利用深度學習建立財務報表舞弊偵測模型

CCUR > College of Business > Department of Accounting & Graduate Institute of Accounting > Thesis > Item 987654321/51201

Please use this identifier to cite or link to this item: https://irlib.pccu.edu.tw/handle/987654321/51201

Title:	利用深度學習建立財務報表舞弊偵測模型 Financial Statement Fraud Detection Models Using Deep Learning
Authors:	邱冠勳
Contributors:	會計學系
Keywords:	深度學習財務報表舞弊類神經網路長短期記憶模型卷積神經網路 deep learning financial statement fraud artificial neural network long short-term memory model convolutional neural network
Date:	2021
Issue Date:	2023-02-25 12:53:17 (UTC+8)
Abstract:	財務報表舞弊欺騙了財務報表的使用者，也會造成投資人重大的損失，及時且有效的偵測財務報表舞弊是相當重要的。本研究以 2000 年至2019 發生財務報表舞弊之上市櫃公司為主要研究對象，先使用隨機森林(random forest)來進行重要變數的篩選，再以類神經網路(artificial neural network)、長短期記憶模型(long short-term memory model)與卷積神經網路(convolutional neural network)分別來建立偵測模型。實證結果顯示，由隨機森林篩選重要變數加上卷積神經網路所建立之模型(RF-CNN)有最佳的偵測能力，其整體準確率達95.66%； F1 分數達91.67%；接收者操作特徵曲線之曲線下面積達99.75%。 Fraud in financial statements deceives the users of financial statements and can also cause great losses to investors. It is very important to detect fraud in financial statements timely and effectively. This study focuses on Taiwan listed companies with fraudulent financial statements from 2000 to 2019 as the research object. First, the random forest is used to screen important variables, and then the artificial neural network, long short-term memory model and convolutional neural network are used to establish the detection models separately. The empirical results show that the RF-CNN model has the best detection ability, with an overall accuracy rate of 95.66%; F1 score of 91.67%; area under the receiver operating characteristic curve of 99.75%.
Appears in Collections:	[Department of Accounting & Graduate Institute of Accounting] Thesis

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