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E4a Artificial Intelligence vs. Financial Planning Impact on Financial Stress: What Regression Misses and Machine Learning Reveals
Short Description
This study investigates how artificial intelligence, financial planners, and their combination, are associated with financial stress, and whether these associations vary by household income. Using data from a January 2025 survey of more than 2,000 U.S. public sector employees, the analysis employs both traditional regression models and machine learning–based causal inference methods to assess how methodological approaches influence conclusions about advice effectiveness. Results reveal that AI tools are most strongly associated with reductions in financial stress among lower-income households, lowering the probability of reporting stress by 23 percentage points among those earning under $50,000. The combination of AI and human advisors produces the most consistent benefits across income groups, while financial planner–only advice shows limited effectiveness once selection is addressed. These findings demonstrate that advice effectiveness is both source-dependent and income-contingent, with important implications for consumer financial well-being. The study contributes to consumer research by showing how methodological choices shape policy conclusions about financial advice. It also integrates self-determination theory with consumer psychology to explain differential outcomes across income strata, highlighting opportunities to democratize financial guidance while optimizing advisor-client relationships.
Type of presentation
Accepted Oral Presentation