You are working on a system that needs to turn financial reports into structured data that downstream analytics and research tools can use. The reports contain dense prose, tables, footnotes, accounting terms, issuer names, dates, monetary values, and references to line items that may appear in different formats across documents. You need an NLP approach that can identify the important fields, normalize them, and preserve enough context for later validation.
How would you parse financial reports using natural language processing?
Financial named entity recognitionStructured extraction from narrative text and semi-structured sectionsFinance-aware tokenization and normalizationLinking extracted spans into usable records