Deep learning architecture for adverse media screening

Abstract

Systems, methods, and apparatus are provided for deep learning architecture for adverse media screening. A dynamic data stream may be generated from news reports acquired from a variety of media channels in a variety of formats. Reports may be separated by format. A graphing module for each type of media may use geometric clustering algorithms to group reports into a coherence graph. Fact-checking sources may be incorporated into the graph. A graph neural network may determine coherence between news claims and fact-checking sources, between news claims and supporting report details, between similar reports, and/or between video and audio within the report itself. The network may output a legitimacy score for a report. A credible report may be flagged and retrieved in the course of a customer screening. Materiality may be determined for a credible report and may trigger a customer account closure protocol.