Text analytics Solution

Adverse Media Monitoring

For due diligence and risk mitigation, organizations must quickly and accurately identify negative information about their customers, suppliers, and partners. The challenge lies in the overwhelming volume of news and media that must be continuously monitored.

NetOwl Extractor identifies adverse events with high accuracy, speed, and scalability across large collections of text. It not only detects adverse events but also extracts the entities involved, the role they play, and the connections between entities—enabling an organization to meet their compliance and risk management requirements.

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What is Adverse Media Monitoring?

Adverse Media Monitoring is the real-time or near real-time analysis of news and other media to identify negative information related to organizations and people. Negative information may be about organizations – such as bankruptcy, lawsuit, environmental pollution, and product recall. It may be also about an organization’s key members– such as an arrest, indictment, or corruption charges against an executive.

Adverse media monitoring allows organizations to reduce potential risks, whether it is part of anti-money laundering (AML), know your customer (KYC), or general due diligence efforts concerning their customers, suppliers, and partners. Identifying such risks not only helps organizations meet regulatory compliance but also avoids reputational damage.

Challenges of Adverse Media Monitoring

Given the sheer number of news and other media sources available each day, it is impossible for people to read them all and identify adverse information in a timely manner. Of course, an organization can filter that voluminous data down to a smaller collection by using keywords to select only texts that mention names of their customers, suppliers, or partners along with keywords that indicate “adverse” events.

Still there would likely be many texts that are not about your customers, suppliers, or partners as words are ambiguous (e.g., “apple” as a company or fruit; “amazon” as a company or a river). Or the texts might mention them, but they are not the actors in adverse events and just happen to be mentioned in the same text. Additionally, if your search keywords are too narrow, you may miss relevant texts because language offers many different ways to describe adverse events.

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Entity, Relationship, and Event Extraction

The word ambiguity problem can be solved through entity extraction as it identifies and disambiguates names of people, companies, organizations, places, etc. in texts. But how do we know if the key entities of interest are involved in negative events? Here event extraction can help. NetOwl’s event extraction, with over 100 types of events organized in an ontology, extracts not only a wide variety of adverse events but also the participants in these events so that, for instance, a “lawsuit” event would be extracted along with its plaintiff and defendant.

In terms of tracking executives of organizations of interest, NetOwl’s relationship extraction can be used to identify connections, for example, by extracting affiliation links between organizations and their executives. So when a CEO is arrested, NetOwl extracts not only who arrested whom but also can find the connection from the CEO to the company.

Advanced Text Analytics for Adverse Media

NetOwl Extractor is a key part behind the scenes of several adverse media monitoring solutions in the market. Those solutions leverage NetOwl to:

Identify key entities mentioned in an individual text
Recognize key relationships between key entities and their affiliated people
Extract adverse events AND the roles key entities play in those events.

By discovering the connections between adverse events and the key entities that play a role in them, adverse media monitoring solutions can quickly analyze large volumes of news and other text sources and provide their users with the critical information needed for their due diligence and risk mitigation processes.