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The digital era has given KYC analysts unprecedented access to a plethora of information on individuals and companies. Newspaper articles, online blogs, and social media are accessible and filterable with one click and may reveal important news on a financial institution’s customer. However, data overload, incomplete and unconfirmed information, and fake news has made the analysis of adverse media a burden for compliance departments, ushering the age of artificial intelligence and machine-learning for customer screening.
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.
This inspection starts from the beginning of the business relationship and is part of the ongoing customer risk assessment outlined in KYC/CDD regulations that all financial institutions must respect. The objective is to mitigate any potential threats posed by criminals wanting to use the institution’s financial services, to ensure the transparency of every transaction, to safeguard the institution’s reputation, to avoid any legal repercussions and to safeguard financial markets from abuses.
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 Europe, the concept of negative news first appeared in the 4th Anti-Money Laundering Directive, 4AMLD of the 20th of May 2015. It describes the need to screen for adverse media as part of the EDD process and should also be applied when there’s an increase of quantity of information that is needed for Customer Due Diligence (CDD) purposes.
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.
However, manual searches are time-consuming and arduous. The sheer volume of information found on the internet does not allow a precise and thorough research. It can be considered ineffective as media sources may be missed, articles may be in different languages unknown or not considered by the analyst, and certain sources may have limited access. This is difficult to sustain if the financial institution is rapidly growing and adding new clients on a daily basis.
2.5 exabytes of data are estimated to be created daily. That is 1 billion gigabytes or the equivalent of having 15.625.000 new smartphones with 64GB of memory in your hands every day.
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.
Many automated tools offer global, curated content that can give real-time feedback and that is updated regularly. They also allow the generation of reports and statistics concerning the number of true hits for the client(s) screened with just the click of a button. Certain tools provide management features for assessing and rating the information that is provided in the software. Screening can also be set-up to run automatically at a precise period, time, or date allowing more freedom to the analyst.
Various risks associated to clients are often perceived during the KYC/CDD process, both the onboarding and review phases, thanks to automated tools.
Despite the numerous advantages, adverse media screening tools may be costly and difficult to set up as parameters need to be set, thresholds need to be adjusted (too large may result in an overabundance of false hits while too small may exclude important articles), when automated checks should be run, what weight should be given to what keywords, and what sources should be used.
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.
Artificial intelligence is capable of assessing in an article if the name of an individual or a company is implicated for a crime or is simply mentioned in the news source. Just based on the language of the article, it can also deduce the general sentiment that it is trying to express, meaning if the person mentioned in the article is implicated for something negative or positive, and how negative the wording and expression is.
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.
The software of the future, which is already in production, will have singular power in capturing adverse media content concerning individuals and companies, turning manual searches and classic automated tools obsolete, and increasing the performance of compliance departments.
What is «fuzzy matching?»
Fuzzy matching is a feature of many automated negative news screening tools that allows to catch all name variations and misspellings of individuals and companies, for example Elizabeth, Elisabeth, or Elsbeth. It is useful for financial institutions that have clients with names in a different language that may vary depending on the area they are from. One such example would be with Arabic names that often have variations such as the use of “bin” or “ben” preceding their last name.
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:
1
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).
2
Automated tools – financial institutions should invest in an automated tool as it saves time, is more precise than manual searches, and generates reports automatically.
3
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).
4
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.
This article is clear, professional and interesting. It really stimulates me to know more about ways to protect individual reputation even if it is not directly linked to financial analysis . I thank dr. Siggia for this important hint!
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.
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This article is clear, professional and interesting. It really stimulates me to know more about ways to protect individual reputation even if it is not directly linked to financial analysis . I thank dr. Siggia for this important hint!
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.