The rapid rise of artificial intelligence (AI) in finance promises to make financial advice less costly and algorithmically objective. However, little is known about how negative financial experiences, particularly traumatic ones such as fraud, influence consumers' decisions when choosing AI for financial advice. Consumer fraud is widespread, and research shows it severely damages victims' financial well-being by eroding trust and confidence (Brenner et al., 2020). This creates a paradox: those who need guidance most may be least willing to trust human advisors. This study bridges research on the consequences of fraud and fintech adoption to address a critical gap: Do fraud victims seek out AI financial advice as an alternative? We propose an "algorithmic trust hypothesis," suggesting that when human intermediaries cause harm, victims may transfer their trust to algorithms perceived as more objective and impartial. The primary objective of this paper is to investigate whether consumer fraud victimization increases the demand for AI financial advice and to examine the roles of digital fluency and eroded trust in financial institutions in this relationship. Using NFCS state-by-state survey data, we contribute to the understanding of how fraud actively drives behavioral adaptation, potentially reversing the well-documented tendency to distrust algorithms (Dietvorst et al., 2015). The findings hold significant implications for researchers, financial planners, and consumer protection advisors.

Accepted Oral Presentation