By Adverse media is explained as any negative information from news sources. It can also be called negative news. One must be very careful when dealing with individuals and companies that have an adverse media profile. Hiring a client with a bad reputation or background is a significant risk for your company.
Adverse media includes traditional news sources, a database of international organizations, blogs, and web articles, social media, and internet forums.
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Adverse Media Screening
An adverse media search is the process of controlling public records (including newspaper archives, databases, and unstructured sources we mentioned earlier) for your potential client’s name and address.
Negative media scanning is scanning negative news about a person or company. This can be part of KYC or AML transactions. Or it can be done on its own.
The purpose of negative media scanning is to be able to identify users’ risks. For example, a customer may have had no problems with the KYC process; this person may not be a PEP. But media incompatibility and a bad company of payment history are also a risk to companies.
Conducting Effective Adverse Media Screening
The first step to running an effective adverse media scanning process is to utilize technology and automated scanning techniques. Manual control is almost impossible in an environment where thousands of media are broadcast every day. Manual control is very time-consuming, especially for a fast-growing business with new customer entry. In addition to providing more accurate results, automated solutions also contribute to fewer false positives in the screening process.
Efficient Adverse Media Results
The results of the screening can be divided into three categories:
- No matching results: The adverse media screening is completed, and the individual under investigation has not appeared in any source of result. If the setup of sources is sound, you can assume the result is accurate.
- True-positive: It was confirmed by analyst evaluation.
- False-positive: It was discounted by analyst evaluation. This may share similar information that gave rise to the “false-identification.” Homonyms, birthdays, nationalities, backgrounds, and insufficient filters might not help the screening tool, which cannot build a clear distinction.