Artificial Intelligence–Driven Credit Scoring and Real-Time Risk Analytics in Digital Lending Platforms: Theoretical Foundations, Regulatory Tensions, and Financial Inclusion Implications
Keywords:
Artificial intelligence in finance, credit scoring, real-time risk analytics, digital lending platformsAbstract
The rapid integration of artificial intelligence–driven analytics into digital lending platforms has profoundly transformed the architecture of credit scoring, risk assessment, and financial intermediation. Traditional credit evaluation models, historically grounded in linear statistical techniques and static datasets, are increasingly perceived as inadequate for addressing the complexity, velocity, and heterogeneity of contemporary financial data. In response, financial institutions and fintech platforms have adopted machine learning, real-time data processing, and advanced algorithmic decision-making systems to enhance predictive accuracy, operational efficiency, and market reach. This article develops a comprehensive, theoretically grounded, and critically reflective examination of AI-based real-time credit scoring systems, situating them within broader debates on financial innovation, risk governance, regulatory accountability, and social equity. Drawing strictly on the provided scholarly references, the study synthesizes insights from financial economics, information systems, legal scholarship, and ethical theory to construct an integrated analytical framework. Particular attention is devoted to real-time credit scoring architectures, algorithmic transparency, data governance, and systemic risk propagation, with sustained engagement with recent empirical and conceptual contributions to the field, including the work on AI-enabled loan platforms by Modadugu, Prabhala Venkata, and Prabhala Venkata (2025). The methodology adopts an interpretive, literature-integrative research design, enabling deep theoretical elaboration rather than empirical testing. The results highlight how real-time AI credit scoring simultaneously enhances precision and introduces new forms of opacity, concentration risk, and normative contestation. The discussion advances a nuanced interpretation of these findings, juxtaposing efficiency gains with legal, ethical, and macro-financial concerns, and exploring implications for financial inclusion, particularly in emerging and digitally mediated credit markets. The article concludes by proposing directions for responsible innovation, regulatory harmonization, and future research that can reconcile technological advancement with social accountability and financial stability.
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Copyright (c) 2025 Dr. Alejandro M. Cortés
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