Using Natural Language Processing to Interpret Risk-Relevant Content

Every day, hundreds of thousands of news stories are published online. At the same time, governments and agencies constantly update and refine their watchlists and databases. It’s an unimaginably huge volume of data. And, it’s also unstructured – meaning that traditional database technology can’t easily query it. Yet, hidden within this vast resource are the vital insights that can protect businesses from counterparty risk. For example, an adverse media mention or a new listing on a sanctions database. 

In this paper, we explore natural language processing (NLP), a fast-evolving technology that’s ideally suited to making sense of unstructured data at a time when corporations depend more than ever on the insights it can provide. Some of the latest developments in RDC’s research include: 

  • Emerging screening technologies known as Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) 
  • More efficient adverse media screening using Reinforcement Learning, a form of artificial intelligence that can help identify items of interest in news articles
  • Leading-edge algorithms purposely developed for KYC/AML screening 

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