Enhancing Financial Decision Quality in Smart Organizations through Explainable AI and Real Time Analytics with AI Governance Maturity

Abstract

This study investigates the links among explainable AI capability, real time financial analytics capability, and financial decision quality within smart organizations while assessing how AI governance maturity moderates those links. Drawing on organizational information processing theory and dynamic capabilities theory, it frames financial decision quality as the product of organizational capabilities that convert clear AI outputs and timely financial data into accurate, evidence based, and strategically coherent choices. AI governance maturity serves as a boundary condition shaping whether explainability translates into stronger financial judgments. A quantitative cross sectional design gathered responses on a five point Likert scale from 385 Vietnamese professionals knowledgeable in AI, financial analytics, governance, or organizational decision making. Analysis relied on IBM SPSS version 26 for reliability checks, exploratory factor analysis, and multiple linear regression, with Hayes Process Macro Model 1 testing the moderation. Findings show that explainable AI capability (β = 0.636) and real time financial analytics capability (β = 0.580) both raise financial decision quality, the former exerting a modestly larger direct influence that underscores the value of interpretable and actionable algorithmic advice. AI governance maturity further amplifies the positive path from explainable AI capability to financial decision quality through a significant interaction term of 0.436. The results indicate that explainability by itself cannot guarantee high quality financial decisions; smart organizations must therefore pair understandable AI systems and timely analytics with model validation, human oversight, continuous monitoring, auditability, and explicit accountability.