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    题名: 神經網路與機器學習方法建構P2P信用評分模型: 以Lending Club為例
    Neural Network and Machine Learning to Construct P2P Lending Credit Score Model: A Case of Lending Club
    作者: 楊立楷
    Yang, Li-Kai
    贡献者: 林士貴
    蔡瑞煌
    楊立楷
    Yang, Li-Kai
    关键词: P2P借貸
    信用評分
    機器學習
    類神經網路
    特徵工程
    Lending Club
    P2P lending
    Credit score
    Machine learning
    Neural network
    Feature engineering
    Lending Club
    日期: 2019
    上传时间: 2019-08-07 16:14:20 (UTC+8)
    摘要: 本研究以不同之神經網路與機器學習方法，包含羅吉斯回歸、支援向量機、決策樹、隨機森林、XGBoost、LightGBM與類神經網路七種，分別建構P2P信用評分模型，並經由交叉驗證方式找尋最佳超參數組合，再計算測試表現，綜合比較得到最適合於實務上應用之信用評分模型。
    本研究使用之資料集來自Lending Club網站公開之P2P貸款資料，首先使用特徵工程的概念進行資料清理，而後為了找尋顯著影響違約的因子，使用XGBoost方法預訓練一次所有資料得到特徵重要度，將重要度最高的數個特徵篩選出，以供模型做為違約因子使用。
    經由比較後，最終得知GBDT方法，包括XGBoost與LightGBM，最適合用於建構P2P評分模型，表現超越神經網路與傳統上一般使用之羅吉斯回歸，而其中以XGBoost表現最為優異。
    This study applies several machine learning and neural network methods, including logistic regression, support vector machine, decision tree, random forest, XGBoost, LightGBM and neural network, to construct a credit score model of P2P loans; and then finds the best set of hyperparameters of each method by cross validation, computes training time and test performance, finally comprehensively compares those statistics, finding out the most suitable credit score model on practical application.
    This study uses open P2P loan data on the website of Lending Club, first we clean the data combining with some feature engineering concepts, in order to find significant default factors, we use XGBoost method to pre-train all data and get feature importances, sort out the most important features to be used as default factors on data modelling.
    After comprehensive comparison, we figure out that GBDT methods, including XGBoost and LightGBM, are the most appropriate for constructing credit score model, outperform neural network and logistic regression, which is commonly used on credit scoring in tradition; among the all methods, XGBoost performs best.
    參考文獻: 中文文獻
    1. 中央銀行 (2018) , “主要國家P2P借貸之發展經驗與借鏡。”, 2018年9月27日央行理監事會後記者會參考資料。
    2. 朱君亞 (2018), “金融壓力事件預警模型：類神經網路、支援向量機與羅吉斯迴歸之比較。” 碩士論文。國立政治大學金融學系碩士班。
    3. 陳勃文 (2018), “機器學習在P2P借貸信用風險模型之應用:以Lending Club為例。” 碩士論文。國立政治大學金融學系碩士班。
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    描述: 碩士
    國立政治大學
    金融學系
    1063520292
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G1063520292
    数据类型: thesis
    DOI: 10.6814/NCCU201900106
    显示于类别:[金融學系] 學位論文

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