本文選題：P2P網(wǎng)絡(luò)借貸 + 借款人信用評價體系　； 參考：《北方工業(yè)大學(xué)》2017年碩士論文

【摘要】：在信息化時代的今天,"互聯(lián)網(wǎng)+"模式引領(lǐng)了整個產(chǎn)業(yè)結(jié)構(gòu),互聯(lián)網(wǎng)金融發(fā)展的更是風(fēng)生水起。作為經(jīng)濟轉(zhuǎn)型的重要方式,P2P網(wǎng)絡(luò)借貸的到來無疑是錦上添花。P2P網(wǎng)絡(luò)借貸是基于網(wǎng)絡(luò)平臺,實現(xiàn)個人與個人之間的資金往來、借貸交易,通過將小額度資金聚攏,而獲取更大收益的一種商業(yè)模式。作為新興行業(yè),雖然國家不斷頒布相關(guān)法律條例,嚴格監(jiān)管P2P網(wǎng)絡(luò)借貸平臺的運作模式,但仍然存在著諸多問題,最為重要的是關(guān)于借款人的信用風(fēng)險評價。針對此問題的研究無論是對P2P網(wǎng)絡(luò)借貸平臺本身,還是對于投資方而言都有著十分重要的實用價值與指導(dǎo)意義。本文系統(tǒng)地論述了我國P2P網(wǎng)絡(luò)借貸的主要運作模式、特點以及相關(guān)風(fēng)險,以P2P借款人信用風(fēng)險為核心,以網(wǎng)絡(luò)借貸平臺抓取的數(shù)據(jù)為藍本,定性與定量方法雙管齊下,科學(xué)的篩選影響借款人信用風(fēng)險的主要因素,確定適當?shù)淖宰兞?從而構(gòu)建借款人信用風(fēng)險評估體系,并進行描述性的統(tǒng)計分析。進一步分別引用決策樹與Radial-Basis Function徑向基函數(shù)(RBF)神經(jīng)網(wǎng)絡(luò)兩種分類算法,建立P2P網(wǎng)絡(luò)借貸借款人信用風(fēng)險評估模型,進行仿真訓(xùn)練,有效地對借款人信用進行量化評估。最后針對兩種算法在指標選取、預(yù)測精度兩個方面的輸出結(jié)果進行對比后發(fā)現(xiàn):利率、還款期限、借款總額以及還清筆數(shù)這四種變量是兩種算法共同重要的評價指標;選取較少評價指標的決策樹模型的預(yù)測精度要高于綜合考慮了所有評價指標的RBF神經(jīng)網(wǎng)絡(luò)模型。然而,單獨識別信用良好的借款人群,RBF神經(jīng)網(wǎng)絡(luò)模型的優(yōu)勢更大。
[Abstract]:In the information age, the "Internet" mode has led the entire industrial structure, and the development of Internet finance is booming. As an important way of economic transformation, there is no doubt that the arrival of P2P network lending is an added bonus. P2P network lending is based on a network platform to realize the exchange of funds between individuals, loan transactions, and by gathering small amounts of funds. And a business model that makes more money. As a new industry, although the state has constantly promulgated relevant laws and regulations to strictly supervise the operation mode of P2P network lending platform, there are still many problems, the most important is the credit risk evaluation of borrowers. The research on this problem has very important practical value and guiding significance for P2P network lending platform and investors. This paper systematically discusses the main operation mode, characteristics and related risks of P2P network lending in China. Taking the credit risk of P2P borrowers as the core and the data captured by the network lending platform as the blueprint, the qualitative and quantitative methods are combined. Scientific screening of the main factors affecting the borrower's credit risk, determining appropriate independent variables, so as to construct the borrower's credit risk assessment system, and carry out descriptive statistical analysis. Furthermore, two classification algorithms, decision tree and Radial-Basis Function radial basis function neural network, are used to establish the credit risk assessment model of loan borrowers in P2P network, and to carry out simulation training to evaluate the borrowers' credit effectively. Finally, through comparing the output results of the two algorithms in index selection and prediction accuracy, it is found that the four variables, interest rate, repayment period, total loan amount and the number of repayments, are the common important evaluation indexes of the two algorithms; The prediction accuracy of the decision tree model with fewer evaluation indexes is higher than that of the RBF neural network model which considers all the evaluation indexes. However, the RBF neural network model has more advantages than the RBF neural network model.
【學(xué)位授予單位】：北方工業(yè)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：1803818