Tolerance for Opacity: A Threshold Framework for AI-Driven Banking

Abstract—The increasing deployment of artificial intelligence in banking raises a structural tension between algorithmic opacity and the institutional obligation to justify decisions under supervisory, judicial, and systemic scrutiny. While technical complexity may enhance predictive performance, it can simultaneously weaken the capacity of financial institutions to reconstruct and defend decision-making pathways when formally challenged. This article introduces the concept of Tolerance for Opacity (TfO) as a threshold framework for assessing the level of opacity compatible with stable institutional reconstructibility. Reconstructibility is understood as the institutional capacity to ex post rearticulate the causal, normative, and evidentiary chain underlying an AI-assisted decision in a form that remains defensible under conditions of formal scrutiny. The paper argues that opacity does not become problematic merely because it limits interpretability; it becomes prudentially relevant when it approaches a level at which reconstructibility may degrade abruptly. The analysis develops the threshold problem conceptually rather than quantitatively. It proposes a structured governance map to visualize the interaction between aggregate opacity and institutional capacity, identifying zones of stability and instability within AI-driven banking environments. The objective is not to eliminate opacity, but to bound its institutional consequences. Situated in the context of the forthcoming implementation of the EU Artificial Intelligence Act and its interaction with existing financial supervisory obligations, the framework seeks to clarify how prudential architecture must evolve to preserve accountability under increasing technological complexity.