利用人工智能技術建立財報舞弊偵測模型

CCUR > College of Business > Department of Accounting & Graduate Institute of Accounting > Thesis > Item 987654321/51239

Please use this identifier to cite or link to this item: https://irlib.pccu.edu.tw/handle/987654321/51239

Title:	利用人工智能技術建立財報舞弊偵測模型 Construction of Financial Statements Fraud Detection Models by Using Artificial Intelligence Technique
Authors:	陳奕廷
Contributors:	會計學系
Keywords:	人工智能財務報表舞弊決策樹CART 決策樹CHAID 類神經網路卷積神經網路支持向量機 artificial intelligence financial statement fraud decision tree cart decision tree chaid artificial neural network convolutional neural network support vector machine
Date:	2022
Issue Date:	2023-03-01 14:38:11 (UTC+8)
Abstract:	財務報表可以提供使用者了解該企業的財務狀況及經營績效，但有些企業為了利益，出具不實的財務報表來隱匿公司實際經營狀況，因此為預防財務報表舞弊的發生尤為重要。本研究之研究樣本選取自臺灣經濟新報資料庫(Taiwan Economic Journal, TEJ)，並根據財團法人證券投資人暨期貨交易人保護中心所公布之財報不實、公開說明書不實二種求償案件類型，以所發布之重大證券犯罪起訴及判決情況作為研究樣本。觀察資料選取2001年至2021年間之臺灣上市及上櫃公司，並採用17個財務變數，以及8個非財務變數。本研究利用決策樹CART、決策樹CHAID以及類神經網路進行初步篩選重要變數後，再以卷積神經網路(Convolutional Neural Network, CNN)與支持向量機(support vector machine, SVM)來建立有效的財務報表舞弊偵測模型，其中以決策樹CART搭配卷積神經網路具有最佳的預測能力，其測試組準確率最高，達90.21%，並且於型一、型二錯誤率皆為2.86%。 Financial statements make users understand a company's financial status and operating performance. However, some companies issue false financial statements to hide the company's actual operating conditions for the sake of profit. Therefore, it is particularly important to prevent fraud in financial statements. The research samples of is adopted from the Taiwan Economic Journal (TEJ), and based on the two types of compensation cases, the false financial report and the false public statement, announced by the Securities Investor and Futures Trader Protection Center of the Consortium Corporation, The published prosecutions and verdicts of major securities crimes are used as research samples. The sample data is the listed and OTC companies in Taiwan from 2001 to 2021, and 17 financial variables and 8 non-financial variables were used. This study uses decision tree CART, decision tree CHAID and neural network to initially screen important variables, and then uses Convolutional Neural Network (CNN) and support vector machine (SVM) to establish an effective financial statement fraud detection model, in which the decision tree CART combined with convolutional neural network has the best prediction ability, and its test group has the highest accuracy rate of 90.21%, and the error rate of type 1 and type 2 is 2.86 %.
Appears in Collections:	[Department of Accounting & Graduate Institute of Accounting] Thesis

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