Architecting financial well-being in algorithmic credit systems: The roles of human capability and institutional design

The rapid diffusion of algorithmic credit systems has transformed lending decisions, yet their implications for financial well-being remain theoretically fragmented and empirically contested. Existing studies often adopt technologically deterministic perspectives, emphasizing access and efficiency while overlooking the roles of borrower capability and institutional governance. This study advances a socio-technical and architectural systems perspective by examining how algorithmic credit systems influence financial well-being and how these effects are conditioned by human capability and institutional design. Using a quantitative, explanatory, cross-sectional design, data were collected from 400 users of algorithmic and digitally mediated credit platforms. Multiple regression and moderation analyses were employed to assess the direct and conditional relationships among algorithmic credit systems, human capability, institutional design, and multidimensional financial well-being outcomes, including repayment behavior, financial stress, and financial resilience. Measurement reliability and validity were established through Cronbach’s alpha and principal component analysis. The results indicate that algorithmic credit systems are positively associated with repayment behavior and financial resilience but are also linked to higher levels of financial stress. Moderation analysis reveals that these effects are significantly shaped by contextual factors: higher levels of human capability and stronger institutional design amplify positive outcomes and mitigate adverse effects. These findings suggest that financial well-being is not an automatic byproduct of automated credit efficiency but an emergent outcome of architectural alignment among technology, borrower capability, and governance structures. The study contributes to theory by empirically integrating technological, human, and institutional dimensions within a single architectural framework, moving beyond isolated analyses of digital credit.