Continuous Credit Scoring Using Artificial Intelligence and Streaming Financial Data
Keywords:
Artificial intelligence, credit scoring, real time data processing, financial technologyAbstract
The digital transformation of financial services has radically altered the epistemological foundations of credit evaluation, risk governance, and financial inclusion. Traditional credit scoring models, once grounded in static financial statements and historical repayment records, are increasingly insufficient in a world characterized by real time transactions, behavioral data streams, and algorithmic decision making. The emergence of artificial intelligence driven credit platforms has enabled lenders to incorporate large volumes of heterogeneous data into predictive systems that continuously update borrower risk profiles, but it has also generated profound theoretical, ethical, and regulatory challenges. This article develops a comprehensive analytical framework for understanding how artificial intelligence and real time data processing reshape credit scoring, risk analysis, and governance within digital lending ecosystems. Drawing upon a broad interdisciplinary literature that spans financial technology, machine learning, regulatory theory, and socio technical systems, the study critically examines how algorithmic credit scoring systems function not merely as technical tools but as institutional actors that redistribute power, risk, and opportunity across financial markets. Central to this investigation is the growing scholarly recognition that real time credit scoring enables lenders to move from episodic evaluation toward continuous risk surveillance, thereby transforming the temporal structure of financial trust and obligation. This conceptual shift is particularly visible in contemporary loan platforms that integrate artificial intelligence with streaming data architectures, as demonstrated by recent empirical and theoretical work on real time credit scoring and risk analysis in digital loan environments (Modadugu et al., 2025).
The discussion further explores how regulatory frameworks must evolve to accommodate the temporal acceleration and complexity introduced by artificial intelligence based credit scoring. Fintech and regtech innovations offer potential pathways for oversight, yet they also raise new questions about accountability, transparency, and the protection of vulnerable borrowers. By integrating theoretical perspectives from financial regulation, machine learning, and social governance, this article provides a holistic understanding of the opportunities and risks associated with real time, artificial intelligence driven credit scoring. In doing so, it contributes to the development of a more nuanced and ethically informed vision of digital lending that aligns technological innovation with sustainable and inclusive financial systems.
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Copyright (c) 2026 Roland F. Keaton (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
