FraudBench is a new benchmark that tests policy‑grounded banking conversational agents against adaptive fraud scenarios. It uses a dual‑control framework and a 698‑document internal policy corpus, presenting 150 adversarial scenarios (107 public, 43 held‑out) that require agents to manage mutable account state and tool access while preventing identity, authorization, and trust manipulation. Preliminary results on four agents show attack‑security rates between 49% and 65%, highlighting weaknesses in money‑mule and first‑party fraud detection.
By Dheeraj Mohandas Pai, Lu Xian
The paper introduces the Agentic Commerce Bench (ACB), a benchmark for measuring fraud in AI agents that autonomously spend money. It presents a taxonomy of agentic commerce fraud, a dataset of twenty fraud classes derived from real production data, and an open‑source detector stack called gordonguard for auditing and replaying hostile counterparties. The study shows that current reasoning layers and security scanners perform poorly on many classes, highlighting the need for better detection mechanisms.
By Ankit Srivastava, Debjyoti Paul
arXiv:2609.34211v2 Announce Type: replace
Abstract: In financial fraud detection, rich semantic context can provide important evidence for transaction behavior modeling and fraud reasoning. However,...
By Linbo Shao, Huilin He, Yating Lou, Dawei Cheng
S$^2$-MAS is a multi‑agent simulation framework that uses 500 million synthetic household personas to model economic responses at society scale. It integrates trainable candidate‑scoring policies, reusable financial scenarios, and explicit state transitions, and has been trained on 2.93 million examples to achieve high accuracy on consumer financial‑choice tasks. Experiments demonstrate the framework’s ability to evaluate millions of households across different purchase‑funding conditions, revealing heterogeneous consumption and repayment patterns driven by policy decisions and accounting rules.
By Yang Li, Yike Wang, Di Wan, Chujun He, Zongsi Xu, Yitao Yu, Shuai Zhong, Minghao Pan, Quanfeng Lu, Xiyu Chen, Sheng Zhong, Deng Binjie, Yichen Guo, Zhenwei Guo, Hou Jun, Kunhan Guo, Weihao Lin, Tuo Zhou, Hai Liu, Chenwei Wu, Jianyuan Zhong, Xilin Dai, Ye Luo
The paper "Financial Fragility in Societies of LLM Agents: Coordination Failures and Stabilizing Mechanisms" investigates how large language model agents can collectively cause financial failures when making individual protective decisions. Using the FRAIL framework, the authors simulate bank runs, debt rollovers, and reward crowdfunding, finding that 77% of bank-run and 83% of debt-rollover episodes fail even without malicious agents. They test three interaction mechanisms—compensated commitments, centralized agreements, and participant-led coalitions—each improving outcomes but none dominating across all scenarios, noting that early broad commitments are key to successful stabilization.
By Zhenhao Fu, Ruipeng Xu, Qibing Ren
SR‑Fraud is a framework that uses a frozen, stateless LLM agent to score transactions in real time while an offline reflection agent proposes boundary hypotheses based on matured errors. The system then verifies these hypotheses deterministically before updating its knowledge state. On a production payment‑fraud benchmark, SR‑Fraud outperforms both static and periodically retrained CatBoost models and successfully detects an emerging fraud burst.
By Xuwei Tan, Yao Ma, Xueru Zhang
arXiv:2607. 19266v1 Announce Type: cross Abstract: Fraud detection systems must scale with rising transaction volume while remaining explainable and reviewable.
By Rahil Sharma
FINESSE is an agent‑based simulation framework that generates synthetic, structured datasets of multiple interdependent financial event streams, such as transactions, payments, account status changes, and policy interventions. Each stream has its own action space, schema, and variable types, and the streams are coupled through agents’ evolving latent states, allowing temporally rich interactions. The accompanying FINESSE‑Bench dataset supports four tasks—balance forecasting, transaction fraud detection, missed payment prediction, and next event prediction—and baseline results are provided using various time‑series and event‑sequence methods.
By Tyler Farnan, Benjamin Eng, Adam Abate, Xirui Hou, Rizal Fathony, Nam H. Nguyen, Senthil Kumar
arXiv:2606. 17555v1 Announce Type: cross Abstract: Banks simultaneously face signature-based fraud (card-not-present attacks, account takeover, ATM cloning) and behavioural financial crime (structuring, layering, mule networks, business email compromise) -- two threat families with fundamentally different detection requirements.
By Joseph Walusimbi, Joshua Benjamin Ssentongo
arXiv:2610.24264v2 Announce Type: replace
Abstract: As LLM-based agents are deployed with increasing autonomy in real-world environments, a critical failure mode emerges: agentic misalignment, where...
By Eric Zhang, Terry Jingchen Zhang, Chijioke Ugwuanyi, Jerick Shi, Bernhard Sch\"olkopf, Zhijing Jin
PAWS is a new dataset for policy-driven agentic world simulation that covers 36 verified U.S. financial and economic policy episodes. It includes 12,727 policy-linked news records and 65,291 stakeholder actions, each linked to supporting news and represented by a multi-layer event frame with interaction mode, financial-action family, semantic attributes, and taxonomic mappings. The dataset aligns actions with daily market-return context and has been validated by AI and human reviewers, demonstrating high agreement on interaction mode and revealing challenges in detecting rare stakeholder actions.
By Tiviatis Sim, Jia Hui Woon, Xinming Gao, Chen Gao, Fengbin Zhu, Zheng Huanhuan, Chua Tat Seng, Kenji Kawaguchi
arXiv:2606. 08146v1 Announce Type: new Abstract: Fraud detection in payment, e-commerce, and telecommunications systems requires accuracy at the individual level, robustness under severe class imbalance, and ease of understanding for risk managers.
By Yichen Chen, Siying Li, Yuhang Liang, Lijun Wang, Renyang Liu