The Convergence of Artificial Intelligence and Python in Finance: Empirical Evidence from NYSE Financial Institutions

Artificial intelligence (AI) has become a transformative force in the global financial sector, with New York Stock Exchange (NYSE) institutions leading adoption across banking, asset management, insurance, payments, and market infrastructure. This study investigates how AI tools are being deployed across financial subsectors and evaluates the role of Python as the enabling technology. A triangulated data collection strategy was employed, drawing on regulatory filings, job postings, technical press, and corporate news releases to ensure both reliability and breadth of coverage (Patton, 2015; Yin, 2018). The findings reveal both convergence and divergence in adoption patterns. Convergence is evident in the dominance of open-source Python libraries, particularly scikit-learn , TensorFlow , and PyTorch , which underpin nearly all AI applications in finance. Divergence is reflected in sectoral emphases: banks focus on compliance automation and customer engagement, asset managers on portfolio optimization, insurers on fraud detection and underwriting, and payment providers on real-time fraud prevention. Market infrastructure firms extend adoption further into advanced applications such as transformers and retrieval-augmented generation (RAG) for credit analytics and ESG scoring. The research contributes to literature by providing systematic, cross-subsector evidence of AI adoption while highlighting disclosure gaps that necessitate reliance on indirect signals such as job postings. Strategically, the findings show that AI is no longer peripheral but central to financial competitiveness, while regulatorily, they underscore the need for standardized disclosure frameworks. The study also demonstrates the importance of integrating Python into university finance curricula, ensuring that graduates are prepared for a data-driven financial industry. Future research should explore longitudinal patterns of adoption, assess performance impacts, and investigate the implications of generative AI.