FraudSociety: A Generative Agent-Based Society for the Financial-Fraud Economy

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

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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.

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Artificial Intelligence (cs.AI) ; Computers and Society (cs.CY)

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arXiv:2610.33536 [cs.AI]

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arXiv:2610.33536v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.33536

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arXiv-issued DOI via DataCite (pending registration)

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From: Mingxuan Jiang [ view email ]

[v1]

Sat, 24 Oct 2026 08:26:22 UTC (2,613 KB)

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