Jul 2026· AI and Ethics· Vol 6· 0 citations· 23 references
Computer Science
TL;DR
The study contributes a theory-differentiated analytical framework for AI governance in financial systems, advancing beyond descriptive policy analysis to provide structured, actionable governance guidance for an emerging economy undergoing rapid digital transformation.
This paper examines how risk governance architecture shapes interactions between artificial intelligence (AI) and environmental, social, and governance (ESG)-oriented sustainability policies in the banking industry. Most current research treats AI as a technological capability that directly affects ESG performance, yet little is known about the governance systems that produce these outcomes. Using PRISMA-guided SLR procedures, we selected 20 studies from 248 initial records identified in the Scopus and Web of Science databases that met the inclusion criteria and conducted a thematic synthesis. The results show that AI is primarily used in ESG disclosure and reporting, credit risk assessment, climate risk analytics, sustainable finance, and responsible AI governance. The literature remains dispersed across theoretical stances, including the resource-based view, stakeholder theory, institutional theory, legitimacy theory, and AI governance literature. Previous research has largely ignored the governance mechanisms that enable successful implementation, focusing instead on the direct implications of AI adoption for ESG-related outcomes. The study proposes an AI–ESG risk governance integrative framework to address this gap. This framework places risk governance architecture at the center of the relationship among AI capabilities, institutional pressures, stakeholder expectations, and ESG-oriented sustainability outcomes. The approach views AI as a strategic capacity integrated into enterprise-wide risk governance systems rather than merely a technical or compliance tool. The results indicate that strong governance arrangements, such as model governance, accountability frameworks, board supervision, and alignment with organizational risk appetite, are necessary for successfully deploying AI-enabled ESG. By offering an integrative theoretical framework and practical insights for banking organizations seeking to improve sustainability performance and long-term resilience through responsible AI use, this study contributes to the growing body of AI-ESG literature.
Rini Marlina, Rosa Christiana Esti Noor Sumaryanti, Poltak Maruli John Liberty Hutagaol· Asian Management and Busines...· 0 citations
This study examines the role of Artificial Intelligence (AI) in enhancing public financial management and budgeting systems, with particular emphasis on its implications for efficiency, transparency, accountability, and fiscal governance. The increasing complexity of government financial operations and the growing demand for evidence-based decision-making have created the need for innovative technological solutions capable of improving budgeting processes and resource allocation. The study adopts a qualitative approach based on an extensive review of scholarly literature, policy documents, institutional reports, and empirical studies relating to AI applications in public finance. The theoretical foundation of the study is anchored on the Technology Acceptance Model (TAM) and Public Choice Theory. TAM explains the factors influencing the adoption of AI technologies by public officials, while Public Choice Theory highlights how AI can reduce inefficiencies, corruption, and waste through automated monitoring and transparent decision-making mechanisms. Findings reveal that AI significantly improves budget forecasting, expenditure monitoring, fraud detection, financial reporting, and policy evaluation through the use of machine learning, predictive analytics, and intelligent automation. The study also identifies challenges such as inadequate digital infrastructure, cybersecurity risks, poor data quality, limited technical expertise, and resistance to organisational change. The study concludes that AI possesses substantial potential to transform public financial management by promoting fiscal discipline, strengthening accountability, and enhancing the effectiveness of budgeting systems. It recommends increased investment in digital infrastructure, human capacity development, data governance frameworks, cybersecurity measures, and ethical AI policies to ensure successful implementation. The study contributes to the growing body of knowledge on digital governance and provides practical insights for policymakers, public administrators, and development practitioners seeking to leverage AI for improved financial management and sustainable public sector performance.
Ukpong Johnson, O. Duke, Bayo Lekara et al.· Science Journal of Business...· 0 citations
This article examines three interconnected dimensions of responsible AI for enterprise modernization: governance infrastructure for accountable AI deployment, algorithmic equity in high-impact decision environments, and the evolving international regulatory landscape shaping enterprise AI governance.
M. Modi· International Journal of Eng...· 0 citations
The research concludes that banking regulatory compliance in the digital era cannot be achieved through passive adherence to legacy frameworks; it requires a proactive, sociotechnical approach to algorithmic transparency.
Joseph Kikomeko, Augustine Alloysius Ogbe· Journal of Banking and Finan...· 0 citations
Applied to African AI governance domains, this framework reveals how institutional fragmentation, infrastructural dependency, and global platform dominance undermine state-centred regulatory models.
Chijioke I. Okorie· Potchefstroom Electronic Law...· 0 citations
It is argued that ESG frameworks, which evolved through incremental adjustment, may prove insufficient for governing algorithmic systems and proposed adding a fourth pillar, Algorithmic Governance, within an extended ESGA framework to address risks that transcend traditional governance categories.
Pitabas Mohanty, Supriti Mishra· Business Strategy and the En...· 0 citations
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