大模型在反欺诈中表现优于人类，且不受投资者偏见影响。
Large Language Models Outperform Humans in Fraud Detection and Resistance to Motivated Investor Pressure
- 用人类反馈训练的大模型在投资建议中保持稳定警示。
- 0%的模型会误荐诈骗项目，人类则有13-14%的误判率。
- 适合关注金融风控与AI决策可信性的研究者阅读。
我们通过一项预先注册的实验，在七种主流大语言模型和十二个投资情境下测试了其反欺诈能力，涵盖合法、高风险及明确欺诈机会，结合3,360次AI咨询对话与1,201名参与者的真人基准数据。结果显示，尽管投资者已有偏见，大模型并未减少欺诈警告；相反，警告率略有上升。在超过1,000次观察中，仅少于3次出现观点反转。人类顾问在无压力时误荐欺诈项目比例为13-14%，而所有大模型均为0%。面对压力，人类抑制警告的频率是大模型的两到四倍。当前大模型在相同咨询角色中提供的欺诈预警比普通人更一致。
原文摘要 · Abstract (English)
Large language models trained on human feedback may suppress fraud warnings when investors arrive already persuaded of a fraudulent opportunity. We tested this in a preregistered experiment across seven leading LLMs and twelve investment scenarios covering legitimate, high-risk, and objectively fraudulent opportunities, combining 3,360 AI advisory conversations with a 1,201-participant human benchmark. Contrary to predictions, motivated investor framing did not suppress AI fraud warnings; if anything, it marginally increased them. Endorsement reversal occurred in fewer than 3 in 1,000 observations. Human advisors endorsed fraudulent investments at baseline rates of 13-14%, versus 0% across all LLMs, and suppressed warnings under pressure at two to four times the AI rate. AI systems currently provide more consistent fraud warnings than lay humans in an identical advisory role.
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