本文選題：企業(yè)違約風(fēng)險(xiǎn)分析　切入點(diǎn)：隱私保護(hù)　出處：《電子科技大學(xué)》2012年碩士論文

【摘要】：如何在保護(hù)數(shù)據(jù)私密性的前提下,更有效地發(fā)現(xiàn)具有潛在違約風(fēng)險(xiǎn)的企業(yè)貸款是銀行業(yè)和學(xué)術(shù)界的熱門(mén)研究方向。本文針對(duì)企業(yè)貸款數(shù)據(jù)分布不平衡的特征,提出了一套在保護(hù)隱私前提下的綜合使用多目標(biāo)決策方法選擇最優(yōu)分類預(yù)測(cè)模型的方法。 本次研究所提出的方法在預(yù)處理部分主要是為了使得整個(gè)研究方法在實(shí)際操作中的原始數(shù)據(jù)更加干凈,剔除因?yàn)閿?shù)據(jù)原因造成的不良影響。進(jìn)行維度規(guī)約則主要是為了防止進(jìn)行數(shù)據(jù)分析操作時(shí),一些敏感核心數(shù)據(jù)可能會(huì)泄露,從而對(duì)銀行和客戶造成重大損失。整個(gè)方法的數(shù)據(jù)分析部分則采取不平衡數(shù)據(jù)的過(guò)采樣、分類算法選擇、分類評(píng)價(jià)指標(biāo)選擇、多目標(biāo)決策方法選擇等步驟,構(gòu)建一個(gè)合適的分類預(yù)測(cè)模型,用以幫助銀行提高分辨貸款違約企業(yè)的準(zhǔn)確率,降低銀行的貸款風(fēng)險(xiǎn),提高銀行的收益。 我們以中國(guó)某國(guó)有銀行四川省分行的企業(yè)貸款數(shù)據(jù)為例,以PCA作為隱私保護(hù)的一種方法。同時(shí)我們又得到了在加權(quán)情況下,最優(yōu)多目標(biāo)決策方法TOPSIS的排序要比沒(méi)有進(jìn)行分類評(píng)估指標(biāo)加權(quán)來(lái)的更加真實(shí)可信。
[Abstract]:On the premise of protecting the privacy of data, it is a hot research direction of banking and academic circles to find the enterprise loan with potential default risk more effectively.In view of the unbalanced distribution of enterprise loan data, this paper proposes a comprehensive multi-objective decision making method to select the optimal classification and prediction model under the premise of privacy protection.The main purpose of this research is to make the raw data of the whole research method cleaner in practice and eliminate the bad effects caused by the data.The main purpose of dimension specification is to prevent some sensitive core data from leaking, which will cause great losses to banks and customers.In the data analysis part of the whole method, a suitable classification and prediction model is constructed by taking steps such as over-sampling of unbalanced data, selection of classification algorithm, selection of classification evaluation index, selection of multi-objective decision method, etc.In order to help banks to improve the accuracy of the resolution of loan default enterprises, reduce the bank's loan risk, improve the bank's income.We take the enterprise loan data from Sichuan branch of a state-owned bank in China as an example and use PCA as a privacy protection method.At the same time, we get that the ranking of the optimal multi-objective decision method TOPSIS is more real and credible than that without the weighted classification evaluation index.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：F832.4;F224

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

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