, and the independence of the source you are using so as to not incur the risk of utilizing biased, partial, or fake news.
News articles not only provide elements on a single individual or company but may also present the names of direct or indirect connections towards other individuals or businesses that may pose a threat to the financial institution.
The sources used for adverse media screening can be numerous and depend on industry objectives. The sources vary from primary search engines to other official records, social media, and any additional information that can provide accurate information to serve the investigation's objective.
How to manually search for negative news?
The most common way to manually search for negative news related to a client is to input the person’s or the company’s name (or acronym) in a search engine. Various search engines, such as Google, offer a feature where one can explore solely news articles. Once, and if, a news story is found, the compliance analysts will review it and cross check it with the client’s personal information to determine if the article is a true hit with potential impact or a false positive.How can automated tools help detect adverse media?
Software that is tailored to the financial institution’s needs can automate the negative news screening process, thus reducing the time needed to search for media content and increasing the accuracy rates of true hits.Various risks associated to clients are often perceived during the KYC/CDD process, both the onboarding and review phases, thanks to automated tools.
Hiring a consultant to help set-up your financial institution’s negative news screening tool releases you of the burden of having to do it yourself and provides years of expertise in the domain of reducing false positives to a minimum.
What is the future of adverse media screening tools?
Software developers have begun to incorporate artificial intelligence, machine-learning, and natural language processing in their adverse media screening tools for increased performance and unrivalled precision.Machine-learning, a set of computer algorithms that are capable of improving upon experience, can now scan articles at incredible rates and with a depth and accuracy that is unparalleled compared to other standard automated tools.
The new tools also don’t just skim the surface of media sources but dig deep into the open web, deep web, and all kinds of structured and unstructured content. Natural language processing (NLP), a subfield of artificial intelligence which can analyse large amounts of natural language data, is capable of capturing news in other languages that may be impossible if done both manually and with traditional automated tools.
How can financial institutions improve their screening of negative news?
There are a number of actions financial institutions can take to improve their screening of adverse media. Below are a few tips:-
Negative news screening policy – drafting a well-defined policy can increase the effectiveness of the screening process and decrease time-wasting. It should identify who should be screened, by whom, and how often; it should coordinate the various lines of business, define the procedure for the escalation of true hits, and the actions to be taken after the analysis of a true hit (for example, writing an SAR or terminating the relationship with the client).
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Automated tools – financial institutions should invest in an automated tool as it saves time, is more precise than manual searches, and generates reports automatically.
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Boolean internet search – if for the moment a financial institution cannot invest in an automated tool it doesn’t mean it shouldn’t screen its clients for negative news. A Boolean internet search allows to combine an individual or a company’s name with keywords related to negative news using AND or OR to create a string. The keywords may include “money laundering” or “fraud.” It is a type of manual search. However, one must be aware that most search engines put a limitation on the length of the search, meaning that variations of the keywords may be excluded from the string (for example fraud and fraudulent).
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Customer data quality – if your financial institution is investing in an automated tool, make sure your customer data is up to date and complete. The software will utilize that information when screening against external negative news databases. Enriching and increasing the quality of customer data will allow the tool to improve the quality of matching results.
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This article is full of informative. I can relate very well. Because I'm part of same profile. With one of blue chip company. For eg: Adverse Media Screening, Sanctions Screening, Financial Crimes etc.