Modeling Structural Deviation in 10-K Risk Factors: A Semantic Anomaly Detection and Explainable AI Approach

This study examines structural changes in regulatory risk disclosure using a semantic modeling framework that integrates sentence embeddings, multivariate anomaly detection, and explainable artificial intelligence. Prior research typically relies on dictionary-based word frequencies, tone indicators, or topic proportions to quantify risk disclosure. While these measures capture disclosure intensity, they do not directly assess whether the internal semantic organization of risk narratives has shifted relative to historical patterns. We propose a structural semantic deviation framework that represents each company-year disclosure using thematic shares and embedding-based dispersion statistics, and evaluates deviations from a historical baseline through unsupervised anomaly detection. Using Item 1A Risk Factors from Wells Fargo and JPMorgan Chase surrounding the 2016 regulatory shock, we demonstrate that traditional lexical metrics fail to isolate structural breaks, whereas embedding-based semantic trajectories reveal substantial narrative reconfiguration. Isolation-based modeling provides stable and discriminative anomaly scores, and SHAP decomposition identifies semantic distance, litigation emphasis, and disclosure contraction as key drivers of deviation in 2025 out-of-sample disclosures. The results suggest that structural semantic modeling captures risk narrative transformation beyond word accumulation, offering an interpretable and scalable framework for regulatory risk assessment.