| dc.contributor.advisor | 周德宇<br>韓幸紋 | zh_TW |
| dc.contributor.advisor | Zhou, Te-Yu<br>Han, Hsing-Wen | en_US |
| dc.contributor.author (Authors) | 莊家明 | zh_TW |
| dc.contributor.author (Authors) | Jhuang, Jia-Ming | en_US |
| dc.creator (作者) | 莊家明 | zh_TW |
| dc.creator (作者) | Jhuang, Jia-Ming | en_US |
| dc.date (日期) | 2022 | en_US |
| dc.date.accessioned | 2-Sep-2022 15:30:57 (UTC+8) | - |
| dc.date.available | 2-Sep-2022 15:30:57 (UTC+8) | - |
| dc.date.issued (上傳時間) | 2-Sep-2022 15:30:57 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0109255024 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/141762 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 財政學系 | zh_TW |
| dc.description (描述) | 109255024 | zh_TW |
| dc.description.abstract (摘要) | 租稅為國家重要財政收入來源，納稅義務人的逃漏稅行為不僅減損國家財政收入，更間接影響租稅的公平性及企業競爭。過往查稅人員多以經驗法則選案進行查核，在稽核效率上略顯不足，今年起政府已經引進AI查找營業稅逃漏，本研究為印證深度學習是否相對傳統的線性模型分析，更能夠準確預測廠商逃漏稅的情形，故使用深度學習中的隨機森林模型以及經由PCA主成分分析篩選過後的變數執行OLS線性分析，比較兩者的預測準確程度，亦利用MDS降維模型將變數分布降維至三維立體空間，觀察有無逃漏稅的廠商資料樣本的可視化分布情形，綜合以上方式找出營業稅逃漏的關鍵變數，確認深度學習的工具確實有助於查稅人員執行查找逃漏稅案件。本研究結果顯示，非線性分析相較於線性分析會更加準確，且透過視覺化的立體圖形也能引導查稅人員在找異常值時，能發現更多的資訊納入查找關鍵因子的考量。本篇研究作為AI深度學習應用於查找營業稅查漏上的初探，未來還有非常大的精進空間。 | zh_TW |
| dc.description.abstract (摘要) | Tax is one of the most important source of national fiscal revenue. Tax evasion not only diminish national fiscal revenue, but also indirectly affect the fairness of taxation among tax payers and even interfere with industrial competition. In the past, tax inspectors followed rules of thumb to select cases to detect tax fraud with no clear guidance toward audit efficiency. Since 2021, the government has incorporated AI to detect business tax evasion. This study is to investigate whether deep learning is more accurate than traditional linear model analysis in the aforementioned tasks. To predict the tax evasion of in the manufacturing sector, the random forest model in deep learning and the variables filtered by PCA principal component analysis were used to perform OLS linear analysis to compare the prediction accuracy. The MDS dimensionality reduction model was also used to reduce the dimensionality of the variable distribution. By using the three-dimensional space, we present the visual distribution of the data samples of manufacturers with or without tax evasion labels. We that identify the key variables of business tax evasion based on the above methods, and confirm that the tools of deep learning indeed provide clear guidance for tax inspectors to search for tax evasion cases.The results of this study show that non-linear analysis is more accurate than linear analysis, and the visual three-dimensional graphics can also guide tax inspectors to develop more intuition when looking for outliers to be considered for tax fraudulent behavior . This research is by no mean a preliminary and explorative study on the application of AI deep learning to understand business tax evasion, and there are still more threads of research to be pursued in the future. | en_US |
| dc.description.tableofcontents | 第一章 緒論 1第一節 研究動機及背景 1第二節 研究流程 4第二章 文獻回顧 6第一節 營業稅制度介紹 6第二節 營業稅逃漏 8第三節 AI大數據應用於營業稅逃漏 11第三章 研究方法 19第一節 資料來源 19第二節 研究方法及設計 21第三節 資料樣本處理與分析 28第四章 研究結果 30第一節 PCA模型 30第二節 OLS模型 32第三節 隨機森林模型 36第四節 OLS模型及RF模型預測能力比較 42第五節 MDS 立體圖形 45第五章 結論 46第六章 研究限制 48參考文獻 49附錄 52 | zh_TW |
| dc.format.extent | 2860537 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0109255024 | en_US |
| dc.subject (關鍵詞) | 營業稅逃漏 | zh_TW |
| dc.subject (關鍵詞) | 深度學習 | zh_TW |
| dc.subject (關鍵詞) | PCA主成分分析 | zh_TW |
| dc.subject (關鍵詞) | MDS降維 | zh_TW |
| dc.subject (關鍵詞) | 隨機森林 | zh_TW |
| dc.subject (關鍵詞) | Business tax evasion | en_US |
| dc.subject (關鍵詞) | Deep learning | en_US |
| dc.subject (關鍵詞) | PCA principal component analysis | en_US |
| dc.subject (關鍵詞) | MDS dimensionality reduction | en_US |
| dc.subject (關鍵詞) | Random forest | en_US |
| dc.title (題名) | 營業稅稽查關鍵因子：基於AI深度學習的探索 | zh_TW |
| dc.title (題名) | Detecting Business Tax Evasion: An AI Deep Learning Explorative Study | en_US |
| dc.type (資料類型) | thesis | en_US |
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| dc.identifier.doi (DOI) | 10.6814/NCCU202201416 | en_US |
