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 financi
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- PossiblePossibly related (embedding) · 52%Building an AI loan document intelligence platform — shipped fraud detection, hybrid search, and 3-tier email fallback this week[P] →
- LinkedLinked via arxiv author · 85%Guy Stephane Waffo Dzuyo →
“Benchmarking Generalization in Financial Statement Fraud Detection: robust evaluation and novel tasks”
- LinkedLinked via arxiv author · 85%Gaël Guibon →
“Benchmarking Generalization in Financial Statement Fraud Detection: robust evaluation and novel tasks”
- LinkedLinked via arxiv author · 85%Christophe Cerisara →
“Benchmarking Generalization in Financial Statement Fraud Detection: robust evaluation and novel tasks”
- LinkedLinked via arxiv author · 85%Luis Belmar-Letelier →
“Benchmarking Generalization in Financial Statement Fraud Detection: robust evaluation and novel tasks”