arXiv cs.AI· Keyang Chen, Mingxuan Jiang, Zeping Li, Yiwen Zhao, Yifan Wang, Qiuyang Zhang, Shuaicheng Qi, Yicun Liu, Zhixin Li, Sen Liu, Yinan Jing, Guangnan Ye, Hongfeng Chai·· 2 小时前AI 评分36
FraudSociety：面向金融诈骗经济的生成式智能体社会模拟器
FraudSociety: A Generative Agent-Based Society for the Financial-Fraud Economy
AI 导读
研究者推出 FraudSociety，首个基于 LLM 智能体的金融诈骗联合模拟器，同时建模信息传播与金融账本两类动态，并以 Need-Intent-Action 架构驱动平民决策、诈骗者随交互历史自适应。模拟结果在宏观上贴近真实统计，微观上重现损失集中度——前 10% 受害记录占总损失 81.1%；反诈实验中最优个性化干预将重复付款率从 49.4% 降至 10.9%。
正文 · 原文
Authors:Keyang Chen, Mingxuan Jiang, Zeping Li, Yiwen Zhao, Yifan Wang, Qiuyang Zhang, Shuaicheng Qi, Yicun Liu, Zhixin Li, Sen Liu, Yinan Jing, Guangnan Ye, Hongfeng Chai
Abstract:Financial fraud caused over \$15.9 billion in losses in the United States in 2025, according to the Federal Trade Commission~\citep{ftc2026databook}. Understanding such large-scale harm requires jointly modeling informational and financial dynamics. Yet existing simulators typically capture only one side of this interaction, leaving a gap in modeling how fraud unfolds in the real world. To bridge this gap, we introduce \textsc{FraudSociety}, the first LLM-agent-based simulator that jointly models these coupled dynamics. It integrates a dynamic social environment for information exposure and communication, persistent financial ledgers that preserve payment and loss histories, and a Need-Intent-Action architecture for civilian decision making. Fraudsters further adapt their communication and financial actions to individual interaction histories and ledger states, forming a closed feedback loop between the two dynamics. We validate \textsc{FraudSociety} across macro-level outcomes, micro-level behavior, and intervention effects. At the macro level, the simulated fraud outcomes closely match real-world patterns across multiple key statistics. At the micro level, it reproduces strong loss concentration, with the top 10\% of victimization records accounting for 81.1\% of total losses, consistent with Canadian Anti-Fraud Centre statistics~\citep{cafc2025annual}. Counter-fraud experiments further reveal variation across intervention strategies, with the best-performing personalized intervention reducing the repeat-payment rate from 49.4\% to 10.9\%. These results establish \textsc{FraudSociety} as both a high-fidelity simulator and a scalable testbed for evaluating anti-fraud strategies under diverse intervention conditions, thereby advancing research on financial fraud and its prevention.
| Subjects: | Artificial Intelligence (cs.AI); Computers and Society (cs.CY) |
| Cite as: | arXiv:2610.33536 [cs.AI] |
| (or arXiv:2610.33536v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.33536 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Mingxuan Jiang [view email]
[v1]
Sat, 24 Oct 2026 08:26:22 UTC (2,613 KB)
来源：arXiv cs.AI · arxiv.org