本文選題：網(wǎng)絡(luò)借貸　切入點：精準(zhǔn)借貸　出處：《山西財經(jīng)大學(xué)》2017年碩士論文

【摘要】：網(wǎng)絡(luò)借貸是我國新型金融體系建設(shè)的重要組成部分,有利于解決中小企業(yè)融資難的問題,一定程度上促進(jìn)了我國經(jīng)濟(jì)的發(fā)展。然而不少網(wǎng)絡(luò)借貸平臺面臨生存危機(jī),原因是平臺自身經(jīng)營成本高,沒有制定科學(xué)的營銷策略,投入大量線下和線上推廣卻難見成效。為了能夠解決上述問題,網(wǎng)絡(luò)借貸平臺需要借助大數(shù)據(jù)技術(shù)對用戶行為進(jìn)行分析和預(yù)測,從而更加了解客戶需求,制定有針對性的營銷策略。網(wǎng)絡(luò)借貸平臺二次貸款預(yù)測研究有利于網(wǎng)絡(luò)借貸平臺對用戶是否由再次貸款需求進(jìn)行預(yù)測,從而提高營銷轉(zhuǎn)化率,進(jìn)而提高其盈利能力。本文以網(wǎng)絡(luò)借貸平臺二次貸款預(yù)測為目標(biāo),研究了如下內(nèi)容:首先結(jié)合網(wǎng)絡(luò)借貸和精準(zhǔn)營銷理論,提出了精準(zhǔn)借貸這一概念,并分析了大數(shù)據(jù)背景下網(wǎng)絡(luò)借貸平臺精準(zhǔn)借貸的流程。其次,對網(wǎng)絡(luò)借貸平臺的用戶數(shù)據(jù)進(jìn)行分析和整理,初步構(gòu)建了網(wǎng)絡(luò)借貸平臺用戶二次貸款指標(biāo)體系;利用具有較好的特征選擇能力的彈性網(wǎng)方法對指標(biāo)進(jìn)行篩選,選擇出具有重要意義的指標(biāo)。然后,在此基礎(chǔ)上將稀疏貝葉斯網(wǎng)絡(luò)結(jié)構(gòu)學(xué)習(xí)方法應(yīng)用于網(wǎng)絡(luò)借貸用戶行為分析中,并證明該方法優(yōu)于一般的貝葉斯網(wǎng)絡(luò);通過分析指標(biāo)之間的關(guān)系,研究出網(wǎng)絡(luò)借貸平臺用戶的行為關(guān)系和重要特點。接著,充分利用Xg-boost這一集成算法的優(yōu)勢,將其應(yīng)用于網(wǎng)絡(luò)借貸用戶二次貸款預(yù)測中,并采用分類正確率和時間復(fù)雜度兩個評價指標(biāo)進(jìn)行預(yù)測模型評估,取得良好的效果,證明該算法適合在類似的問題中被廣泛推廣和應(yīng)用。最后根據(jù)分析結(jié)果并結(jié)合網(wǎng)絡(luò)借貸發(fā)展形勢,提出平臺為了實現(xiàn)精準(zhǔn)借貸可以充分利用用戶信息,構(gòu)建用戶行為網(wǎng)絡(luò)和預(yù)測模型,為制定精準(zhǔn)借貸策略提供數(shù)據(jù)支持。本文得出的主要結(jié)論有:(1)網(wǎng)絡(luò)借貸平臺用戶二次貸款預(yù)測為平臺精準(zhǔn)借貸提供數(shù)據(jù)支持,對于平臺服務(wù)策略制定具有指導(dǎo)性意義;(2)彈性網(wǎng)方法在變量篩選中的應(yīng)用有選擇重要變量構(gòu)建模型,提高模型分類性能;(3)消費記錄和社會資本的引入可以提高模型的分類正確率;(4)Xg-boost模型在網(wǎng)絡(luò)借貸平臺二次貸款的預(yù)測性能中優(yōu)于其他集成分類器且時間復(fù)雜度低,適宜推廣。
[Abstract]:Network lending is an important part of the construction of new financial system in China, which is helpful to solve the problem of financing difficulties for small and medium-sized enterprises, and to some extent promotes the development of our economy. However, many online lending platforms are faced with survival crisis. The reason is that the platform itself has high operating costs, no scientific marketing strategy, a large amount of offline and online promotion is difficult to see results. Online lending platforms need to use big data technology to analyze and predict user behavior in order to better understand customer needs. The research on the secondary loan forecast of the network loan platform is helpful for the network loan platform to predict whether the users' demand for the second loan will be predicted by the second loan, thus increasing the marketing conversion rate. In order to improve its profitability, this paper aims at the second loan forecast of network lending platform, and studies the following contents: firstly, combining the theory of network lending and precision marketing, the concept of precision lending is put forward. And analyzes the network lending platform precision lending process under the background of big data. Secondly, the user data of the network lending platform is analyzed and collated, and the user secondary loan index system of the network lending platform is preliminarily constructed. Using the elastic network method with better feature selection ability to screen the index and select the important index. Then, the sparse Bayesian network structure learning method is applied to the behavior analysis of the network lending user, based on which the sparse Bayesian network structure learning method is applied to the behavior analysis of the network loan user. It is proved that this method is superior to the general Bayesian network, and through the analysis of the relationship between the indexes, the behavior relationship and important characteristics of the users of the network lending platform are studied. Then, the advantages of the Xg-boost integration algorithm are fully utilized. It is applied to the secondary loan prediction of network loan users, and two evaluation indexes, the classification accuracy and the time complexity, are used to evaluate the prediction model, and good results are obtained. It is proved that the algorithm is suitable to be widely used in similar problems. Finally, according to the analysis results and combined with the development situation of network lending, the platform can make full use of user information in order to realize precision lending. Build user behavior network and forecast model to provide data support for the development of precision lending strategy. The main conclusions of this paper are: 1) the network lending platform user second loan forecast provides data support for the platform precision lending. For the application of the flexible network method in variable selection, the important variables are selected to build the model, which is of guiding significance to the development of platform service strategy. To improve the classification performance of the model, the consumption record and the introduction of social capital can improve the classification accuracy of the model and the 4Xg-boost model is superior to other integrated classifiers in the prediction performance of the secondary loan in the network lending platform, and the time complexity is low, and the model is suitable for popularization.
【學(xué)位授予單位】：山西財經(jīng)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：F724.6;F832.4

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