Vector Semantics at Scale: An AI Pipeline for Financial Text Similarity

The present paper proposes an end-to-end AI system that transforms unstructured corporate filings into vector representations and also computes interpretable similarity signals using tf-idf and cosine metrics in a distributed pipeline. The methodology incorporates effective preprocessing to ensure reliable token normalization, as well as stemmer/lemmatizer selection and scalable retrieval for parsing/clustering (as defined above). This allows an examination of the accounting policy narratives across firms and across time. Through extensive empirical assessment, we quantify how these similarity features relate to firm-level attributes and investor behavior. In doing so, we illustrate how classic NLP can produce actionable structure from financial disclosures at web scale. The design and experiments provide a reproducible framework for AI text analytics for regulated applications, combining information retrieval methods with cloud compute for high-throughput document understanding.