Algorithmic Creditworthiness and Financial Inclusion: Real-Time AI Credit Scoring under Conditions of Imperfect Information

Abstract

The rapid digitization of financial services has fundamentally transformed the informational architecture of credit markets, particularly through the integration of artificial intelligence–driven real-time credit scoring systems. This transformation has profound implications for longstanding theoretical problems in credit allocation, including adverse selection, moral hazard, and credit rationing under imperfect information. Building upon classical credit market theories and contemporary empirical insights, this article develops a comprehensive analytical examination of AI-enabled credit scoring platforms as institutional mechanisms for risk assessment, inclusion, and efficiency. Drawing strictly on an interdisciplinary body of literature spanning financial economics, fintech studies, regulatory reports, and data governance scholarship, the study interrogates how real-time data processing reshapes borrower–lender relationships, alters the distribution of credit access, and redefines the boundaries of financial inclusion.

The research is anchored in an extensive theoretical synthesis that situates AI credit scoring within the historical evolution of credit information systems, from traditional relationship banking and bureau-based scoring to alternative data–driven fintech lending models. Particular attention is paid to the integration of machine learning, streaming data, and platform-based decision architectures in modern loan origination processes, with emphasis on their capacity to mitigate information asymmetries while simultaneously generating new forms of opacity and algorithmic risk. The analytical framework engages directly with recent scholarly contributions on real-time credit scoring and risk analysis in AI-powered loan platforms, situating these advances within broader debates on digital financial inclusion, data ethics, and systemic stability (Modadugu et al., 2025).

Methodologically, the study adopts a qualitative, theory-driven research design that synthesizes insights from global financial inclusion datasets, regulatory disclosures, central bank reports, and peer-reviewed academic research. Rather than relying on econometric modeling or quantitative simulation, the article emphasizes deep textual analysis and conceptual interpretation to elucidate causal mechanisms and institutional dynamics. The results section presents a structured interpretive analysis of how AI-based credit scoring affects access to unsecured consumer lending, particularly for low- and moderate-income populations, while also examining its implications for credit risk management, pricing, and portfolio resilience.

The discussion advances a nuanced theoretical argument that real-time AI credit scoring represents neither a panacea for financial exclusion nor a deterministic source of discrimination, but rather a contingent institutional innovation whose outcomes depend critically on governance structures, regulatory oversight, and data infrastructure. By integrating classical economic theory with contemporary fintech evidence, the article contributes a comprehensive, publication-ready framework for understanding the role of AI in modern credit markets. The findings hold significant implications for policymakers, financial institutions, and researchers concerned with inclusive growth, technological governance, and the future of consumer credit systems.