As Artificial Intelligence (AI) becomes increasingly embedded in financial decision-making, the opacity of complex models presents significant challenges for professionals and regulators. While the field of Explainable AI (XAI) attempts to bridge this gap, current research often reduces the implementation challenge to a binary trade-off between model accuracy and explainability. This paper argues that such a view is insufficient for the financial domain, where algorithmic choices must navigate a complex sociotechnical web of strict regulatory bounds, budget constraints, and latency requirements. Through semi-structured interviews with twenty finance professionals, ranging from C-suite executives and developers to regulators across multiple regions, this study empirically investigates how practitioners prioritize explainability relative to four competing factors: accuracy, compliance, cost, and speed. Our findings reveal that these priorities are structured not as a simple trade-off, but as a system of distinct prerequisites and constraints. Accuracy and compliance emerge as non-negotiable "hygiene factors": without them, an AI system is viewed as a liability regardless of its transparency. Operational levers (speed and cost) serve as secondary constraints that determine practical feasibility, while ease of understanding functions as a gateway to adoption, shaping whether AI tools are trusted, used, and defensible in practice.

翻译：随着人工智能（AI）日益融入金融决策，复杂模型的不透明性给从业者和监管者带来了重大挑战。尽管可解释人工智能（XAI）领域试图弥合这一差距，但现有研究往往将实施挑战简化为模型准确性与可解释性之间的二元权衡。本文认为，这种观点在金融领域是不充分的，因为算法选择必须在一个由严格监管边界、预算限制和延迟要求构成的复杂社会技术网络中权衡取舍。通过对二十位金融从业者（涵盖多个地区的C级高管、开发人员和监管者）进行半结构化访谈，本研究实证探讨了从业者如何相对于四个竞争因素——准确性、合规性、成本和速度——来优先考虑可解释性。我们的研究结果表明，这些优先级并非简单的权衡关系，而是由不同的先决条件和约束条件构成的系统。准确性与合规性被视为不可妥协的“基础因素”：若不具备这两者，无论其透明度如何，AI系统均被视为一种负担。操作杠杆（速度与成本）作为次要约束，决定了实际可行性；而易于理解则成为采纳的门槛，塑造了AI工具在实践中是否被信任、使用并具备可辩护性。