本文選題：PP借貸 + 違約預(yù)測��； 參考：《中國管理科學》2017年11期

【摘要】：在P2P網(wǎng)絡(luò)借貸中,預(yù)測借款的違約概率是用戶信用評價的關(guān)鍵,也是借貸平臺與投資者關(guān)注的重點問題。由于P2P平臺所獲取的用戶財務(wù)信息有限,P2P借款信用評價和違約預(yù)測面臨新的挑戰(zhàn)。本文結(jié)合P2P平臺的信息特點,提出一種融入軟信息的網(wǎng)絡(luò)借款違約預(yù)測方法。首先利用主題模型抽取并量化文本軟信息中的相關(guān)變量,進而分析不同軟信息變量對借款違約的影響關(guān)系;其次,設(shè)計了一種兩階段的變量選擇方法對軟硬信息進行組合篩選;最后,引入隨機森林算法構(gòu)建融入軟信息的違約預(yù)測模型,并結(jié)合P2P平臺的真實數(shù)據(jù)進行實證分析。結(jié)果表明,在P2P借款的違約預(yù)測模型中融入有價值的軟信息可以提高預(yù)測準確率。
[Abstract]:In P2P network lending, predicting the default probability of loan is the key of user credit evaluation, and also the key issue of loan platform and investors. Due to the limited financial information obtained by P2P platform, P2P loan credit evaluation and default prediction face new challenges. According to the information characteristics of P2P platform, this paper proposes a network loan default prediction method which integrates soft information. Firstly, we use the topic model to extract and quantify the relevant variables in the text soft information, and then analyze the influence of different soft information variables on the loan default. Secondly, a two-stage variable selection method is designed to select the soft and hard information. Finally, the stochastic forest algorithm is introduced to construct the default prediction model with soft information, and the real data of P2P platform are analyzed empirically. The results show that the prediction accuracy can be improved by incorporating valuable soft information into the default prediction model of P2P loan.
【作者單位】： 合肥工業(yè)大學管理學院;
【基金】：國家自然科學基金資助項目(71731005,71571059)
【分類號】：F724.6;F832.4

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本文編號：2092618