Beyond algorithmic trust: human–AI interaction competency strengthens AI-driven financial decision-making and financial resilience

Introduction: This study examines the impact of AI trading usage on investor financial resilience through a cognitive-behavioral process grounded in the Stimulus-Organism-Response (S-O-R) framework. Methods: AI trading usage and perceived financial uncertainty were conceptualized as stimuli, perceived algorithmic trust as the organism, and sustainable investment behavior and investor financial resilience as sequential responses. Data were collected from 569 middle-income retail investors across major Indian cities and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results: AI trading usage and perceived financial uncertainty positively influenced perceived algorithmic trust, which subsequently enhanced sustainable investment behavior and investor financial resilience. Human-AI interaction competency significantly moderated the relationship between perceived algorithmic trust and sustainable investment behavior. Discussion: The study advances understanding of AI-driven financial decision-making by highlighting the sequential role of perceived algorithmic trust, sustainable investment behavior, and Human-AI interaction competency in strengthening investor financial resilience and adaptive investment practices.