我國個人信用風(fēng)險評估方法研究
發(fā)布時間：2018-09-04 09:56
【摘要】：在新中國成立后,我國建立了特色鮮明的計劃經(jīng)濟體制,從而使得信用基礎(chǔ)非常脆弱,個人信用體制的發(fā)展受到嚴(yán)重的阻礙。隨著中國的改革開放,經(jīng)濟體制由計劃經(jīng)濟轉(zhuǎn)變?yōu)樯鐣髁x市場經(jīng)濟,消費信用得到很好的發(fā)展,從而信用體制以及其風(fēng)險管理日益受到關(guān)注。近期《2014—2020年社會信用體系建設(shè)規(guī)劃綱要》的頒布實施,作為中國第一部社會信用體系國家級建設(shè)專項規(guī)劃,開啟了中國社會信用體系規(guī)劃建設(shè)的新篇章；同時在2015年“信用中國”網(wǎng)站的開通,國家平臺先導(dǎo)工程已上線運行,接入了各省區(qū)市和37個部門,對社會信用體系發(fā)展做出階段性成果。在國家開始高度重視信用體系發(fā)展的時期,更需要商業(yè)銀行、學(xué)術(shù)界不斷地開拓創(chuàng)新個人信用風(fēng)險研究。促進利用個人信用進行消費是當(dāng)今社會經(jīng)濟環(huán)境下擴大內(nèi)需、促進經(jīng)濟發(fā)展的重要方法。當(dāng)前中國首當(dāng)其沖的任務(wù)就是大力發(fā)展經(jīng)濟,利用個人信用消費對國民經(jīng)濟的增長起到推動力的作用,但是中國經(jīng)濟還處于社會主義初級階段,個人信用方面的發(fā)展會遇到很多困難阻礙,從而個人信用風(fēng)險很難得到有效的控制。同時美國次貸危機,使得人們更加重視對個人信用風(fēng)險的管理,因此本文研究個人信用風(fēng)險評估方法更加具有現(xiàn)實意義。經(jīng)過回顧相關(guān)的研究,對于個人信用風(fēng)險評估的研究逐步從定性向定量方向發(fā)展,國內(nèi)的文獻往往僅局限于利用德國或澳大利亞公開信用數(shù)據(jù)庫對國外研究過的信用風(fēng)險評估方法進行實證改進,只是單純的考慮信用評估方法,并沒有將中國特有的國情特征作為評估指標(biāo),缺乏適合中國實際狀況的評估指標(biāo)體系。本文采用中國家庭金融調(diào)查中心的調(diào)研數(shù)據(jù)作為個人信用風(fēng)險評估的樣本數(shù)據(jù),進一步對個人信用風(fēng)險評估方法進行對比研究,從而發(fā)現(xiàn)更加有效的個人信用風(fēng)險評估模型,促使中國個人信用風(fēng)險評估指標(biāo)體系更加健康快速的發(fā)展。本文主要從以下部分對國內(nèi)個人信用風(fēng)險評估方法進行研究。一、本文首先從三個方面介紹道德中的信用,法律中的信用,經(jīng)濟中的信用。對中國當(dāng)前個人信用所面臨的主要風(fēng)險因素進行分析,即社會經(jīng)濟環(huán)境方面和放貸機構(gòu)。從社會、經(jīng)濟環(huán)境方面看風(fēng)險主要集中在系統(tǒng)性風(fēng)險、利率風(fēng)險、政策法律風(fēng)險這三個方面。我們這里指的放貸機構(gòu)主要是商業(yè)銀行。從放貸機構(gòu)看,目前的主要風(fēng)險包括個人信用風(fēng)險、流動性風(fēng)險、操作風(fēng)險等。放貸機構(gòu)所面對的最重要的風(fēng)險之一是個人信用風(fēng)險。個人信用風(fēng)險主要表現(xiàn)在債務(wù)人的違約、借款人信用等級變化等。當(dāng)前個人信用風(fēng)險主要表現(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)水平相對落后、缺失法律依據(jù)。流動性風(fēng)險主要指當(dāng)前商業(yè)銀行資產(chǎn)和負(fù)債“期限錯搭”—“短存長貸”的現(xiàn)象,從而產(chǎn)生資金的流動性風(fēng)險。二、本文主要研究個人信用風(fēng)險,歸納了個人信用風(fēng)險評估流程分為以下四個部分：(1)問題定義(2)樣本數(shù)據(jù)收集及預(yù)處理；(3)建立個人信用風(fēng)險評估模型；(4)模型的檢驗、解釋及其應(yīng)用；對主流的信用風(fēng)險管理量化方法進行詳細(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。三、本文根據(jù)中國國情及借鑒國內(nèi)外商業(yè)銀行的個人信用風(fēng)險評估指標(biāo)體系,最終初選出24項個人信用風(fēng)險評估指標(biāo)。我們將通過量化分析的方法對以上初選的24項指標(biāo)進行個人信用風(fēng)險識別能力的衡量,根據(jù)量化標(biāo)準(zhǔn)進一步對指標(biāo)進行篩選,最終建立簡單、有效的個人信用風(fēng)險評估體系。對個人信用風(fēng)險評估指標(biāo)的識別能力進行判別：第一、通過獨立樣本t檢驗,得出5個評估指標(biāo)識別個人信用風(fēng)險的能力比較差,相對而言,其他的19個評估指標(biāo)識別個人信用風(fēng)險的能力比較強。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個評估指標(biāo)剔除出個人信用風(fēng)險評估指標(biāo)體系。第二、通過獨立樣本非參數(shù)統(tǒng)計檢驗得出,4個評估指標(biāo)識別個人信用風(fēng)險的能力比較差,相對而言,其他的20個評估指標(biāo)識別個人信用風(fēng)險的能力比較強。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個評估指標(biāo)剔除出個人信用風(fēng)險評估指標(biāo)體系。