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.

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@article{dzuyo2026benchmarking,
  title = {Benchmarking Generalization in Financial Statement Fraud Detection: robust evaluation and novel tasks},
  author = {Guy Stephane Waffo Dzuyo and Gaël Guibon and Christophe Cerisara and Luis Belmar-Letelier},
  year = {2026},
  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 },
  url = {https://arxiv.org/abs/2607.19259},
  keywords = {cs.LG, cs.AI},
  eprint = {2607.19259},
  archiveprefix = {arXiv},
}

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