本文關(guān)鍵詞：決策樹算法在農(nóng)村信用社農(nóng)戶信用評級中的應(yīng)用　出處：《湖南大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 信用評分 決策樹 SAS

【摘要】：農(nóng)村信用合作社（農(nóng)村合作銀行、農(nóng)村商業(yè)銀行，以下簡稱“農(nóng)信社”）長期以來作為農(nóng)村金融的主力軍，在服務(wù)農(nóng)業(yè)生產(chǎn)者和小商品生產(chǎn)者，支持農(nóng)業(yè)發(fā)展方面發(fā)揮了無可替代的作用，在解決農(nóng)戶貸款難，促進農(nóng)民增收,支持農(nóng)村經(jīng)濟發(fā)展和農(nóng)業(yè)現(xiàn)代化等方面發(fā)揮了重要而積極的作用，但其自身不良貸款率仍然是商業(yè)銀行的5-7倍。因此，客觀、全面、準(zhǔn)確的評估農(nóng)村客戶的還款能力和還款意愿，拒絕不符合條件的客戶，將是避免、控制、減少損失的一個重要手段。傳統(tǒng)的信用評分由于標(biāo)準(zhǔn)不一，具有成本高、主觀性強、效率低下的特點。社會經(jīng)濟的發(fā)展，農(nóng)戶概念的外延和內(nèi)涵都發(fā)生了較大改變，再用傳統(tǒng)的農(nóng)戶信用評價體系來運用到現(xiàn)在的農(nóng)戶概念上，不合時宜的。歐美國家的使用經(jīng)驗表明，個人信用評分具有快速處理客戶貸款申請，而且成本低、標(biāo)準(zhǔn)一致，比較客觀的特點，在銀行信用風(fēng)險管理中發(fā)揮重要的作用。歐美國家現(xiàn)代信用評分體系廣泛運用了統(tǒng)計學(xué)、運籌學(xué)、人工智能等方面的技術(shù)，在此基礎(chǔ)上形成的數(shù)據(jù)挖掘技術(shù)在信用評分模型的構(gòu)建中發(fā)揮著廣泛而且重要的作用。 本文首先介紹了國內(nèi)外學(xué)者研究個人信用評級的不同理論與方法，并簡要介紹了其優(yōu)缺點；其次分析了農(nóng)戶信用評級現(xiàn)狀，定性評價在評級過程中所占比重較大，因而建立一種定量的評級方法對降低貸款風(fēng)險具有重要意義；第三，介紹了本文研究所采用的的決策樹技術(shù)、數(shù)據(jù)挖掘方法以及評級工具——SAS；第四，本文收集了本銀行系統(tǒng)近4年的真實農(nóng)戶貸款樣本數(shù)據(jù)，利用SAS中的決策樹算法，通過數(shù)據(jù)清洗、轉(zhuǎn)換，抽樣、分析，建立決策樹模型，并對屬性進行賦值，，建立農(nóng)戶信用評分模型；第五，本文得出的農(nóng)戶信用評分模型采用百分制，不同的分值對應(yīng)相應(yīng)的信用等級，從而采取不同的信貸策略。 本文創(chuàng)新點在于將傳統(tǒng)信用評級的定量指標(biāo)由占比不到70%提高到94%，大大提升了農(nóng)戶信用評級的精確度，同時測試得出農(nóng)戶信用評級模型在高信用級別的客戶中具有較高的預(yù)測精度，對于中、低級信用級別的客戶精度還有待改善的觀點。
[Abstract]:Rural credit cooperatives (rural cooperative banks, rural commercial banks, hereinafter referred to as "rural credit cooperatives") have long been the main force in rural finance, serving agricultural producers and small commodity producers. It has played an irreplaceable role in supporting agricultural development and has played an important and active role in solving the difficulties of farmers' loans, promoting farmers' income, and supporting rural economic development and agricultural modernization. But its own non-performing loan rate is still 5-7 times of commercial banks. Therefore, objective, comprehensive and accurate evaluation of rural customers' repayment ability and repayment will be avoided and controlled. The traditional credit rating has the characteristics of high cost, high subjectivity, low efficiency and the development of social economy. The extension and connotation of the concept of peasant household have changed greatly. It is inappropriate to use the traditional credit evaluation system of farmers to the present concept of farmers. The experience of European and American countries shows that. Personal credit rating has the characteristics of rapid processing of customer loan applications, low cost, consistent standards, and more objective characteristics. In the bank credit risk management plays an important role. Europe and the United States modern credit scoring system has been widely used in statistics, operational research, artificial intelligence and other aspects of technology. On this basis, data mining technology plays an important and extensive role in the construction of credit scoring model. This paper first introduces the different theories and methods of personal credit rating, and briefly introduces its advantages and disadvantages. Secondly, the paper analyzes the present situation of farmers' credit rating, and the qualitative evaluation occupies a large proportion in the process of rating, so it is of great significance to establish a quantitative rating method to reduce the risk of loans. Thirdly, this paper introduces the decision tree technology, data mining method and rating tool, which are used in this paper. In 4th, this paper collects the real farmer loan sample data of the bank system in the past 4 years, using the decision tree algorithm in SAS, through data cleaning, transformation, sampling, analysis, building decision tree model. The attribute is assigned and the credit scoring model is established. In 5th, the credit rating model of farmers adopted the percent system, and the different scores correspond to the corresponding credit grade, thus adopting different credit strategies. The innovation of this paper lies in increasing the quantitative index of traditional credit rating from less than 70% to 94, which greatly improves the accuracy of farmers' credit rating. At the same time, it is concluded that the farmer credit rating model has high prediction accuracy among high credit grade customers, and the customer accuracy of middle and low credit grade still needs to be improved.
【學(xué)位授予單位】：湖南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：F832.43;TP311.13

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