Algorithmic & Architectural Intelligence in Banking: A Theory-Driven Framework and Empirical Evidence from Banking Decision-Makers
DOI:
https://doi.org/10.54536/ajbmi.v1i1.7063Keywords:
Algorithmic Intelligence, Banking Decision-Making, Decision Architecture, Governance, Organizational DesignAbstract
The increasing adoption of algorithmic systems in banking institutions has transformed how strategic and behavioral decisions are structured and enacted. While existing studies emphasize the efficiency and predictive advantages of algorithmic intelligence, limited attention has been given to the organizational and institutional conditions through which such systems influence decision outcomes. Addressing this gap, the present study develops and empirically tests a theory-driven framework that examines how algorithmic intelligence and decision architecture jointly shape strategic and behavioral decision outcomes in banking institutions. Grounded in decision architecture theory and institutional theory, the study conceptualizes algorithmic intelligence as an embedded organizational capability whose effects are mediated by decision architecture and conditioned by the banking environment. A quantitative research design was employed, drawing data from senior banking decision-makers in Philippine banks and regulated financial institutions. Data were collected using a structured survey instrument and analyzed through descriptive statistics, regression analysis, and moderation testing to examine the proposed relationships. The findings indicate that algorithmic intelligence significantly influences strategic and behavioral decision outcomes, particularly in terms of credit utilization behavior, interest sensitivity, and repayment discipline. These effects are strengthened when algorithmic systems are embedded within well-defined decision architectures and are more pronounced in digital banking environments characterized by higher automation intensity. The results further underscore the importance of governance and institutional safeguards in moderating the behavioral implications of analytics-driven decision systems. By empirically validating an integrative framework, this study contributes to the literature by advancing a human-centered, architecture-based understanding of algorithmic decision-making in banking. The findings offer theoretical extensions to decision architecture and institutional perspectives and provide practical insights for banking executives and policymakers seeking to design accountable, resilient, and effective algorithmic decision environments.
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