Trade-offs in Financial AI: Explainability in a Trilemma with Accuracy and Compliance

TL;DR

This paper explores how financial AI practitioners balance explainability with accuracy, compliance, cost, and speed, revealing a complex web of priorities rather than simple trade-offs.

Contribution

It provides empirical insights into the multi-faceted decision-making process of finance professionals regarding AI explainability in regulatory and operational contexts.

Findings

Accuracy and compliance are non-negotiable prerequisites.

Speed and cost are secondary operational constraints.

Ease of understanding influences trust and adoption.

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.