Real-Time Credit Scoring, Artificial Intelligence, and Data-Intensive Risk Governance: Theoretical Foundations, Methodological Advances, and Systemic Implications for Digital Lending Platforms - Armand Léon Dupont (Emerging Frontiers Library for The American Journal of Interdisciplinary Innovations and Research, 2025)
The transformation of credit markets through artificial intelligence-driven analytics and real-time data processing has redefined the epistemic foundations of credit risk assessment, financial inclusion, and regulatory oversight. Over the past two decades, credit scoring has evolved from static, historically grounded statistical models toward dynamic, continuously updated systems capable of integrating heterogeneous data streams at unprecedented temporal resolutions. This evolution has been accelerated by advances in machine learning, big data infrastructures, and digital financial platforms, which together have enabled real-time credit scoring architectures that promise enhanced predictive accuracy, operational efficiency, and market responsiveness. At the same time, these developments have raised fundamental theoretical, methodological, ethical, and regulatory questions that remain insufficiently resolved in contemporary scholarship. This article develops a comprehensive and integrative research framework for understanding real-time credit scoring and risk analysis within digital loan platforms, grounding the analysis in interdisciplinary literature spanning finance, data science, regulatory theory, and socio-technical systems research. Drawing extensively on existing scholarship, the study situates real-time credit scoring within broader debates on fintech-driven financial inclusion, algorithmic governance, and systemic financial stability. Particular attention is given to the conceptual shift from periodic, backward-looking credit evaluations to continuous, forward-looking risk monitoring, as articulated in recent research on AI-enabled loan platforms (Modadugu et al., 2025). The article elaborates the theoretical underpinnings of machine learning-based credit analytics, including learning paradigms, feature construction, temporal modeling, and interpretability challenges, while critically engaging with counter-arguments concerning opacity, bias, and regulatory compliance (Alhaddad, 2018; Bodo et al., 2017). Methodologically, the paper adopts a qualitative, literature-driven analytical design that synthesizes insights from empirical studies, conceptual models, and policy-oriented analyses to derive an integrated explanatory narrative. The results section presents a structured interpretation of how real-time credit scoring systems reshape risk classification, borrower–lender relationships, and institutional decision-making processes. These findings are not presented as numerical outputs but as analytically grounded patterns emerging across the reviewed literature, highlighting convergences and tensions among competing scholarly perspectives (Arner et al., 2016; Brummer & Yadav, 2018). The discussion extends this analysis by interrogating the implications of real-time risk analytics for financial regulation, ethical accountability, and long-term financial stability, while identifying persistent gaps related to governance, transparency, and socio-economic impact. By offering an expansive theoretical and methodological synthesis, this article contributes to ongoing academic and policy debates on the role of artificial intelligence in credit markets and provides a foundation for future empirical and normative research.