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

• URL: http://arxiv.org/abs/2607.19259v1
• Date: Tue, 21 Jul 2026 16:32:54 GMT
• Title: Benchmarking Generalization in Financial Statement Fraud Detection: robust evaluation and novel tasks
• Abstract summary: We propose a robust FSFD framework leveraging Large Language Models (LLMs) to integrate structured financial data and unstructured textual information from financial reports.<n>We construct and make publicly available a comprehensive U.S. company dataset combining financial statements, summarized MD&A text, and fraud labels.<n>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.
• Score: 7.094237287509504
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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