【摘要】：在新中國(guó)成立后,我國(guó)建立了特色鮮明的計(jì)劃經(jīng)濟(jì)體制,從而使得信用基礎(chǔ)非常脆弱,個(gè)人信用體制的發(fā)展受到嚴(yán)重的阻礙。隨著中國(guó)的改革開放,經(jīng)濟(jì)體制由計(jì)劃經(jīng)濟(jì)轉(zhuǎn)變?yōu)樯鐣?huì)主義市場(chǎng)經(jīng)濟(jì),消費(fèi)信用得到很好的發(fā)展,從而信用體制以及其風(fēng)險(xiǎn)管理日益受到關(guān)注。近期《2014—2020年社會(huì)信用體系建設(shè)規(guī)劃綱要》的頒布實(shí)施,作為中國(guó)第一部社會(huì)信用體系國(guó)家級(jí)建設(shè)專項(xiàng)規(guī)劃,開啟了中國(guó)社會(huì)信用體系規(guī)劃建設(shè)的新篇章；同時(shí)在2015年“信用中國(guó)”網(wǎng)站的開通,國(guó)家平臺(tái)先導(dǎo)工程已上線運(yùn)行,接入了各省區(qū)市和37個(gè)部門,對(duì)社會(huì)信用體系發(fā)展做出階段性成果。在國(guó)家開始高度重視信用體系發(fā)展的時(shí)期,更需要商業(yè)銀行、學(xué)術(shù)界不斷地開拓創(chuàng)新個(gè)人信用風(fēng)險(xiǎn)研究。促進(jìn)利用個(gè)人信用進(jìn)行消費(fèi)是當(dāng)今社會(huì)經(jīng)濟(jì)環(huán)境下擴(kuò)大內(nèi)需、促進(jìn)經(jīng)濟(jì)發(fā)展的重要方法。當(dāng)前中國(guó)首當(dāng)其沖的任務(wù)就是大力發(fā)展經(jīng)濟(jì),利用個(gè)人信用消費(fèi)對(duì)國(guó)民經(jīng)濟(jì)的增長(zhǎng)起到推動(dòng)力的作用,但是中國(guó)經(jīng)濟(jì)還處于社會(huì)主義初級(jí)階段,個(gè)人信用方面的發(fā)展會(huì)遇到很多困難阻礙,從而個(gè)人信用風(fēng)險(xiǎn)很難得到有效的控制。同時(shí)美國(guó)次貸危機(jī),使得人們更加重視對(duì)個(gè)人信用風(fēng)險(xiǎn)的管理,因此本文研究個(gè)人信用風(fēng)險(xiǎn)評(píng)估方法更加具有現(xiàn)實(shí)意義。經(jīng)過回顧相關(guān)的研究,對(duì)于個(gè)人信用風(fēng)險(xiǎn)評(píng)估的研究逐步從定性向定量方向發(fā)展,國(guó)內(nèi)的文獻(xiàn)往往僅局限于利用德國(guó)或澳大利亞公開信用數(shù)據(jù)庫(kù)對(duì)國(guó)外研究過的信用風(fēng)險(xiǎn)評(píng)估方法進(jìn)行實(shí)證改進(jìn),只是單純的考慮信用評(píng)估方法,并沒有將中國(guó)特有的國(guó)情特征作為評(píng)估指標(biāo),缺乏適合中國(guó)實(shí)際狀況的評(píng)估指標(biāo)體系。本文采用中國(guó)家庭金融調(diào)查中心的調(diào)研數(shù)據(jù)作為個(gè)人信用風(fēng)險(xiǎn)評(píng)估的樣本數(shù)據(jù),進(jìn)一步對(duì)個(gè)人信用風(fēng)險(xiǎn)評(píng)估方法進(jìn)行對(duì)比研究,從而發(fā)現(xiàn)更加有效的個(gè)人信用風(fēng)險(xiǎn)評(píng)估模型,促使中國(guó)個(gè)人信用風(fēng)險(xiǎn)評(píng)估指標(biāo)體系更加健康快速的發(fā)展。本文主要從以下部分對(duì)國(guó)內(nèi)個(gè)人信用風(fēng)險(xiǎn)評(píng)估方法進(jìn)行研究。一、本文首先從三個(gè)方面介紹道德中的信用,法律中的信用,經(jīng)濟(jì)中的信用。對(duì)中國(guó)當(dāng)前個(gè)人信用所面臨的主要風(fēng)險(xiǎn)因素進(jìn)行分析,即社會(huì)經(jīng)濟(jì)環(huán)境方面和放貸機(jī)構(gòu)。從社會(huì)、經(jīng)濟(jì)環(huán)境方面看風(fēng)險(xiǎn)主要集中在系統(tǒng)性風(fēng)險(xiǎn)、利率風(fēng)險(xiǎn)、政策法律風(fēng)險(xiǎn)這三個(gè)方面。我們這里指的放貸機(jī)構(gòu)主要是商業(yè)銀行。從放貸機(jī)構(gòu)看,目前的主要風(fēng)險(xiǎn)包括個(gè)人信用風(fēng)險(xiǎn)、流動(dòng)性風(fēng)險(xiǎn)、操作風(fēng)險(xiǎn)等。放貸機(jī)構(gòu)所面對(duì)的最重要的風(fēng)險(xiǎn)之一是個(gè)人信用風(fēng)險(xiǎn)。個(gè)人信用風(fēng)險(xiǎn)主要表現(xiàn)在債務(wù)人的違約、借款人信用等級(jí)變化等。當(dāng)前個(gè)人信用風(fēng)險(xiǎn)主要表現(xiàn)如下：借款人的履約能力降低、借款人的還款意愿模糊、虛假按揭。當(dāng)前的操作風(fēng)險(xiǎn)主要集中在：銀行的貸款資格標(biāo)準(zhǔn)有所降低、銀行管理體制不完善、技術(shù)水平相對(duì)落后、缺失法律依據(jù)。流動(dòng)性風(fēng)險(xiǎn)主要指當(dāng)前商業(yè)銀行資產(chǎn)和負(fù)債“期限錯(cuò)搭”—“短存長(zhǎng)貸”的現(xiàn)象,從而產(chǎn)生資金的流動(dòng)性風(fēng)險(xiǎn)。二、本文主要研究個(gè)人信用風(fēng)險(xiǎn),歸納了個(gè)人信用風(fēng)險(xiǎn)評(píng)估流程分為以下四個(gè)部分：(1)問題定義(2)樣本數(shù)據(jù)收集及預(yù)處理；(3)建立個(gè)人信用風(fēng)險(xiǎn)評(píng)估模型；(4)模型的檢驗(yàn)、解釋及其應(yīng)用；對(duì)主流的信用風(fēng)險(xiǎn)管理量化方法進(jìn)行詳細(xì)介紹如專家判別法、羅切斯特(logistic)回歸、決策樹、神經(jīng)網(wǎng)絡(luò)等方法,并比較其優(yōu)缺點(diǎn)。三、本文根據(jù)中國(guó)國(guó)情及借鑒國(guó)內(nèi)外商業(yè)銀行的個(gè)人信用風(fēng)險(xiǎn)評(píng)估指標(biāo)體系,最終初選出24項(xiàng)個(gè)人信用風(fēng)險(xiǎn)評(píng)估指標(biāo)。我們將通過量化分析的方法對(duì)以上初選的24項(xiàng)指標(biāo)進(jìn)行個(gè)人信用風(fēng)險(xiǎn)識(shí)別能力的衡量,根據(jù)量化標(biāo)準(zhǔn)進(jìn)一步對(duì)指標(biāo)進(jìn)行篩選,最終建立簡(jiǎn)單、有效的個(gè)人信用風(fēng)險(xiǎn)評(píng)估體系。