Beyond Trust: Critical Evaluation of AI-Generated Financial Advice in Consumer Financial Decision-Making
Abstract
Artificial intelligence is increasingly being used to provide personalised financial information and recommendations through robo-advisors, digital financial platforms and generative AI-based systems. Recent research has examined consumer engagement with AI-enabled financial advice through trust, adoption, financial literacy, AI literacy, personalization, explainability and algorithmic reliance. However, these perspectives provide only a partial explanation of what occurs between receiving a specific AI-generated financial recommendation and deciding whether to act upon it. While financial and AI literacy concern knowledge and understanding, explainability relates to the communication of system outputs, trust reflects confidence in AI, and reliance concerns the behavioural decision to follow advice. The recent literature does not clearly establish an integrated conceptual account of the consumer evaluative process through which AI-generated financial advice is scrutinized for its relevance, risk, uncertainty, assumptions and suitability before being accepted, modified, verified or rejected. This issue is particularly significant because increased confidence in AI-enabled financial advice does not necessarily imply improved financial decision quality. Addressing this gap, the present conceptual paper develops a framework explaining how consumers critically evaluate AI-generated financial advice before incorporating it into financial decisions. The proposed framework positions critical evaluation as the process linking advice exposure with subsequent consumer response and distinguishes it from AI literacy, financial literacy, trust, explainability and algorithmic reliance. The study contributes by shifting attention beyond whether consumers trust or adopt AI towards how they assess specific AI-generated recommendations before acting upon them. The framework provides a foundation for future empirical research on consumer evaluation, calibrated reliance and responsible AI-enabled financial decision-making.