Compliance Screening
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Adverse Media Screening

Introduction

Signs Adverse Media screening uses Contextualized Sentiment Analysis with Machine Learning to replace the traditional keyword approach.

With Signzy's API, users are able to determine the emotional meaning or context of a given article, whether it be positive, negative, or neutral. Our solution can score and predict negative news articles with a high degree of accuracy, and reduce false positives by 50% or more, because we only provide results that are truly negative.

Supposedly, a word “murder” in any paragraph would be marked as adverse media if we use a traditional method of keyword searching. However, the article could be about a plot twist of a Netflix series. That's why we could not rely on traditional keyword searching methods. That's why at Signzy we utilize machine learning, natural language processing and statistics to determine the meaning of an article.

Adverse Media Source Examples :- BBC, The New York Times, Al Jazeera, Washington Post, Gulf News, CNN, The Telegraph, Fox News, The World Bank, Wall Street Journal, Mumbai Mirror, The Asian Age, The Seattle Times, Caribbean New Now, China Daily, The Indian Express, Bangkok Post, Sydney Morning Herald, Chicago Tribune, Newsroom Panama, Winnipeg Sun, and thousands more.

How does the Signzy sentiment analysis works?

Sentiment analysis for the adverse media provides a confidence score for articles based on actual negative sentiment in the article. By doing this, we reduce the number of false positives.

Our confidence band is divided into three bands

  • High – 80% confidence and higher
  • Moderate – 50% to 79%
  • Low – 0% to 49% confidence

How often our are sources updated?

Our Adverse Media solution is updated with new articles from all sources every day and goes back as far as the original source's archives are available on their websites. US media sources typically go back many, many years. Some country’s media sources do not offer archives and in those rare occasions, we have data going back to the date the source was added to our platform.

Sources from where we usually pull data

  1. General news
  2. Crime
  3. Courts
  4. Animal Rights
  5. Human Rights
  6. Corruption
  7. Terrorism
  8. Business
  9. Financial
  10. Politics
  11. Governments
  12. Military
  13. Security

Our global sourcing analyst team is responsible for identifying approving, configuring and maintaining all sources, our solution utilizes. The following sources are disqualified and are not included in our solution's source list.

  1. Blogs
  2. Fake News
  3. Unregistered/ illegally Established Media

When a media source is deemed authorized for our solution, the Global Sourcing Analyst Team only configures the features within the media source that are relevant to our solution. We do not activate the capture of articles from non-essential chapters, like Sports, Entertainment, Obituaries, etc.

Sample Input

To obtain the outcomes of adverse media, it is possible to conduct a comprehensive search across all categories simultaneously or employ the various category filters provided below.

Adverse/Negative Media Categories

Abbreviation

Name

ALLNM

All NM categories

ANRTS

Animal Rights

BUFIN

Business & Financial

CRCRT

Crime & Courts

GNEWS

General News

MISEC

Military & Security

NMCOR

Corruption

POGOV

Politics & Government

Input

JSON


Output

JSON