對(duì)個(gè)人信用風(fēng)險(xiǎn)評(píng)估指標(biāo)的識(shí)別能力進(jìn)行判別：第一、通過獨(dú)立樣本t檢驗(yàn),得出5個(gè)評(píng)估指標(biāo)識(shí)別個(gè)人信用風(fēng)險(xiǎn)的能力比較差,相對(duì)而言,其他的19個(gè)評(píng)估指標(biāo)識(shí)別個(gè)人信用風(fēng)險(xiǎn)的能力比較強(qiáng)。所以我們需要將婚姻狀況、其他非金融資產(chǎn)、活期賬戶存款總額、持有現(xiàn)金額、遵守交通規(guī)則這5個(gè)評(píng)估指標(biāo)剔除出個(gè)人信用風(fēng)險(xiǎn)評(píng)估指標(biāo)體系。第二、通過獨(dú)立樣本非參數(shù)統(tǒng)計(jì)檢驗(yàn)得出,4個(gè)評(píng)估指標(biāo)識(shí)別個(gè)人信用風(fēng)險(xiǎn)的能力比較差,相對(duì)而言,其他的20個(gè)評(píng)估指標(biāo)識(shí)別個(gè)人信用風(fēng)險(xiǎn)的能力比較強(qiáng)。所以我們需要將婚姻狀況、其他非金融資產(chǎn)、活期賬戶存款總額、遵守交通規(guī)則這4個(gè)評(píng)估指標(biāo)剔除出個(gè)人信用風(fēng)險(xiǎn)評(píng)估指標(biāo)體系。四、本文將羅切斯特(logistic)逐步回歸統(tǒng)計(jì)方法進(jìn)一步細(xì)分為Forward Stepwise羅切斯特(logistic)逐步回歸和Backward Stepwise羅切斯特(logistic)逐步回歸,將羅切斯特(logistic)逐步回歸模型應(yīng)用到個(gè)人信用風(fēng)險(xiǎn)評(píng)估。根據(jù)Forward Stepwise羅切斯特(logistic)逐步回歸的結(jié)果,從個(gè)人信用風(fēng)險(xiǎn)管理的角度考慮,需要對(duì)個(gè)人信用風(fēng)險(xiǎn)評(píng)估指標(biāo)體系中特別關(guān)注的是：年稅后貨幣工資、信用卡記錄、在銀行已經(jīng)申請(qǐng)的貸款項(xiàng)目數(shù)、住房情況、專業(yè)技術(shù)職稱、政治面貌、股票賬戶。根據(jù)Backward Stepwise羅切斯特(logistic)逐步回歸的結(jié)果,從個(gè)人信用風(fēng)險(xiǎn)管理的角度考慮,需要對(duì)個(gè)人信用風(fēng)險(xiǎn)評(píng)估指標(biāo)體系中評(píng)估指標(biāo)給予特別關(guān)注如下：年稅后貨幣工資、信用卡記錄、工作編制、在銀行已經(jīng)申請(qǐng)的貸款項(xiàng)目數(shù)、是否為農(nóng)業(yè)戶口、住房情況、專業(yè)技術(shù)職稱、政治面貌、股票賬戶。五、為了更好的評(píng)估個(gè)人信用風(fēng)險(xiǎn),我們嘗試綜合羅切斯特(logistic)回歸分析方法和聚類分析方法的優(yōu)勢(shì),本文采用了基于羅切斯特(logistic)逐步回歸的聚類分析混合方法構(gòu)造個(gè)人信用風(fēng)險(xiǎn)評(píng)估模型。首先運(yùn)用羅切斯特(logistic)逐步回歸模型進(jìn)行回歸來確認(rèn)聚類成分,再者采用最近距離法對(duì)樣本數(shù)據(jù)進(jìn)行分類,最終實(shí)現(xiàn)個(gè)人信用的有效分類；在完成綜合個(gè)人信用風(fēng)險(xiǎn)評(píng)估模型的建立后,運(yùn)用ROC曲線對(duì)模型進(jìn)行進(jìn)一步檢驗(yàn)。為了排除信用風(fēng)險(xiǎn)評(píng)估指標(biāo)之間的含義重合對(duì)模型的不良影響,我們運(yùn)用SPSS軟件利用極大似然方法羅切斯特(logistic)逐步回歸方法,最終經(jīng)過篩選確定了9個(gè)評(píng)估指標(biāo),依次分別是政治面貌、文化程度、專業(yè)技術(shù)職稱、住房情況、是否農(nóng)業(yè)戶口、股票賬戶、信用卡記錄、在銀行已經(jīng)申請(qǐng)的貸款項(xiàng)目數(shù)、年稅后貨幣工資。采用聚類分析進(jìn)一步確定了4個(gè)聚類成分分別為政治面貌、文化程度、住房情況、信用記錄。最終建立雙邊聚類模型,對(duì)羅切斯特(logistic)回歸模型和雙邊聚類統(tǒng)計(jì)模型進(jìn)行對(duì)比得出雙邊聚類統(tǒng)計(jì)模型更有效。六、結(jié)束語論述本文的主要結(jié)論及不足。
[Abstract]:After the founding of the People's Republic of China, China has established a distinctive planned economic system, which has made the credit foundation very fragile and seriously hindered the development of the personal credit system. Recently, the promulgation and implementation of the Outline of the Social Credit System Construction Plan for 2014-2020 has opened a new chapter in the planning and construction of China's social credit system as the first state-level special plan for the construction of China's social credit system, and at the same time, the opening of the "Credit China" website in 2015 has enacted the State. The platform pilot project has been put into operation on line and has been connected to provinces, municipalities and 37 departments, and has made periodic achievements in the development of social credit system. At present, China's primary task is to vigorously develop the economy and use personal credit consumption to promote the growth of the national economy. However, China's economy