Integrating Artificial Intelligence, Risk Governance, and Sustainability in Contemporary Financial and Socio-Technical Systems: A Holistic Analytical Framework

Abstract

The accelerating integration of artificial intelligence into financial systems, risk management infrastructures, and sustainability-oriented decision environments represents one of the most consequential transformations in contemporary socio-technical systems. Across domains such as credit scoring, insurance, disaster management, supply chain governance, energy efficiency, and climate-related financial stability, artificial intelligence has emerged not merely as a technical tool but as an epistemic and institutional force reshaping how uncertainty, risk, and value are conceptualized and governed. This article develops a comprehensive, theoretically grounded, and empirically informed analysis of artificial intelligence–enabled risk assessment and decision-making, with particular emphasis on real-time credit scoring and data-driven financial platforms. Drawing on interdisciplinary scholarship spanning sustainability studies, risk theory, financial economics, organizational studies, and systems engineering, the study situates AI-driven credit risk analytics within a broader ecosystem of governance challenges and ethical considerations.
Central to the analysis is the recognition that real-time credit scoring systems exemplify a paradigmatic shift in risk evaluation, characterized by continuous data flows, algorithmic inference, and dynamic feedback mechanisms. Such systems challenge traditional notions of model stability, accountability, and transparency while simultaneously promising efficiency gains, improved inclusion, and more granular risk differentiation (Modadugu et al., 2025). By embedding this focal case within a wider analytical landscape that includes enterprise risk management, climate-related financial uncertainty, supply chain vulnerability, and sustainable artificial intelligence, the article advances a unified conceptual framework capable of capturing both the opportunities and systemic risks associated with AI-driven decision infrastructures.
Methodologically, the article adopts a qualitative, theory-integrative research design grounded in critical synthesis and interpretive analysis of existing peer-reviewed literature. Rather than pursuing statistical generalization, the study emphasizes analytical depth, tracing conceptual lineages, identifying points of convergence and tension across disciplines, and interrogating underlying assumptions embedded in prevailing AI applications. The results reveal that while artificial intelligence enhances predictive capacity and operational responsiveness, it also amplifies model risk, institutional opacity, and socio-ethical fragility when deployed without robust governance mechanisms.
The discussion advances a multi-layered interpretation of AI-enabled risk systems, highlighting the necessity of aligning technical innovation with organizational culture, regulatory coordination, and sustainability imperatives. The article concludes by outlining future research directions that emphasize reflexive governance, human–AI collaboration, and the integration of environmental and social risk metrics into financial AI architectures. Through its extensive theoretical elaboration and critical engagement with the literature, this study contributes to ongoing debates on the responsible, sustainable, and resilient deployment of artificial intelligence in complex risk environments.