这篇论文解决了财务报表欺诈检测的泛化评估问题，用LLM整合文本数据，在CI-FSFD任务上达到SOTA，适合关注AI金融应用和风险检测的研究者。
本文提出一个稳健的财务报表欺诈检测(FSFD)框架，利用大语言模型(LLMs)整合结构化财务数据与财务报告中的非结构化文本。研究人员指出现有方法依赖随机数据划分，导致性能估计过于乐观，不能反映对新公司或未来时期的泛化能力。他们构建了一个新的基准任务CI-FSFD(Company-Isolated FSFD)，并发布了包含美国公司财务报表、MD&A文本摘要和欺诈标签的数据集。在CI-FSFD任务上，该方法取得了最佳性能，验证了文本数据和鲁棒评估在可靠财务欺诈检测中的关键价值。
Benchmarking Generalization in Financial Statement Fraud Detection: robust evaluation and novel tasks
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.