is still in the primary stage of socialism, and personal credit development will encounter many difficulties and obstacles. At the same time, the American subprime mortgage crisis makes people pay more attention to the management of personal credit risk. Therefore, it is more practical to study the methods of personal credit risk assessment. Domestic literatures are often limited to the empirical improvement of the credit risk assessment methods studied abroad by using German or Australian open credit databases. They only consider the credit assessment methods purely, and do not take the characteristics of China's unique national conditions as the evaluation index. They lack the assessment suitable for China's actual conditions. Indicator system. This paper uses the survey data of China Family Financial Survey Center as the sample data of personal credit risk assessment, and further makes a comparative study of personal credit risk assessment methods, so as to find a more effective personal credit risk assessment model, and promote the Chinese personal credit risk assessment index system to be healthier and faster. This paper mainly studies the domestic personal credit risk assessment methods from the following parts. First, this paper introduces the moral credit, the legal credit, and the economic credit from three aspects. From the perspective of social and economic environment, risk mainly concentrates on three aspects: systemic risk, interest rate risk, policy and legal risk. The lending institutions we refer to here are mainly commercial banks. One of the risks is personal credit risk. Personal credit risk is mainly manifested in the debtor's default, the change of the borrower's credit rating and so on. Liquidity risk mainly refers to the current phenomenon that the assets and liabilities of commercial banks are "mismatched in terms of maturity", "short-term deposit and long-term loan", thus resulting in liquidity risk of funds. Second, this paper mainly studies the personal credit risk and summarizes the personal credit risk. Risk assessment process is divided into the following four parts: (1) problem definition (2) sample data collection and preprocessing; (3) establishment of personal credit risk assessment model; (4) model testing, interpretation and application; detailed introduction of mainstream credit risk management quantitative methods such as expert discrimination, logistic regression, decision tree, God Thirdly, according to China's national conditions and the individual credit risk assessment index system of commercial banks at home and abroad, 24 individual credit risk assessment indicators are initially selected. We will weigh the individual credit risk identification ability of these 24 indicators through quantitative analysis. Quantity, according to the quantitative criteria for further screening indicators, and ultimately establish a simple and effective personal credit risk assessment system. Individual credit risk assessment indicators to identify the ability to distinguish: First, through independent sample t test, five evaluation indicators identified the ability of individual