When Personalization Becomes Bias: Structural and Discursive Religious Framing in AI-Generated Financial Advice
Large language models (LLMs) are increasingly integrated into financial advisory systems, yet their role in reproducing religious bias remains underexamined. This study provides systematic mixed-methods evidence of such bias across three LLMs (ChatGPT, Gemini, and Grok) using 432 simulated advisor-client interactions spanning 16 religious identity pairings (Christian, Muslim, Hindu, and non-religious) and three core household financial decisions: stock investment, house purchase, and life insurance. Combining regression and reflexive thematic analyses, we identify structural biases across models and decision contexts and the discursive mechanisms through which they are linguistically enacted. Unbiased advice appeared in only 12-18% of cases. Gemini consistently produced more bias than Grok, while ChatGPT's outputs were statistically comparable to Grok's. Religiously symmetric advisor-client pairings almost always triggered explicit religious framing, and non-religious clients often received advisor-centered religious appeals. Qualitative findings show that bias is linguistically manifested through religious anchoring, uneven cultural signaling, and tone modulation, varying by model and financial scenario. Stock investment prompts produced more financially technical responses, whereas life insurance advice triggered stronger religious language. The study develops a dual-dimensional framework linking structural bias rooted in model training and design with discursive bias expressed through language, advancing understanding of algorithmic bias in LLM-generated financial advice. It also shows that such advice adapts linguistically to identity cues, revealing a managerial dilemma between personalization and neutrality. Finally, it highlights implications for businesses, financial institutions, and regulators seeking to ensure neutrality, cultural sensitivity, and trust in AI-mediated advice.