本文選題：借款人信用評估 + P2P網(wǎng)絡借貸　； 參考：《哈爾濱工業(yè)大學》2017年碩士論文

【摘要】：P2P網(wǎng)絡借貸進入中國已有10余年的時間,目前已成為個人以及中小微企業(yè)進行融資的重要渠道。2016年全年,我國網(wǎng)絡借貸平臺的累計數(shù)量已達5877家,而正常運營的平臺數(shù)量卻只有2448家,平臺成交量卻達到了20638.72億元,行業(yè)總體貸款余額更是高達8162.24億元。雖然正常運營贏得平臺有所減少,但是相較2015年去年的成交量與貸款余額分別增長110%和100.99%,表明P2P網(wǎng)絡借貸還是解決融資問題的一種重要方式。近年,隨著我國互聯(lián)網(wǎng)金融整改行動的逐步深入,我國網(wǎng)絡借貸行業(yè)的外部借貸環(huán)境逐步得到改善,但P2P網(wǎng)絡借貸的借款人信用風險問題依然是影響網(wǎng)絡借貸行業(yè)發(fā)展的重要因素,且國內大多數(shù)P2P網(wǎng)絡借貸平臺的信用評估方式都較為簡單、評估性能也不大理想,對借款人的信用管理還有待加強。因此,本文通過隨機森林模型,結合翼龍貸的數(shù)據(jù),構建了P2P借款人信用評估模型對P2P平臺中借款人存在的信用風險問題進行評估。本文在研究P2P借款人信用評估過程中,首先簡要介紹了P2P網(wǎng)絡借貸的相關概念及發(fā)展現(xiàn)狀,其次對比介紹了隨機森林、支持向量機、Logistic回歸模型,確定隨機森林為本文P2P借款人信用評估所采用模型;最后結合翼龍貸數(shù)據(jù),確定P2P借款人信用評估指標體系,并利用隨機森林構建了P2P網(wǎng)絡借貸的借款人信用評估模型。在構建隨機森林模型過程中,首先參照其他P2P網(wǎng)絡借貸地相關研究初步選取了影響借款人信用風險評估19項指標;其次利用隨機森林特征選取方法篩選出8項指標作為本文P2P借款人信用評估指標體系;然后利用R語言分別構建了隨機森林和支持向量機,并對其分類性能進行了比較分析。實證結果表明,隨機森林特征選擇能有效地篩選指標,且發(fā)現(xiàn)在P2P網(wǎng)絡借貸的信用評估方面,隨機森林模型具有更好的分類性能。通過分析指標篩選結果與實證結果還發(fā)現(xiàn),借款人在平臺中的里斯借款記錄及借款信息都是影響P2P網(wǎng)絡借貸借款人信用風險的重要觀測指標。
[Abstract]:P2P network lending has been in China for more than 10 years and has become an important channel for individuals and small and medium-sized enterprises to raise funds. In all of 2016, the total number of online lending platforms in China has reached 5877. But the normal operating platform number is only 2448, the platform turnover has reached 2.063872 trillion yuan, the industry overall loan balance is as high as 816.224 billion yuan. Although the normal operation won platform has been reduced, the volume of transactions and loan balance rose 110 percent and 100.99 percent, respectively, compared with last year in 2015, indicating that P2P network lending is also an important way to solve the financing problem. In recent years, with the gradual deepening of China's Internet financial reform, the external lending environment of China's network lending industry has been gradually improved. However, the credit risk of the borrowers of P2P network lending is still an important factor affecting the development of the network lending industry, and most of the domestic P2P network lending platform credit evaluation methods are relatively simple, the evaluation performance is not ideal. The credit management of borrowers needs to be strengthened. Therefore, based on the random forest model and pterosaurus data, this paper constructs a P2P borrower credit evaluation model to evaluate the credit risk of the borrower in P2P platform. In the course of studying the credit evaluation of P2P borrowers, this paper firstly briefly introduces the related concepts and development status of P2P network lending, and then compares the stochastic forest, support vector machine and logistic regression model. The random forest is the model used in the credit evaluation of P2P borrowers. Finally, the credit evaluation index system of P2P borrowers is established with pterosaurus loan data, and the credit evaluation model of P2P borrowers is constructed by using random forests. In the process of constructing a stochastic forest model, 19 indexes of credit risk assessment of borrowers are preliminarily selected according to the relevant studies of other P2P network lending sites. Secondly, 8 indexes are selected by the random forest feature selection method as the credit evaluation index system of P2P borrowers, and then the random forest and support vector machine are constructed by using R language, and their classification performance is compared and analyzed. The empirical results show that the stochastic forest feature selection can effectively screen the index, and it is found that the stochastic forest model has better classification performance in the credit evaluation of P2P network lending. Through the analysis of the index screening results and the empirical results, it is also found that the loan records and loan information of the borrowers in the platform are important observation indicators that affect the credit risk of P2P network borrowers.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：F832.4;F724.6

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