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Author: 黃彥凱
Yen-Kai Huang
Thesis Title: 探討 P2P 借貸資料分析技術應用於商業銀行之研究
A Study of P2P Lending Data Analysis Technology on Commercial Bank
Advisor: 楊朝龍
Chao-Lung Yang
Committee: 王孔政
Kung-Jeng Wang
陳俊男
Chun-Nan Chen
Degree: 碩士
Master
Department: 管理學院 - 工業管理系
Department of Industrial Management
Thesis Publication Year: 2017
Graduation Academic Year: 105
Language: 中文
Pages: 44
Keywords (in Chinese): P2P 借貸金融科技Techfin機器學習
Keywords (in other languages): P2P Lending, Fintech, Techfin, Machine Learning
Reference times: Clicks: 327Downloads: 1
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P2P 借貸是近年國外金融科技(Fintech)中極為熱門的業務。基本上,P2P 借 貸業者透過平台媒合貸款人及債權人,並提供風險評估。本研究透過分析 Kaggle 網站中數據專家針對 Lending Club 資料集所提供之資料分析方法,進行方法的比 較與探討,並思考國內商業銀行如何利用相關技術來拓展 P2P 借貸之相關業務。 研究發現利用機器學習方法可有效地找出貸款人違約的關鍵因子,並進而有效預 測貸款人違約機率。在商業銀行業務拓展的思考下,可運用 P2P 借貸技術對商業 銀行高風險客戶提供資金貸款服務。


In recent years, P2P (Peer to Peer Lending) has become one the of most popular financial technology (FinTech) business worldwide. P2P lending company is the media that connects the investors and borrowers. They also provide the risk analysis for their customers. This study is to exam the analytic technology from Kaggle - a data science and machine learning website. The statistic experts and data scientists at Kaggle analyzed and cross-exam Lending Club, a typical P2P lending company, that leads the online lending platform. Kaggle’s analysis helps us understand how we can potentially utilize the relevant P2P data analytic technology for Taiwan’s business banks. The study found that the use of machine learning method can effectively identify the key factors of the borrower’s default, and thus effectively predict the probability of lender's risk. Therefore, P2P lending technology can be used to determine the loan services to the high-risk clients in the development of commercial banks.

摘要 i ABSTRACT ii 誌謝 iii 圖目錄 vi 表目錄 vii 第一章 緒論 1 1.1 研究背景與動機 1 1.2 研究目的 3 1.3 研究流程與論文架構 4 第二章 文獻回顧 5 2.1 傳統借貸與金融創新 P2P 借貸模式 5 2.1.1 傳統借貸 5 2.1.2 P2P 借貸 6 2.1.3 傳統借貸與金融創新 P2P 借貸比較 7 2.2 數據分析與機器學習分類方法 8 2.2.1 資料視覺化(Data Visualization) 8 2.2.2 決策樹(Decision Tree) 9 2.2.3 隨機森林(Random Forest) 10 2.2.4 混淆矩陣(Confusion Matrix) 10 第三章 研究方法與討論 12 3.1 資料視覺化分析 Lending Club 資料集 12 3.1.1 清償貸款時間分析 12 3.1.2 借貸目的分析 13 3.1.3 借貸目的與清償還款天數的平均值 13 3.1.4 貸款利息的分布與數量 14 3.2 機器學習方法預測整理 15 3.2.1 A 專家:利用隨機森林(Random Forest)預測還款違約 16 3.2.2 B 專家:利用決策樹(Decision tree)預測借貸目的為信用卡(credit card)、醫療(medical)以及債務(debt)的還款違約 19 3.3 研究方法結論 26 第四章 研究結論與建議 30 4.1 研究結論 30 4.2 研究建議 30 參考文獻 32

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