Algorithmic Creditworthiness and Real Time Risk Governance in Artificial Intelligence Enabled Digital Lending Platforms
Keywords:
Real time credit scoring, artificial intelligence, digital lending, prudential regulationAbstract
The digital transformation of financial services has accelerated the evolution of credit risk assessment from rule based, document driven processes into dynamic, data intensive, and algorithmically mediated decision systems. This shift has been especially pronounced in online lending platforms, fintech firms, and non banking finance companies, where real time data processing, alternative data sources, and artificial intelligence based analytics have enabled lenders to assess borrower risk with unprecedented speed and granularity. However, this technological expansion has also introduced new epistemic, ethical, regulatory, and systemic challenges that question whether contemporary credit scoring models can simultaneously achieve accuracy, fairness, transparency, and prudential soundness. This study develops a comprehensive theoretical and analytical investigation of real time AI driven credit scoring systems by integrating financial inclusion theory, machine learning credit risk models, legal prudential doctrines, and big data governance frameworks. Drawing on interdisciplinary scholarship and recent empirical insights from emerging markets and global fintech ecosystems, the article critically examines how algorithmic credit scoring reshapes the meaning of creditworthiness, reallocates risk between lenders and borrowers, and transforms regulatory oversight in the digital economy.
The discussion extends these findings by exploring the implications of algorithmic credit governance for financial inclusion, prudential regulation, and ethical accountability. It argues that while digital credit scoring can expand access to finance for previously excluded populations, it can also reproduce and even intensify structural inequalities through biased data and opaque algorithms. The article further contends that existing regulatory frameworks, grounded in traditional notions of documentation, collateral, and static risk assessment, are increasingly misaligned with the real time, data driven nature of digital lending. The study concludes by proposing a theoretically grounded vision for responsible AI credit scoring that integrates technological innovation with legal prudence, transparency, and social responsibility.
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