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  本文關(guān)鍵詞：P2P金融平臺借款者資信評估體系研究　出處：《遼寧大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 資信評估 P2P Logistic回歸

【摘要】：近年來,P2P金融機構(gòu)信貸業(yè)務(wù)正在以蓬勃的態(tài)勢發(fā)展,P2P金融機構(gòu)已經(jīng)成為中小微企業(yè)和個人消費者借款的機構(gòu)之一,合法投資者也將P2P金融機構(gòu)作為賺取利潤的平臺,P2P信貸的特點是快捷便利、無擔(dān)保,信貸資金的安全完全依靠借款者的信用,因此對P2P金融平臺和投資者來說,最大的風(fēng)險即為借款者信用風(fēng)險,在我國信用評價體系不健全的條件下,一旦借款者信用出現(xiàn)問題,欠款不還,投資者的資產(chǎn)將遭受損失,P2P金融平臺將存在壞賬風(fēng)險,投資者對P2P平臺的信任度將會降低,整個運營易陷入惡性循環(huán)。因此找出對借款者信用狀況產(chǎn)生影響的因素進而構(gòu)建信用評價體系,對于保證P2P平臺良好運營、保障投資者合法權(quán)益、切實幫助借款者籌借款項、維護好金融秩序具有非常重要的意義。以往對借款者信用的評估多采用打分的方法,指標權(quán)重的確定多采用主觀的層次分析法。本文運用logistic回歸方法,通過對某P2P平臺公司的借款者相關(guān)信息進行分析,再對借款者進行資信評估,即對各類借款者所負債務(wù)能否如約還本付息的能力和可信任程度進行綜合評價,最終提取出對借款者信用評估產(chǎn)生影響的因素,這對于P2P公司降低壞賬風(fēng)險率、為投資者建立安全的投資平臺,具有重要意義。主要工作包括:建立借款者信用風(fēng)險評估體系指標集;對數(shù)據(jù)進行分類和預(yù)處理;對不同分類的借款者進行描述統(tǒng)計分析;分別采用多項Logistic和二元logistic回歸方法建立借款者信用評估模型;參數(shù)估計的結(jié)果顯示以下十個因素會對借款者信用產(chǎn)生影響:借款者單位性質(zhì)、借款者工作崗位所屬行業(yè)、借款者工作崗位、借款者是否為循環(huán)貸、借款者銀行流水日均余額、借款者資產(chǎn)負債比、借款者貸款或者信用卡3個月內(nèi)逾期次數(shù)、借款者貸款或者信用卡6個月內(nèi)申請次數(shù)、借款者有無擔(dān)保、借款者本人素質(zhì);最后,提出降低投資者風(fēng)險、完善我國P2P金融平臺信用評估制度和改善數(shù)據(jù)收集質(zhì)量的相關(guān)建議。
[Abstract]:In recent years, P2P financial institutions credit business is booming. P2P financial institutions have become small and medium-sized enterprises and individual consumers to borrow one of the institutions. Legitimate investors also take P2P financial institutions as a platform for making profits. P2P credit is characterized by fast and convenient, unsecured, and the security of credit funds depends entirely on the credit of borrowers. Therefore, for P2P financial platform and investors, the biggest risk is the borrower credit risk. Under the condition of imperfect credit evaluation system in China, once the borrower credit problems, the debt will not be repaid. Investors' assets will suffer losses. P2P financial platform will have the risk of bad debts, investors' trust in P2P platform will be reduced. The whole operation is easy to fall into a vicious circle. So find out the factors that affect the credit status of borrowers and then build a credit evaluation system to ensure the good operation of P2P platform and protect the legitimate rights and interests of investors. It is very important to help borrowers to raise money and maintain financial order. In the past, the evaluation of borrowers' credit often used the method of scoring. The index weight is determined by the subjective analytic hierarchy process. This paper uses the logistic regression method to analyze the relevant information of the borrowers of a P2P platform company. Then the credit rating of borrowers, that is, the ability and the degree of trust of all kinds of borrowers to repay their debts, and finally extract the factors that have an impact on the credit evaluation of borrowers. This is of great significance for P2P companies to reduce the risk rate of bad debts and to establish a safe investment platform for investors. The main work includes: establishing the index set of borrowers' credit risk assessment system; Classification and preprocessing of data; Describe and analyze the borrowers in different categories; The credit evaluation model of borrowers is established by using multiple Logistic and binary logistic regression method. The results of the parameter estimate show that the following ten factors have an impact on the borrower's credit: the nature of the borrower's unit, the industry in which the borrower works, the borrower's job, and whether the borrower is a revolving loan. The average daily balance of the borrower's bank is income, the ratio of assets to liabilities of the borrower, the number of overdue times within three months of the borrower's loan or credit card, the number of times the borrower has applied for the loan or credit card within six months, and the borrower has no guarantee. The quality of the borrower himself; Finally, some suggestions are put forward to reduce the risk of investors, perfect the credit evaluation system of P2P financial platform and improve the quality of data collection.
【學(xué)位授予單位】：遼寧大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：D922.28

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本文編號：1431558