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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/119092


    Title: 機器學習在P2P借貸信用風險模型之應用：以Lending Club為例
    Application of Machine Learning in P2P Lending Credit Risk Model - A Case of Lending Club
    Authors: 陳勃文
    Chen, Po-Wen
    Contributors: 林士貴
    蔡瑞煌
    Lin, Shih-Kuei
    Tsai, Rua-Huan
    陳勃文
    Chen, Po-Wen
    Keywords: P2P借貸
    類神經網路
    羅吉斯迴歸
    信用風險
    違約預測
    P2P lending
    Neural network
    Logistic regression
    Credit risk
    Default predicton
    Date: 2018
    Issue Date: 2018-07-31 13:46:04 (UTC+8)
    Abstract: 本研究使用傳統方法與機器學習方法，建立P2P借貸平台上的貸款違預測模型，並比較各種方法之績效。本研究使用美國最大的P2P借貸平台，即Lending Club所公開之數據庫。本文先就近年針對P2P貸款違約因子的研究做討論，並審視不同因子間的相關性以決定羅吉斯迴歸之自變量，並建立四個依輸入特徵區分之羅吉斯迴歸模型。在機器學習方法中，類神經網路有四個控制變因，為批次訓練量、訓練次數、隱藏層數、隱藏層神經元數，以每次控制一至兩個變因的方法，尋找類神經網路最佳的超參數組。最佳的超參數組合為激勵函數為雙取正切函數(tanh)，批次訓練量為70，隱藏層神經元數為8，隱藏層1層，訓練次數至少要200次以上。最後，將羅吉斯迴歸模型、類神經網路模型及支援向量機模型做比較並將三種模型之預測結果進行統計檢定後，發現類神經網路模型之預測準確率顯著高於另外兩者。
    This study uses traditional methods and machine learning methods to establish a default prediction model of loans on the P2P lending platform, and then compares the performance of various methods. This study uses the database published by Lending Club, which is the largest P2P lending platform in the United States. We first overview the research on P2P loan default factors in recent years, and inspect the correlation between different factors to determine the independent variables of logistic regression. We establish four logistic regression models based on input characteristics. In machine learning method, the neural network has four control variables, which are batch training, training times, hidden layers, neurons of hidden layer. We find the best hyper-parameter group for the network by controlling one or two variables each time. The optimal hyper-parameter combination is to set the activation function as tanh, the batch training amount as 70, the number of neurons of hidden layer as 8, and the hidden layer as 1 layer, and the times of training as 200 times at least. Finally, we compared the logistic regression model, the neural network model and the support vector machine model by doing statistical test and found that the prediction accuracy of the neural network model is significantly higher than the other two.
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    Description: 碩士
    國立政治大學
    金融學系
    105352033
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0105352033
    Data Type: thesis
    DOI: 10.6814/THE.NCCU.MB.024.2018.F06
    Appears in Collections:[Department of Money and Banking] Theses

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