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題名 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貢獻者 林士貴<br>蔡瑞煌
楊立楷
Yang, Li-Kai關鍵詞 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 上傳時間 7-Aug-2019 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。英文文獻1. 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國立政治大學
金融學系
1063520292資料來源 http://thesis.lib.nccu.edu.tw/record/#G1063520292 資料類型 thesis dc.contributor.advisor 林士貴<br>蔡瑞煌 zh_TW dc.contributor.author (Authors) 楊立楷 zh_TW dc.contributor.author (Authors) Yang, Li-Kai en_US dc.creator (作者) 楊立楷 zh_TW dc.creator (作者) Yang, Li-Kai en_US dc.date (日期) 2019 en_US dc.date.accessioned 7-Aug-2019 16:14:20 (UTC+8) - dc.date.available 7-Aug-2019 16:14:20 (UTC+8) - dc.date.issued (上傳時間) 7-Aug-2019 16:14:20 (UTC+8) - dc.identifier (Other Identifiers) G1063520292 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/124747 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 金融學系 zh_TW dc.description (描述) 1063520292 zh_TW dc.description.abstract (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。 zh_TW dc.description.abstract (摘要) 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. en_US dc.description.tableofcontents 第一章 緒論 1第一節 研究背景及動機 1第二節 研究目的 3第二章 文獻回顧 4第一節 信貸違約因子 4第二節 羅吉斯迴歸 5第三節 支援向量機 6第四節 決策樹 6第五節 隨機森林 7第六節 XGBoost 7第七節 LightGBM 8第八節 類神經網路 8第三章 研究方法 9第一節 羅吉斯迴歸 9第二節 支援向量機 10第三節 決策樹 14第四節 隨機森林 16第五節 XGBoost 17第六節 LightGBM 22第七節 類神經網路 23第八節 模型衡量指標 34第四章 資料描述 36第一節 資料來源 36第二節 資料清理 37第五章 實證結果 44第一節 格狀搜尋最佳超參數 44第二節 測試表現綜合比較 49第六章 研究結論與未來展望 52第一節 研究結論 52第二節 未來展望 53參考文獻 54附錄一 60附錄二 72 zh_TW dc.format.extent 2359602 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G1063520292 en_US dc.subject (關鍵詞) P2P借貸 zh_TW dc.subject (關鍵詞) 信用評分 zh_TW dc.subject (關鍵詞) 機器學習 zh_TW dc.subject (關鍵詞) 類神經網路 zh_TW dc.subject (關鍵詞) 特徵工程 zh_TW dc.subject (關鍵詞) Lending Club zh_TW dc.subject (關鍵詞) P2P lending en_US dc.subject (關鍵詞) Credit score en_US dc.subject (關鍵詞) Machine learning en_US dc.subject (關鍵詞) Neural network en_US dc.subject (關鍵詞) Feature engineering en_US dc.subject (關鍵詞) Lending Club en_US dc.title (題名) 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 zh_TW dc.title (題名) Neural Network and Machine Learning to Construct P2P Lending Credit Score Model: A Case of Lending Club en_US dc.type (資料類型) thesis en_US dc.relation.reference (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。英文文獻1. Aldrich, J. H., & Nelson, F. D. (1984), Quantitative Applications in the Social Sciences: Linear Probability, Logit, and Probit Models. Thousand Oaks, CA: SAGE Publications.2. Alexander, V. E. & Clifford, C. C. (1996), Categorical Variables in Developmental Research: Methods of Analysis. Elsevier.3. Arya, S., Eckel C. & Wichman C. (2013), “Anatomy of the Credit Score.” Journal of Economic Behavior & Organization, Vol. 95, 175-185.4. Baesens, B., Gestel, T. V., Stepanova M. & Poel, D. V. D. (2004), “Neural Network Survival Analysis for Personal Loan Data.” Journal of the Operational Research Society, Vol. 56, 1089-1098.5. Bishop, C. M. (2006), Pattern Recognition and Machine Learning. Springer.6. Bolton, C. (2009), Logistic Regression and its Application in Credit Scoring. University of Pretoria.7. Breiman, L. (1996), “Bagging Predictors.” Machine Learning, Vol. 24, No. 2, 123-140.8. Breiman, L. (2001). “Random Forests.” Machine Learning, Vol. 45, No. 1, 5-32.9. Breiman, L., Friedman, J., Stone, C. J. & Olshen, R. A. (1984), Classification and Regression Trees., Taylor & Francis.10. Brown, M., Grundy, M., Lin, D., Cristianini, N., Sugnet, C., Furey, T., Ares, M. & Haussler, D. (1999), “Knowledge-Base Analysis of Microarray Gene Expression Data Using Support Vector Machines.” Technical Report, University of California in Santa Cruz.11. Chen, T. Q. & Guestrin, C. (2016), “XGBoost: A Scalable Tree Boosting System.” KDD `16 Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785-794.12. Crouhy, M., Galai D. & Mark R. (2014), The Essentials of Risk Management 2nd Edition. McGraw-Hill.13. Cybenko, G. (1989), “Approximation by Superpositions of a Sigmoidal Function Mathematics of Control.” Signals and Systems, Vol. 2, No. 4, 303-314.14. Dietterich, T. G. (2000), “An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization.” Machine Learning, Vol. 40, No. 2, 139-157.15. Duchi, J., Hazan, E. & Singer, Y. (2011), “Adaptive Subgradient Methods for Online Learning and Stochastic Optimization.” Journal of Machine Learning Research, Vol. 12, 2121–2159.16. Elrahman, S. M. A & Abraham, A. (2013), “A Review of Class Imbalance Problem.” Journal of Network and Innovative Computing, Vol. 1, 332-340.17. Everett, C. R. (2015). “Group Membership, Relationship Banking and Loan Default Risk: the Case of Online Social Lending.” Banking and Finance Review, Vol.7, No.2, 15-54.18. Fletcher, R. (1981), “A Nonlinear Programming Problem in Statistics.” SIAM Journal on Scientific and Statistical Computing, Vol. 2, No. 3, 257-267.19. Friedman, J. H. (2001), “Greedy Function Approximation: A Gradient Boosting Machine.” The Annals of Statistics, Vol. 29, No. 5, 1189-1232.20. Genuer, R., Poggi, J. 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