政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/136030

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/136030

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | 全文筆數/總筆數 : 113313/144292 (79%)
造訪人次 : 50945402 線上人數 : 946

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

搜尋範圍

查詢小技巧：

您可在西文檢索詞彙前後加上"雙引號"，以獲取較精準的檢索結果

若欲以作者姓名搜尋，建議至進階搜尋限定作者欄位，可獲得較完整資料

進階搜尋

主頁 ‧ 登入 ‧ 上傳 ‧ 說明 ‧ 關於政大典藏 ‧ 管理

到手機版

政大機構典藏 > 商學院 > 企業管理研究所（MBA學位學程） > 學位論文 > Item 140.119/136030

請使用永久網址來引用或連結此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/136030

題名:	信用卡盜刷模型偵測：分別以類神經網路及支援向量機之模型成效比較 Credit card fraud model detection：Comparison of the model effectiveness of neural network and support vector machine
作者:	陳宇慈
貢獻者:	蔡炎龍周冠男陳宇慈
關鍵詞:	深度學習信用卡盜刷類神經網路異常偵測資料不平衡資料降維 Deep Learning Credit Card Fraud Neural Network Anomaly Detection Data Imbalance Dimension Reduction
日期:	2021
上傳時間:	2021-07-01 21:38:01 (UTC+8)
摘要:	在現今的時代中，網路購物、線上購物已為消費者進行購物的主要管道。而在付款方式的選擇上，「信用卡支付」又相較於「超商取貨付款」，多了更多的便利性。加上銀行業者、以及電商業者時常會提供信用卡付款優惠當成誘因，吸引消費者使用信用卡付款。但是，任何事物有正面也有反面，而信用卡所帶來的便利的背後即是盜刷的風險。目前信用卡盜刷主要可以分成三種盜刷形式，依序分別為偽冒申請、盜刷、偽卡交易，依照台灣財團法人聯合信用卡中心統計數字顯示，2018年信用卡盜刷金額高達23.59億元。而在研究流程的部分，為了研究模型準確度的保證，本研究先透過合成少數類過取樣技術 (SMOTE) 演算法將原始資料集進行資料類別不平衡的處理，接著將已處理好的數據透過全連結神經網路進行信用卡盜刷模型的建立，另外，也以三種方法進行降維模型的設計，三種方法分別是使用主成分分析 (Principal components analysis, PCA)、全連結神經網路(NN)、以及函數式API (Function API)，最後在模型成效評估時則以支援向量機（Support Vector Machine）作為分類模型的建置，最後則是透過混合矩陣來評估模型分類的效果。而從實證結果中我們可以發現，以類神經網絡來建立信用卡盜刷模型的模型準確度表現是最好的。 In this era, online shopping has become the main channels for consumers to shop. When it comes to the choice of payment methods, "Credit Card " is more convenient than "convenient store Pickup and Payment". In addition, banks and e-commerce companies often offer credit card payment discounts as an incentive to attract consumers to pay with credit cards. However, everything has its pros and cons, and behind the convenience brought by credit cards is the risk of fraud. At present, credit card fraud can be divided into three types of fraudulent use, which are counterfeit applications, fraudulent use, and counterfeit card transactions. According to statistics from the Taiwan Consortium Credit Card Center, the amount of fraudulent use of credit cards in 2018 was as high as 2.359 billion NTD. In the research process, in order to ensure the accuracy of the research model, in this research, first uses the Synthetic Minority Oversampling Technology (SMOTE) algorithm to deal with the data imbalance, and then the processed data is passed through the fully connected neural network, which is used to build the credit card fraud model. In addition, three methods are used to build the dimensionality reduction model. The three methods are the use of principal components analysis (PCA) and the fully connected neural network (NN). And functional API (Function API). In the end of the research process, support vector machine (Support Vector Machine) is used as the construction of the classification model in the evaluation of the model effect. The effect of the model classification is evaluated through the mixing matrix. From the final results, we can find that the accuracy of the model that uses a neural network to build a credit card fraud model is the best.
