Trade-offs in Financial AI: Explainability in a Trilemma with Accuracy and Compliance
TL;DR
This study empirically investigates how practitioners prioritize explainability relative to four competing factors: accuracy, compliance, cost, and speed, and reveals that these priorities are structured not as a simple trade-off, but as a system of distinct prerequisites and constraints.
Abstract
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.
