本文選題：信用風(fēng)險(xiǎn) + 數(shù)據(jù)挖掘技術(shù)��； 參考：《南京財(cái)經(jīng)大學(xué)》2015年碩士論文

【摘要】：網(wǎng)絡(luò)金融和虛擬經(jīng)濟(jì)的發(fā)展給傳統(tǒng)銀行業(yè)務(wù)造成沖擊和挑戰(zhàn)的同時(shí),也給商業(yè)提供了前所未有的發(fā)展機(jī)遇。隨著大數(shù)據(jù)時(shí)代的到來(lái),商業(yè)銀行每天的業(yè)務(wù)信息數(shù)據(jù)呈爆炸式增長(zhǎng),不僅給數(shù)據(jù)的存儲(chǔ)工作造成困難,還極大增加了商業(yè)銀行利用數(shù)據(jù)的難度。近年來(lái),我國(guó)商業(yè)銀行的個(gè)人信貸業(yè)務(wù)迅猛發(fā)展,個(gè)人信貸市場(chǎng)的規(guī)模和信貸業(yè)務(wù)的多樣性都有著顯著的提高。個(gè)人信貸業(yè)務(wù)作為銀行盈利來(lái)源的同時(shí),也存在不良貸款和壞賬損失的情況。如何通過(guò)商業(yè)銀行掌控的數(shù)據(jù)資源進(jìn)行個(gè)人信用違約評(píng)估,降低不良貸款率和壞賬損失成為研究熱點(diǎn)。個(gè)人信用違約評(píng)估不僅可以幫助商業(yè)銀行消除信息不對(duì)稱和管理上存在的風(fēng)險(xiǎn),而且能夠提高商業(yè)銀行整體收益。面對(duì)海量數(shù)據(jù)給商業(yè)銀行信用風(fēng)險(xiǎn)管理工作帶來(lái)的挑戰(zhàn),數(shù)據(jù)處理技術(shù)的不斷更新也給商業(yè)銀行提供了更好的機(jī)遇。本文借助Logistic模型和數(shù)據(jù)挖掘技術(shù),在充分利用南京市某家城市商業(yè)銀行近30000例個(gè)人信貸業(yè)務(wù)數(shù)據(jù)的基礎(chǔ)上,對(duì)商業(yè)銀行個(gè)人信用違約風(fēng)險(xiǎn)進(jìn)行評(píng)估,并對(duì)個(gè)人信用違約概率進(jìn)行預(yù)測(cè)分析。文章以個(gè)人信用違約為研究對(duì)象,結(jié)合數(shù)據(jù)挖掘技術(shù)并充分利用數(shù)據(jù)挖掘軟件,建立個(gè)人信用違約評(píng)估模型,并針對(duì)商業(yè)銀行風(fēng)險(xiǎn)管理上存在的薄弱環(huán)節(jié)提出改進(jìn)措施。
[Abstract]:The development of network finance and virtual economy not only brings challenges and challenges to traditional banking business, but also provides unprecedented development opportunities for business.With the arrival of big data's era, the business information data of commercial banks are increasing explosively every day, which not only makes it difficult to store the data, but also greatly increases the difficulty for commercial banks to use the data.In recent years, the personal credit business of commercial banks in China has developed rapidly, and the scale of personal credit market and the diversity of credit business have improved significantly.Personal credit business as a source of bank profits, but also bad loans and bad debt losses.How to evaluate personal credit default through the data resources controlled by commercial banks and reduce non-performing loan rate and bad debt loss has become a hot research topic.Personal credit default assessment can not only help commercial banks eliminate information asymmetry and management risks, but also improve the overall income of commercial banks.In the face of the challenges brought by the massive data to the credit risk management of the commercial banks, the continuous updating of the data processing technology also provides a better opportunity for the commercial banks.With the help of Logistic model and data mining technology, this paper evaluates the personal credit default risk of commercial banks on the basis of making full use of nearly 30000 personal credit business data of a city commercial bank in Nanjing.The probability of personal credit default is predicted and analyzed.This paper takes personal credit default as the research object, combines the data mining technology and makes full use of the data mining software, establishes the personal credit default evaluation model, and puts forward the improvement measures in view of the weak link in the commercial bank risk management.
【學(xué)位授予單位】：南京財(cái)經(jīng)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：F832.33;TP311.13

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