The rapid expansion of financial technology has fundamentally transformed the architecture of credit markets by introducing artificial intelligence driven analytics, real time data processing, and alternative data infrastructures that challenge the dominance of traditional credit scoring models. This transformation is particularly consequential in emerging and digitally evolving economies where financial exclusion has historically constrained entrepreneurship, household resilience, and economic growth. Contemporary FinTech credit platforms no longer rely solely on static financial histories but integrate behavioral, transactional, and contextual data streams to generate dynamic risk profiles. The theoretical promise of such systems lies in their capacity to overcome informational asymmetries, enhance predictive accuracy, and widen access to credit for individuals and micro enterprises that have been marginalized by legacy banking frameworks (Demirguc Kunt et al., 2018; Chen et al., 2021). However, this promise is accompanied by equally significant concerns related to algorithmic opacity, data privacy, bias amplification, and systemic risk, making the governance of AI driven credit infrastructures a critical research frontier (Qureshi et al., 2024; Zhang et al., 2023).

Within this complex context, real time credit scoring emerges as a pivotal innovation. Unlike batch based or periodic assessment models, real time scoring continuously updates borrower risk using live transactional data, thereby enabling adaptive lending decisions, instant loan approvals, and proactive default mitigation. The work of Modadugu et al. (2025) has provided one of the most comprehensive contemporary treatments of this paradigm by demonstrating how real time data pipelines integrated with machine learning architectures substantially improve the responsiveness and stability of loan platforms. Their findings suggest that real time analytics not only enhance the accuracy of default prediction but also enable more inclusive lending strategies by recognizing positive behavioral signals that are invisible in conventional credit files. Building upon this foundational insight, the present article seeks to develop a broader theoretical and methodological framework that situates real time AI credit scoring within the political economy of financial inclusion, institutional trust, and digital transformation.

The central objective of this research is to generate a comprehensive and publication ready theoretical analysis of how AI powered real time credit scoring systems reshape risk governance, borrower inclusion, and market structure in FinTech ecosystems. Drawing exclusively on the scholarly sources provided, the study synthesizes insights from financial economics, information systems theory, development finance, and ethical governance to construct an integrated analytical model. It addresses three interrelated research questions. First, how do AI driven real time credit scoring models differ structurally and functionally from traditional credit assessment frameworks. Second, in what ways do these models contribute to financial inclusion, particularly for micro enterprises and underserved populations. Third, what new risks, biases, and regulatory dilemmas arise from the increasing reliance on algorithmic decision making in credit markets.

Methodologically, the study adopts a qualitative integrative review and theoretical synthesis approach. Rather than conducting primary empirical data collection, it systematically interprets and triangulates findings from existing high quality academic and policy oriented literature to generate a coherent conceptual framework. This approach is particularly suitable given the rapid evolution of FinTech technologies and the ethical and institutional complexities they introduce, which cannot be adequately captured through narrow quantitative metrics alone (Frost et al., 2019; Hollnagel et al., 2006). The analysis emphasizes explanatory depth, historical contextualization, and critical comparison across scholarly traditions.

The results of the study demonstrate that real time AI credit scoring fundamentally reconfigures the relationship between lenders and borrowers by shifting from static judgment to continuous risk negotiation. This shift enables more flexible credit products, improved portfolio performance, and enhanced borrower engagement, thereby reinforcing both financial inclusion and financial stability when properly governed (Modadugu et al., 2025; Rehman et al., 2025). At the same time, the findings reveal that algorithmic systems can reproduce and even intensify social inequalities if training data, feature selection, and institutional incentives are not carefully aligned with principles of fairness, transparency, and accountability (Ali, 2024; Salami et al., 2024).

The discussion situates these findings within broader debates on digital capitalism, technological determinism, and institutional resilience. It argues that AI driven credit infrastructures should not be understood merely as technical tools but as socio economic systems embedded in power relations, regulatory regimes, and cultural expectations. By integrating insights from resilience engineering, resource based theory, and diffusion of innovation, the article proposes a holistic governance model that balances innovation with ethical responsibility and systemic stability (Barney, 1991; Rogers, 2003; Hollnagel et al., 2006).

In conclusion, the article contends that real time AI credit scoring represents one of the most transformative developments in contemporary finance, with profound implications for inclusion, risk management, and economic development. Yet its ultimate impact will depend on the institutional frameworks, ethical safeguards, and participatory design principles that guide its implementation. By offering a rigorous and deeply elaborated theoretical foundation, this study contributes to both academic scholarship and policy discourse on the future of FinTech and inclusive finance.