credit risk is relatively poor, relative to the other 19 So we need to exclude the personal credit risk assessment index system from the five evaluation indicators: marital status, other non-financial assets, total current account deposits, cash holdings, compliance with traffic rules. Second, through independent sample non-parametric statistical test, we get four evaluations. Assessment indicators have a poor ability to identify individual credit risk. Relatively speaking, the other 20 indicators have a strong ability to identify individual credit risk. Therefore, we need to exclude the individual credit risk assessment index system from the four evaluation indicators: marital status, other non-financial assets, total current account deposits and compliance with traffic rules. Fourthly, this paper subdivides the logistic stepwise regression method into Forward Stepwise Rochester stepwise regression and Backward Stepwise Rochester stepwise regression, and applies the logistic stepwise regression model to personal credit risk assessment. According to Backward Stepwise As a result of logistic regression, from the perspective of personal credit risk management, it is necessary to pay special attention to the evaluation index system of personal credit risk assessment as follows: annual monetary salary after tax, credit card records, work preparation, the number of loan items that have been applied for in banks, whether they are agricultural accounts, housing conditions Fifth, in order to better evaluate personal credit risk, we try to integrate the advantages of logistic regression analysis and clustering analysis. In this paper, we construct a personal credit risk assessment model based on logistic stepwise regression. Firstly, the logistic stepwise regression model is used to confirm the clustering components, and then the nearest distance method is used to classify the sample data to realize the effective classification of personal credit. After the establishment of comprehensive personal credit risk assessment model, the model is further tested by ROC curve. We use SPSS software to use the maximum likelihood method Rochester (logistic) stepwise regression method, and finally through screening to determine nine evaluation indicators, respectively, the political outlook, education level, professional titles, housing conditions, whether agricultural household registration, stocks. Accounts, credit card records, the number of loans that have been applied for in the bank, annual monetary wages after tax. Cluster analysis was used to further determine the four clustering components are political outlook, education level, housing situation, credit records. Finally, a bilateral clustering model was established, and a logistic regression model and a bilateral clustering statistical model were used. The comparison shows that the bilateral clustering statistical model is more effective. Six, concluding remarks discuss the main conclusions and shortcomings of this paper.
【學(xué)位授予單位】：西南財(cái)經(jīng)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：F832.4

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