參考文獻:	Agarwal, A., El-Ghazawi, T., El-Askary, H., & Le-Moigne, J. (2007, December 1). Efficient Hierarchical-PCA Dimension Reduction for Hyperspectral Imagery. https://doi.org/10.1109/ISSPIT.2007.4458191 ÇAVDAR, İ., & FARYAD, V. (2019). New Design of a Supervised Energy Disaggregation Model Based on the Deep Neural Network for a Smart Grid. Energies, 12(7), 1217. https://doi.org/10.3390/en12071217 Chen, C., Leu, J., & Prakosa, S. W. (2018, April 1). Using autoencoder to facilitate information retention for data dimension reduction. https://doi.org/10.1109/IGBSG.2018.8393545 陳昇瑋文字作者 Chenshengwei, & Wen, Y. (2019). 人工智慧在台灣 : 產業轉型的契機與挑戰 = AI Taiwan / Ren gong zhi hui zai tai wan : chan ye zhuan xing de qi ji yu tiao zhan = AI Taiwan. 天下雜誌股份有限公司, 大和圖書有限公司 Tai Bei Shi: Tian Xia Za Zhi Gu Fen You Xian Gong Si, [Xin Bei Shi. Ghosh, & Reilly. (1994, January 1). Credit card fraud detection with a neural-network. https://doi.org/10.1109/HICSS.1994.323314 Hu, C., Hou, X., & Lu, Y. (2014). Improving the Architecture of an Autoencoder for Dimension Reduction. 2014 IEEE 11th Intl Conf on Ubiquitous Intelligence and Computing and 2014 IEEE 11th Intl Conf on Autonomic and Trusted Computing and 2014 IEEE 14th Intl Conf on Scalable Computing and Communications and Its Associated Workshops. https://doi.org/10.1109/uic-atc-scalcom.2014.50 Ibrahim, M. F. I., & Al-Jumaily, A. A. (2016, December 1). PCA indexing based feature learning and feature selection. https://doi.org/10.1109/CIBEC.2016.7836122 Jain, V., Agrawal, M., & Kumar, A. (2020, June 1). Performance Analysis of Machine Learning Algorithms in Credit Cards Fraud Detection. https://doi.org/10.1109/ICRITO48877.2020.9197762 Kazemi, Z., & Zarrabi, H. (2017, December 1). Using deep networks for fraud detection in the credit card transactions. https://doi.org/10.1109/KBEI.2017.8324876 Khatri, S., Arora, A., & Agrawal, A. P. (2020, January 1). Supervised Machine Learning Algorithms for Credit Card Fraud Detection: A Comparison. https://doi.org/10.1109/Confluence47617.2020.9057851 Li, J., Fong, S., & Zhuang, Y. (2015, December 1). Optimizing SMOTE by Metaheuristics with Neural Network and Decision Tree. https://doi.org/10.1109/ISCBI.2015.12 Malini, N., & Pushpa, M. (2017). Analysis on credit card fraud identification techniques based on KNN and outlier detection. 2017 Third International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB). https://doi.org/10.1109/aeeicb.2017.7972424 Mittal, S., & Tyagi, S. (2019, January 1). Performance Evaluation of Machine Learning Algorithms for Credit Card Fraud Detection. https://doi.org/10.1109/CONFLUENCE.2019.8776925 Srivastava, A., Yadav, M., Basu, S., Salunkhe, S., & Shabad, M. (2016, March 1). Credit card fraud detection at merchant side using neural networks. Retrieved October 23, 2020, from IEEE Xplore website: https://ieeexplore.ieee.org/document/7724348/ 塚本邦尊, 文字作者 Bangzun Zhongben, Dianyi Shantian, Wenxiao Daze, & Yongyu Zhuang. (2020). 東京大學資料科學家養成全書 : 使用Python動手學習資料分析 = 東京大学のデータサイエンティスト育成講座 : 東京大学のデータサイエンティスト育成講座 / Dong jing da xue zi liao ke xue jia yang cheng quan shu : shi yongPython dong shou xue xi zi liao fen xi = dong jing da xue no de - ta sa i e nn te i su to yu cheng jiang zuo : dong jing da xue no de - ta sa i e nn te i su to yu cheng jiang zuo. 臉譜出版: 英屬蓋曼群島商家庭傳媒股份有限公司城邦分公司發行, Tai Bei Shi.
描述:	碩士國立政治大學企業管理研究所(MBA學位學程) 108363092
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0108363092
資料類型:	thesis
DOI:	10.6814/NCCU202100493
顯示於類別:	[企業管理研究所（MBA學位學程）] 學位論文

文件中的檔案:

檔案	大小	格式	瀏覽次數
309201.pdf	4586Kb	Adobe PDF2	0	檢視/開啟

在政大典藏中所有的資料項目都受到原著作權保護.

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

DSpace Software Copyright © 2002-2004 MIT & Hewlett-Packard / Enhanced by NTU Library IR team Copyright © - 回饋