用大模型融合财报数据与文本，提升财务造假检测泛化能力
Benchmarking Generalization in Financial Statement Fraud Detection: robust evaluation and novel tasks
- 结合结构化数据与摘要文本，用大模型统一建模
- 新基准任务下准确率显著优于现有方法
- 适合金融风控、审计与AI合规研究者参考
财务报表欺诈检测对维护市场诚信至关重要，但面临欺诈手法日益复杂、文本信息利用不足等问题。现有方法多依赖随机数据划分，导致性能评估过于乐观，无法反映真实场景中对新公司或未来时期的有效泛化。为此，我们提出一个基于大语言模型的稳健框架，整合财务报表的结构化数据与管理层讨论与分析（MD&A）的非结构化文本。构建并公开了一个涵盖美国上市公司财务报表、摘要文本及欺诈标签的综合性数据集。设计了更具挑战性的公司隔离式欺诈检测任务（CI-FSFD），实现该任务下的最佳表现，验证了文本信息与鲁棒评估在可靠欺诈检测中的关键价值。
原文摘要 · Abstract (English)
Financial statement fraud detection (FSFD) is crucial for market integrity but faces challenges from increasingly sophisticated schemes and under-utilized textual data in financial reports. Existing methods often rely on random data splits, leading to overoptimistic performance estimates that do not reflect real-world generalization to new companies or future periods. To address this recurring problem with the state of the art, we propose a robust FSFD framework leveraging Large Language Models (LLMs) to integrate both structured financial data and unstructured textual information from financial reports. We provide a more realistic evaluation through a novel and challenging benchmark task called Company-Isolated FSFD (CI-FSFD). We construct and make publicly available a comprehensive U.S. company dataset combining financial statements, summarized MD&A text, and fraud labels. Our approach achieves the best performance on the challenging CI-FSFD task, demonstrating the critical value of textual data and robust evaluation for reliable financial fraud detection.
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