AI in Financial Services: Ubiquitous Code, Invisible Outcomes

Nearly 77% of major NYSE financial institutions have integrated explicit Python-based analytical tools into their operations, yet the vast majority offer no quantification of the results in the public record. Historically, mapping the technological sophistication of the banking sector was an exercise in frustration, relying on opaque expenditure reports or vague press releases that obscured the engineering reality.

The study, which analyses a triangulated corpus of regulatory filings and corporate communications through January 2026, offers a sobering look at the state of AI in financial services. While the adoption of the Python ecosystem—specifically the 'backbone' of pandas, NumPy, and scikit-learn—is widespread, the Outcome Claims Index (OCI) is effectively zero. This metric, designed to flag asserted performance gains such as risk reduction or accuracy improvements, indicates that while the tools are present, public evidence of their efficacy is entirely missing.

Measuring AI in Financial Services: Libraries vs Keywords

The methodological shift presented in this research is substantial. Traditional analysis of corporate technology is often low-resolution, scanning for broad buzzwords and high-level strategy statements that signal density but lack specificity. Previous attempts to gauge adoption relied on the aggregate volume of terms like 'automation' or 'digital strategy', which provide a gross estimate of complexity but little functional detail. This study’s approach is far more precise. It targets specific named Python libraries within a strict frame of 180 NYSE firms. By parsing for functional units such as TensorFlow for deep learning or cvxpy for optimisation, the researchers bypass the marketing veneer. This granular detection separates the noise of corporate communications from the signal of actual technical deployment, proving that a firm is not just discussing innovation but has installed the necessary machinery.

Despite this high-fidelity detection of tools, the silence on outcomes is deafening. The data shows that 76.7% of firms are 'Explicit Python' users, yet the industry remains reticent about the returns on this investment. The study measured the presence of code libraries; it reveals that quantified outcome claims are rarely placed in the public record. The discrepancy between the heavy investment in AI infrastructure and the absence of performance data raises difficult questions for investors seeking to value these technological assets, as the specific utility of these deployments remains obscure.