The rapid expansion of digital lending platforms across both developed and emerging economies has fundamentally transformed the architecture of credit markets, particularly in segments traditionally excluded from formal banking systems. This transformation has been driven not merely by the migration of financial services to online channels but by the convergence of real time data processing, artificial intelligence driven credit scoring, and the incorporation of alternative data streams into risk evaluation processes. Traditional credit scoring methods, grounded in historical repayment records and static financial ratios, are increasingly inadequate for the realities of platform based lending where borrowers often have thin or nonexistent credit files, incomes are volatile, and financial behaviors are embedded in complex digital ecosystems. The need for more responsive, inclusive, and analytically robust credit risk frameworks has therefore become a defining challenge of contemporary financial innovation. Against this backdrop, this article develops an original, theoretically integrated, and empirically grounded framework for understanding how real time artificial intelligence driven credit scoring systems reshape risk assessment, borrower inclusion, and institutional decision making in digital loan platforms.

Drawing on a wide range of literature on technology acceptance, financial inclusion, credit risk modeling, organizational use of artificial intelligence, and regulatory governance, this study situates real time credit scoring within broader debates about digital transformation and socio technical change. The work is anchored in the recent contribution by Modadugu, Venkata, and Venkata, who demonstrate how real time artificial intelligence architectures can continuously update borrower risk profiles by integrating transactional, behavioral, and contextual data streams in digital loan platforms (Modadugu et al., 2025). Their findings provide a critical reference point for examining how the move from batch based to continuous credit evaluation alters both the epistemology of risk and the operational practices of lenders. Rather than treating creditworthiness as a static attribute, real time systems conceptualize it as a dynamic and evolving construct, continuously recalibrated through machine learning models that absorb new information as it is generated.

By integrating these diverse strands into a single coherent framework, the article demonstrates that real time artificial intelligence driven credit scoring is not merely a technical upgrade but a profound reconfiguration of how creditworthiness is defined, measured, and governed. The study concludes that the future of digital lending will depend not only on algorithmic sophistication but also on institutional trust, regulatory alignment, and ethical stewardship of data. In doing so, it offers scholars, practitioners, and policymakers a deep and theoretically grounded understanding of one of the most consequential developments in contemporary financial systems.