Algorithmic Accountability in U.S. Consumer FinTech: Governance Mechanisms for Credit Risk, Fair Lending, and Financial Stability

Abstract

Financial technology (FinTech) has become a core component of the United States financial system, reshaping payment infrastructure, consumer credit markets, and data-driven financial intermediation. While FinTech innovation has improved efficiency and expanded financial access, its rapid scaling has also introduced new sources of systemic risk, regulatory fragmentation, and consumer vulnerability—particularly as artificial intelligence increasingly governs financial decision-making. This study examines FinTech innovation through an integrated governance lens, focusing on how AI-enabled intermediation interacts with financial stability and public-interest outcomes in the United States. Using a multi-source, institutionally grounded panel dataset spanning 2012–2022, the analysis conceptualizes FinTech as part of national financial infrastructure rather than as a peripheral technological disruption. The empirical strategy combines a composite measure of FinTech intensity with indicators of explainable AI adoption and institutional governance strength to assess their joint effects on financial stability risk and public-interest performance. Fixed-effects panel models, interaction specifications, and robustness checks are employed to isolate institutional and temporal dynamics. The results reveal three central findings. First, greater FinTech intensity enhances intermediation efficiency and financial inclusion but is associated with elevated short-term financial stability risks when deployed without adequate oversight. Second, the adoption of explainable AI significantly moderates these risks by reducing algorithmic opacity and improving auditability and supervisory visibility. Third, governance capacity—reflected in regulatory coordination, disclosure mandates, and supervisory engagement—emerges as a decisive factor in aligning technological innovation with systemic resilience and consumer trust. These findings suggest that the principal challenge facing the U.S. financial system is not technological innovation itself, but the institutional lag between innovation and governance. By demonstrating that explainable AI functions as a stabilizing mechanism rather than merely a compliance tool, the study advances both financial stability theory and the literature on AI governance in finance. The paper contributes a conceptual–methodological framework that integrates innovation, governance, and public interest, offering actionable policy insights for regulators, financial institutions, and FinTech firms. More broadly, the analysis underscores the importance of governing AI-enabled finance in a manner that supports long-term financial stability, equity, and national economic resilience.