本文選題：P2P網(wǎng)貸 + 信用風(fēng)險��； 參考：《廣州大學(xué)》2017年碩士論文

【摘要】：P2P網(wǎng)絡(luò)借貸,俗稱P2P網(wǎng)貸,起步于英國.近年來,伴隨著互聯(lián)網(wǎng)技術(shù)的快速普及,P2P網(wǎng)貸在國內(nèi)外發(fā)展特別迅速,呈現(xiàn)出各種各樣的形式.P2P網(wǎng)貸屬于小額借貸,投資門檻低,備受大眾青睞,然而平臺跑路、借款人違約等事件層出不窮,因而也得到廣泛關(guān)注.本文以中國P2P網(wǎng)貸為研究對象,運用兩種常用的機(jī)器學(xué)習(xí)方法和一種新的貝葉斯方法來評估網(wǎng)貸借款人的信用風(fēng)險.這三種方法分別是:支持向量機(jī)(SVM)、隨機(jī)森林(RF)以及貝葉斯可加分類樹(BACT).首先將原始數(shù)據(jù)分成訓(xùn)練數(shù)據(jù)和測試數(shù)據(jù),然后將訓(xùn)練數(shù)據(jù)等分成三份,稱為原始訓(xùn)練集.考慮到數(shù)據(jù)存在類不平衡問題,故使用SMOTE算法對訓(xùn)練數(shù)據(jù)進(jìn)行處理,以下將稱這種數(shù)據(jù)為SMOTE訓(xùn)練集.其次分別以原始訓(xùn)練集、SMOTE訓(xùn)練集分別通過3折交叉驗證,同時選取各模型對應(yīng)參數(shù).然后結(jié)合訓(xùn)練集、所選參數(shù)及模型對測試集進(jìn)行建模、預(yù)測,進(jìn)而比較不同訓(xùn)練數(shù)據(jù)集下訓(xùn)練所得模型之間的分類效果.最終發(fā)現(xiàn),在使用SMOTE訓(xùn)練集來訓(xùn)練模型時,貝葉斯可加分類樹和隨機(jī)森林能夠很好地識別違約的借款人,不過他們的AUC值沒有顯著變化,支持向量機(jī)的AUC值顯著變大;貝葉斯可加分類樹的分類準(zhǔn)確率和AUC值都比隨機(jī)森林、支持向量機(jī)大,其兩類誤分類率均比隨機(jī)森林、支持向量機(jī)小;隨機(jī)森林和貝葉斯可加分類樹對應(yīng)的ROC曲線明顯位于支持向量機(jī)所對應(yīng)ROC曲線的左上方,而隨機(jī)森林對應(yīng)的ROC曲線幾乎與貝葉斯可加分類樹對應(yīng)的ROC曲線重合;這些都說明在SMOTE訓(xùn)練集下,貝葉斯可加分類樹在評估借款人的信用風(fēng)險方面具有很好的效果。
[Abstract]:P2P network lending, commonly known as P2P network loans, started in the United Kingdom. In recent years, with the rapid popularization of Internet technology, P2P network loans are developing rapidly at home and abroad, showing a variety of forms. P2P network loans are small loans, low investment barriers, popular favor, but the platform runs. Borrowers default and other events emerge in endlessly, and thus get widespread attention. In this paper, two commonly used machine learning methods and a new Bayesian method are used to evaluate the credit risk of Chinese P2P loan borrowers. The three methods are: support vector machine (SVM), random forest forest (RFF), and Bayes additive classification tree (BACTT). First, the raw data is divided into training data and test data, and then the training data is divided into three parts, called the original training set. Considering the class imbalance of the data, the SMOTE algorithm is used to process the training data, which will be called the SMOTE training set. Secondly, the original training set and the SMOTE training set are respectively verified by 3 fold cross-validation, and the corresponding parameters of each model are selected at the same time. Then the test set is modeled and predicted with the training set, the selected parameters and the model, and then the classification effect between the training models under different training data sets is compared. Finally, it is found that when using SMOTE training set to train the model, Bayes plus classification tree and random forest can identify the defaulting borrowers well, but their AUC value does not change significantly, and the AUC value of support vector machine increases significantly. The classification accuracy and AUC value of the Bayes additive classification tree are higher than those of the random forest, and the support vector machine (SVM) is larger than the random forest, and the misclassification rate of the two kinds of tree is smaller than that of the random forest, and the support vector machine (SVM) is smaller. The ROC curve of random forest and Bayes additive classification tree is obviously located at the upper left of the ROC curve corresponding to support vector machine, while the ROC curve of random forest almost coincides with ROC curve corresponding to Bayes additive classification tree. These results show that the Bayes additive classification tree is effective in assessing the credit risk of the borrower under SMOTE training set.
【學(xué)位授予單位】：廣州大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：F832.4;F724.6

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本文編號：1868860