This paper introduces a systems-based conceptual framework to map and analyze behavioral externalities of AI in consumer finance. The goal is to advance scholarly understanding of how technology interacts with consumer psychology, regulatory environments, and market structures. Traditional economic notions of externalities focus on quantifiable costs or benefits that fall outside the primary transaction. This paper extends that concept to include behavioral spillovers: non-market interactions that subtly erode financial literacy, autonomy, and fairness. Current research on AI in finance focuses largely on technical metrics of accuracy and efficiency or macroeconomic outcomes (Barocas et al., 2023; Sunstein, 2024). Yet, the daily experience of end-users engaging with AI-powered tools reveals patterns of disengagement, dependency, overconfidence, or exclusion that are poorly captured by these lenses. A systems view helps bridge this gap by considering technology, human behavior, and institutional design as interconnected and evolving.

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