FSFDLLM: Financial Statement Fraud Detection Aided by Large Language Models

Financial reporting fraud poses a considerable threat to financial market stability. Traditional detection methods predominantly rely on quantitative data analysis while neglecting the informational value of textual disclosures, and existing text analysis approaches suffer from 3 critical limitations: inadequate interpretability, poor domain adaptability, and absence of temporal continuity modeling. This paper proposes the financial statement fraud detection large language model (FSFDLLM) framework, which leverages large language models (LLMs) to achieve in-depth textual feature extraction and interpretable reasoning. Our methodology addresses 3 key challenges: (a) Specifically, we develop a structured prompting mechanism to guide the LLM in conducting the fraud risk assessment of financial texts while generating explanatory rationales. (b) We construct a hybrid learning architecture that integrates LLM-generated pseudo-labels and rationale chains as enhanced features with structured financial data for joint classification model training. (c) The framework has 3 distinctive features: First, it overcomes the domain transfer bottleneck in conventional natural language processing (NLP) models by harnessing the LLM’s semantic comprehension to accurately capture domain-specific financial terminology. Second, it establishes an auditable decision evidence chain through LLM-generated explanatory rationales, thereby enhancing regulatory compliance. Third, it captures continuous fraud patterns by modeling temporal financial indicators and inter-period textual semantic correlations. Experimental evaluations on a real-world listed company dataset demonstrate that our framework considerably outperforms baseline models in key metrics including F1-score and area under the receiver operating characteristic curve (AUC), while the generated explanatory text provides verifiable semantic evidence for regulatory scrutiny. This study presents a cross-modal solution for financial statement fraud detection (FSFD) that harmonizes high accuracy with interpretability, offering practical value for enhancing financial regulatory technology tools.