SYSTEMS AND METHODS FOR RELEVANCE-BASED DOCUMENT ANALYSIS AND FILTERING

Aspects of the present disclosure provide systems, methods, apparatus, and computer-readable storage media that support relevance-based analysis and filtering of documents and media for one or more enterprises. Aspects disclosed herein leverage custom-built taxonomies, natural language processing (NLP), and machine learning (ML) for identifying and extracting features from highly-relevant documents. The extracted features are vectorized and then filtered based on entities (e.g., enterprises, organizations, individuals, etc.) and compliance-based risks (e.g., illegal or non-compliant activities) that are highly relevant to a particular client. The filtered feature vectors are used to identify and highlight relevant information in the corresponding documents, enabling decision making to resolve compliance-related risks. The aspects described herein generate fewer false positive or otherwise less relevant results than conventional document screening applications or manual techniques.