Artificial Intelligence in Data Governance for Financial Decision-Making: A Systematic Review

Artificial intelligence (AI) has been increasingly embedded within data-driven financial decision-making; however, its effectiveness was found to remain dependent upon the maturity of data governance frameworks. This systematic review was conducted in accordance with PRISMA 2020 guidelines to synthesise evidence from 1155 Scopus-indexed studies published between 2015 and 2025. A mixed-methods design combining corpus analysis, quantile radar regression, and radar visualisation of structural equation modelling (SEM) was employed. Empirical validation was found to demonstrate a robust model fit (CFI = 0.947; RMSEA = 0.041). Governance maturity was confirmed as a mediating construct (β = 0.73) linking AI integration (β = 0.76) to financial outcomes (β = 0.71). The findings were found to indicate that algorithmic capacity alone does not ensure decision quality without transparent, auditable, and ethically grounded governance systems. A quantile-sensitive radar visualisation is advanced in this review, offering conceptual and methodological novelty for explainable, responsible, and data-centric financial analytics. This study is found to contribute to the ongoing discourse on sustainable digital transformation within AI-enabled financial ecosystems.