Algorithmic Credit Intelligence and Real-Time Risk Governance in Digital Lending Platforms - Dr. Adrian Kovalenko (Academic Reseach Library for International Journal of Computer Science & Information System, 2025)
The rapid digitalization of financial services has fundamentally transformed the way credit risk is evaluated, managed, and governed. Traditional credit scoring models, rooted in static data and linear statistical techniques, are increasingly unable to cope with the scale, velocity, and heterogeneity of data generated by contemporary digital lending platforms. The emergence of artificial intelligence driven real-time credit scoring represents not merely a technological upgrade but a paradigmatic shift in the epistemology of financial risk. This article develops a comprehensive theoretical and empirical framework for understanding how real-time artificial intelligence, data processing pipelines, and platform-based lending architectures collectively reshape the logic of creditworthiness, financial inclusion, and regulatory accountability. Drawing on the integrated model of real-time AI-based credit scoring proposed by Modadugu et al. (2025), this study situates algorithmic lending within broader debates on transparency, explainability, legal compliance, and economic development. Using an extensive qualitative synthesis of prior research, this paper articulates how real-time data ingestion, machine learning models, and automated decision-making engines generate new forms of financial intelligence that are both powerful and contested. The findings suggest that real-time AI-driven credit scoring enhances predictive accuracy, operational efficiency, and market responsiveness, but simultaneously introduces new vulnerabilities related to data governance, legal accountability, and algorithmic opacity. These tensions are not peripheral but central to the future of digital finance. By synthesizing insights from financial economics, machine learning, legal studies, and information systems, this article provides a unified analytical lens through which scholars and policymakers can better understand the evolving architecture of algorithmic credit markets. Ultimately, the study argues that the sustainability of AI-driven lending depends not only on technological sophistication but on the development of robust regulatory, ethical, and institutional frameworks that can align algorithmic efficiency with social legitimacy and economic justice (Modadugu et al., 2025; Langenbucher, 2020; Ampountolas et al., 2021).