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| Title: | LLM提示工程與查核報告能否提升財報舞弊偵測？
Does LLM Prompt Engineering and Audit Report Embedding Improve Financial Fraud Detection? |
| Authors: | 張永愛
Chang, Yung-Ai |
| Contributors: | 莊皓鈞
周彥君
Chuang, Hao-Chun
Chou, Yen-Chun
張永愛
Chang, Yung-Ai |
| Keywords: | 財報舞弊偵測
查核報告嵌入
提示工程
BERT
SBERT
孤立森林
SHAP values
Financial Statement Fraud Detection
Auditor Report Embedding
Prompt Engineering
BERT
SBERT
Isolation Forest
SHAP Values |
| Date: | 2025 |
| Issue Date: | 2025-09-01 15:05:34 (UTC+8) |
| Abstract: | 本研究探討結合大型語言模型（Large Language Models, LLM）提示工程與會計師查核報告嵌入（embedding）是否能提升財報舞弊偵測的效果。相較於過往僅使用數值型財務與非財務指標進行分析，本研究納入文字型內容，透過 ChatGPT-4o 提取與舞弊風險高度相關的五大語意構面與關鍵字，並結合 BERT 與 Sentence-BERT 等語言模型進行語意向量化，建立具語意辨識能力的文字型指標。
實證資料涵蓋台灣上市、上櫃、興櫃與創新版等公司，舞弊樣本由投保中心公布之「財報不實」訴訟案件中選取，正常樣本則依相同產業與時間配對。分析方法採用無監督學習之孤立森林（Isolation Forest，IF）進行異常偵測，並結合 SHAP values 提升模型可解釋性。
研究結果顯示，納入文字型指標能有效提升舞弊偵測之敏感度與精確性，特別是在採樣平衡情境下，「關鍵查核事項＋年分」模型之真陽性數為全指標模型的兩倍，偽陽性亦較少。此外，SBERT 雖能提升召回率，但相對於 BERT 模型，其誤判數亦較多，顯示需視應用情境權衡選擇。本研究證實查核報告中語意訊號對舞弊風險具有高度辨識力，並提供監理機構與企業一套具備實務可行性的早期預警方法。
This study explores whether integrating prompt engineering with large language models (LLMs) and auditor report embeddings can enhance the detection of financial statement fraud. Unlike previous approaches that relied solely on numerical financial and non-financial indicators, this research incorporates textual data by extracting five key semantic dimensions and associated keywords related to fraud risk using ChatGPT-4o. These textual features are then vectorized using language models such as BERT and Sentence-BERT to create semantically meaningful indicators.
The empirical data covers companies listed on the Taiwan Stock Exchange, OTC (Over-the-Counter), Emerging Stock Board, and the Innovation Board. Fraudulent samples are selected from financial misstatement litigation cases disclosed by the Securities and Futures Investors Protection Center. Normal samples are matched based on industry and reporting period. The analysis employs an unsupervised anomaly detection method, Isolation Forest (IF), and incorporates SHAP values to enhance model interpretability.
The results show that incorporating textual indicators significantly improves the sensitivity and precision of fraud detection. In particular, under balanced sampling conditions, the "Key Audit Matters + Year" model identified twice as many true positives and fewer false positives compared to the full-feature model. While SBERT improved recall rates, it also resulted in more false positives than the BERT-based model, suggesting a trade-off depending on application context. This study confirms that semantic signals within auditor reports are highly indicative of fraud risk and offers a practical early warning framework for regulators and companies. |
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| Description: | 碩士
國立政治大學
資訊管理學系
112356037 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0112356037 |
| Data Type: | thesis |
| Appears in Collections: | [資訊管理學系] 學位論文
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