Benchmarking Generalization in Financial Statement Fraud Detection: robust evaluation and novel tasks

Abstract: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.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2607.19259 [cs.LG]

(or arXiv:2607.19259v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.19259 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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