行为锚定的语义增强用于金融欺诈建模与推理

中文摘要 AI 辅助

在金融欺诈检测中，丰富的语义上下文可以为交易行为建模和欺诈推理提供重要证据。然而，由于隐私限制，公开的真实世界金融数据集往往缺乏丰富的语义。因此，合成数据集融入了生成的语义，但以牺牲行为真实性为代价；用于上下文推理的文本描述仍然稀缺。我们通过一个基于原始交易行为锚定的语义增强框架来弥合这一差距，以模拟多模态金融数据。我们（1）提出了一个多智能体语义增强框架，通过角色专业化智能体和一致性细化，生成基于交易行为的可解释金融语义，以及（2）新贡献了一个有价值的多模态金融欺诈数据集MS-FFSD，该数据集丰富了结构化语义和文本语义，同时保留了基于真实数据的交易行为。此外，我们系统分析了语义增强的质量和实用性。结果表明统计保真度和框架泛化性，同时表明更丰富的语义有利于欺诈建模和上下文感知的LLM推理。总体而言，这项工作推进了多模态金融欺诈研究，并将新兴的LLM和多智能体能力与运营反欺诈实践相结合。框架和数据集已在此https URL发布。

英文摘要

In financial fraud detection, rich semantic context can provide important evidence for transaction behavior modeling and fraud reasoning. However, public real-world financial datasets often lack rich semantics due to privacy constraints. Consequently, synthetic datasets incorporate generated semantics, but at the cost of behavioral realism; textual descriptions for contextual reasoning remain scarce. We address this gap through a semantic enrichment framework grounded in original transaction behavior to simulate multimodal financial data. We (1) propose a multi-agent semantic enrichment framework that generates interpretable financial semantics grounded in transaction behavior through role-specialized agents and consistency refinement, and (2) newly contribute a valuable multimodal financial fraud dataset, MS-FFSD, enriched with structured semantics and textual semantics while preserving real-data-grounded transaction behavior. Furthermore, we systematically analyze the quality and utility of semantic enrichment. Results demonstrate statistical fidelity and framework generalizability, while showing that richer semantics benefit fraud modeling and context-aware LLM reasoning. Overall, this work advances multimodal financial fraud research and bridges emerging LLM and multi-agent capabilities with operational anti-fraud practice. The framework and dataset are released at https://github.com/AI4Risk/MS-FFSD.

发表机构

• Tongji University（同济大学）

机构由 AI 辅助整理，请以论文原文为准。