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What is Adverse Media Screening and Why It Matters to Financial Institutions

By Greg Pinn

What is adverse media screening?

Adverse media screening (AMS), also known as adverse media monitoring, is the process of querying global, reputable news sources for relevant information on clients and prospects who may pose an increased risk to financial institution (FIs). Conducted at client onboarding and periodically thereafter, adverse media screening has become a vital part of financial sector customer due diligence (CDD).

In an ongoing effort to halt money laundering and other financial crimes, many regulatory bodies around the world increasingly recommend AMS. The Financial Action Task Force, a global anti-money laundering watchdog group, includes AMS in its international CDD standards.1 The European Union has required AMS for certain high-risk situations since 20172: The United States Department of the Treasury’s Financial Crimes Enforcement Network (FinCEN) requires “appropriate risk-based procedures” for FI CDD.4 For many FIs, this means implementing AMS systems.

Since adverse media screening processes find persons and organizations that have committed money laundering and related offenses, those FIs that do not implement AMS risk regulatory penalties. Even worse, they risk reputational damage and business losses —including loss of investor confidence and plummeting stock prices — if they are viewed as complicit in criminal activities.

Existing adverse media screening systems fall short

Currently, most adverse media screening efforts are conducted through either:

  1. Human-curated databases. These databases list persons suspected or known to have committed money laundering offenses as defined by the Financial Action Task Force. (These predicate offenses include money laundering, terrorist financing, human trafficking, securities fraud, bribery, and other crimes). Available through third-party vendors, these databases are the result of teams of researchers reading news articles and creating or enhancing criminal or suspect profiles based on that information. The quality of these solutions is limited to the vendor’s research capabilities. They may be slow, with significant lag times between when new information is published and when it appears in the database. These databases are also limited in linguistic scope, typically only producing English-language profiles.
  2. In-house screening. In-house AMS often employs search platforms to examine global news feeds in an attempt to find negative mentions of clients or prospects. FIs enter a list of entities to be queried, along with a list of offense-related keywords, into a search platform. The platform then scours publicly and commercially available information sites — potentially including web pages, government databases, news sites, and open-source intelligence. When the system uncovers potentially negative information about a client or prospect, it alerts investigators.

While less expensive and better automated than third-party solutions, in-house AMS systems typically miss too many matches (especially when they rely on commercially available news aggregators) and return too many false positives — increasing the investigative burden. Nor do they group or deduplicate articles. This deficiency requires investigators to wade through redundant information.

A better way

Enhancing in-house AMS systems with name matching and entity resolution capabilities makes adverse media screening more efficient, accurate, and cost effective.

  • Name matching technologies examine names recorded in a host of variations (Rebecca Hawks, Rebecca Ann Hawks, Becca A. Hawks, Becky Hawks) in hundreds of languages and dozens of scripts. These technologies then apply identifiers such as age, residence, employer, and job title to connect the right person to the search subject.
  • Entity resolution capabilities find records in different data sets that refer to the same entity, then link that information to real-world people, places, and organizations — taking into account the context in which an entity is mentioned.

How do these capabilities play out in the real world? Consider the following scenario.

A woman named Rebecca Hawks, a grocery store manager in Cleveland, Ohio, is applying for a mortgage at your bank.

There are a lot of Rebecca Hawks in the world. Name matching and entity resolution technologies working with your screening system find this name and its variations across a plethora of documents appearing in publicly and commercially available databases, written in an array of languages. Name matching and entity resolution capabilities automatically narrow the field, limiting the number of documents presented to investigators. They automatically reject mentions of Rebecca Hawks, a software engineer living in London. They reject obituaries written about Rebecca Hawks. They reject news stories about Rebecca Hawks winning a leadership award at her 8th grade graduation in 2022. Understanding context, they reject mentions of a Rebecca Hawks and “arrest” if the word “arrest” is preceded by the word “cardiac.”

Your enhanced AMS system flags reports of a retail worker named Rebecca Hawks from Cincinnati who was convicted of drug trafficking in 2021. This crime received significant coverage. Your system groups and deduplicates all the news stories discussing Rebecca Hawks’ crime, arrest, and trial. Upon further reading, your investigators learn that the drug-dealing Rebecca Hawks graduated from high school in 1979. The Rebecca Hawks applying for a mortgage was born in 1990. She gets her loan.

Adverse media screening is a best practice for customer due diligence in the financial sector. Name matching and entity resolution capabilities improve AMS, helping you more quickly and accurately spot negative mentions of clients and prospects while dramatically reducing investigators’ workload.


  1. Financial Action Task Force, “The FATF Recommendations,” February, 2023
  2. Directive (EU) 2015/849 of The European Parliament and of the Council of The European Union. May 20, 2015.
  3. Finance Crimes Enforcement Network, “Joint Statement on the Risk-Based Approach to Assessing Customer Relationships and Conducting Customer Due Diligence,” July 6, 2022,
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