Public Signals of Python‑Enabled AI in Finance: Disclosure Patterns and Outcome Claims in NYSE Institutions

Abstract Artificial intelligence (AI) is diffusing rapidly across financial services, but public disclosure of AI and software capability remains sparse and heterogeneous, even as Python has become the dominant language for analytics and model deployment. This research constructs a systematic map of Python-framed AI disclosure for a strict frame of 180 New York Stock Exchange (NYSE) financial institutions. A triangulated corpus of regulatory filings, employer portals, corporate communications, and sectoral press is assembled through January 2026, and each firm is coded into one of three disclosure states: explicit Python, indirect AI, or none. Named Python libraries are detected via sentence-level dictionary matching and contextual filters, then mapped to seven analytical dimensions (natural language processing, machine learning, deep learning, reinforcement learning, probabilistic modeling, optimization, and visualization). An Outcome Claims Index (OCI) flags quantified performance assertions (e.g., risk reduction, accuracy gains) and supports both frequentist and Bayesian inference when such claims are rarely observed. The results show that 76.7% of firms disclose explicit Python usage, 2.2% disclose only indirect AI references, and 21.1% disclose neither, with a visibility-weighted explicit share of 0.629. Disclosure patterns vary strongly by subsector, and a common backbone of pandas, NumPy, and scikit-learn coexists with toolkits tailored to text-intensive, tabular risk, payments, and market microstructure tasks. OCI values are effectively zero across subsectors, indicating that quantified outcome claims are rarely placed in the public record. The study delivers a subsector-resolved empirical catalog of Python adoption in finance and a replication-ready pipeline for measuring tool-level AI disclosure.