四、本文將羅切斯特(logistic)逐步回歸統(tǒng)計方法進一步細(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)用到個人信用風(fēng)險評估。根據(jù)Forward Stepwise羅切斯特(logistic)逐步回歸的結(jié)果,從個人信用風(fēng)險管理的角度考慮,需要對個人信用風(fēng)險評估指標(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)申請的貸款項目數(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,從個人信用風(fēng)險管理的角度考慮,需要對個人信用風(fēng)險評估指標(biāo)體系中評估指標(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)申請的貸款項目數(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、為了更好的評估個人信用風(fēng)險,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,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)造個人信用風(fēng)險評估模型。首先運用羅切斯特(logistic)逐步回歸模型進行回歸來確認(rèn)聚類成分,再者采用最近距離法對樣本數(shù)據(jù)進行分類,最終實現(xiàn)個人信用的有效分類；在完成綜合個人信用風(fēng)險評估模型的建立后,運用ROC曲線對模型進行進一步檢驗。為了排除信用風(fēng)險評估指標(biāo)之間的含義重合對模型的不良影響,我們運用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個評估指標(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)申請的貸款項目數(shù)、年稅后貨幣工資。采用聚類分析進一步確定了4個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,對羅切斯特(logistic)回歸模型和雙邊聚類統(tǒng)計模型進行對比得出雙邊聚類統(tǒng)計模型更有效。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】：西南財經(jīng)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：F832.4
本文編號：2221743
[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】：西南財經(jīng)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：F832.4
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9 陳昕;;商業(yè)銀行個人信用風(fēng)險的實證分析[J];現(xiàn)代審計與經(jīng)濟;2008年03期
10 胡望斌,朱東華,汪雪鋒;商業(yè)銀行個人信用風(fēng)險等級評估與預(yù)測[J];商業(yè)時代;2005年09期
相關(guān)會議論文 前2條
1 賴輝;帥理;周宗放;;基于雙邊聚類的個人信用風(fēng)險判別方法及實證研究[A];風(fēng)險分析和危機反應(yīng)的創(chuàng)新理論和方法——中國災(zāi)害防御協(xié)會風(fēng)險分析專業(yè)委員會第五屆年會論文集[C];2012年
2 朱元梅;帥理;周宗放;;基于PSO—RBF神經(jīng)網(wǎng)絡(luò)的個人信用風(fēng)險評估研究[A];風(fēng)險分析和危機反應(yīng)的創(chuàng)新理論和方法——中國災(zāi)害防御協(xié)會風(fēng)險分析專業(yè)委員會第五屆年會論文集[C];2012年
相關(guān)重要報紙文章 前5條
1 劉春洪;規(guī)避個人信用風(fēng)險迫在眉睫[N];發(fā)展導(dǎo)報;2002年
2 本報記者鄒小丹;規(guī)避個人信用風(fēng)險迫在眉睫[N];中華工商時報;2002年
3 孫瑞灼;提醒個人信用風(fēng)險是銀行應(yīng)盡義務(wù)[N];中國信息報;2008年
4 鄒小丹;個人信用風(fēng)險正成為信貸消費“瓶頸”[N];亞太經(jīng)濟時報;2002年
5 本報記者 張彥武;“亞飛模式”——個人信用體系的有益嘗試[N];中國汽車報;2002年
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1 帥理;個人信用風(fēng)險評估理論與方法的拓展研究[D];電子科技大學(xué);2015年
相關(guān)碩士學(xué)位論文 前6條
1 杜鵬鴿;商業(yè)銀行個人信用風(fēng)險評價研究[D];石家莊經(jīng)濟學(xué)院;2015年
2 宓珊珊;我國個人信用風(fēng)險評估方法研究[D];西南財經(jīng)大學(xué);2016年
3 周仲輝;互聯(lián)網(wǎng)金融背景下的個人信用風(fēng)險度量系統(tǒng)的開發(fā)與應(yīng)用[D];湖南大學(xué);2016年
4 曹佳;基于人工神經(jīng)網(wǎng)絡(luò)的商業(yè)銀行個人信用風(fēng)險控制模型[D];北京化工大學(xué);2008年
5 耿慧;基于神經(jīng)網(wǎng)絡(luò)的銀行個人信用風(fēng)險評估研究[D];山東科技大學(xué);2009年
6 崔健;商業(yè)銀行個人信用風(fēng)險評價[D];天津大學(xué);2005年
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