Design Principles for Explainable AI in Finance: A Multi- Stakeholder Framework

Electronic marketplaces increasingly deploy AI for critical decisions, onboarding, pricing, approvals, and fraud detection. Effective explainable AI (XAI) must satisfy diverse stakeholders while maintaining technical accuracy. This research presents qualitative methods analyzing 24 semi-structured interviews across five specialized groups: retail users, advisors, developers, risk officers, and regulators from nine European countries. The investigation examined lending and buy-now-pay-later platforms through two scenarios - credit-limit changes and fraud-flag reviews - using validated XAI evaluation techniques. Three patterns emerged. First, process-oriented explanations that explain "why this case" enhance fairness perceptions and clarify actions. Second, progressive disclosure frameworks - beginning with concise text, then offering detailed visualizations - optimize comprehension without overload. Third, raw confidence metrics create uncertainty, whereas counterfactual examples demonstrate decision-altering factors effectively. These patterns, derived through systematic thematic analysis, challenge conventional approaches that prioritize algorithmic transparency over stakeholder comprehension. The study establishes six design principles for interpretable AI and proposes a multi-stakeholder evaluation framework connecting explanation tools to technical accuracy and human-centered outcomes. This responsible AI approach provides specialized meaning through role-appropriate explanations. The governance toolkit comprises operational performance indicators and audit-compliant documentation protocols, offering practical implementation methods for platform operators and regulators navigating XAI deployment in financial marketplaces.