【摘要】：隨著金融自由化、經(jīng)濟(jì)全球化和金融創(chuàng)新的發(fā)展,商業(yè)銀行信用風(fēng)險(xiǎn)管理日益迫切與重要。作為社會(huì)重要的資金樞紐,商業(yè)銀行以信貸支持低碳經(jīng)濟(jì)的同時(shí)面臨由環(huán)境和社會(huì)危機(jī)引發(fā)的企業(yè)信貸風(fēng)險(xiǎn)。綠色信貸的提出為商業(yè)銀行風(fēng)險(xiǎn)管理提供了新的方向和指標(biāo),它能夠幫助商業(yè)銀行評(píng)估和管理項(xiàng)目融資過程中所涉及的環(huán)境風(fēng)險(xiǎn)和社會(huì)風(fēng)險(xiǎn),同時(shí)達(dá)到了應(yīng)用經(jīng)濟(jì)手段去實(shí)現(xiàn)環(huán)境和社會(huì)可持續(xù)發(fā)展的目的。 長期以來,我國商業(yè)銀行信用風(fēng)險(xiǎn)管理體系不健全,沒有有效的信用風(fēng)險(xiǎn)模型來控制和衡量企業(yè)的違約風(fēng)險(xiǎn)。因此,建立銀行信貸風(fēng)險(xiǎn)評(píng)估模型和有效的企業(yè)信用評(píng)估體系是國內(nèi)銀行共同面臨的研究課題�，F(xiàn)今,隨著信息技術(shù)的不斷發(fā)展,人工智能方法和機(jī)器學(xué)習(xí)模型被應(yīng)用于解決信用評(píng)級(jí)問題,本文嘗試用支持向量機(jī)方法對(duì)商業(yè)銀行綠色信貸信用評(píng)級(jí)做一些探討和研究。 本文首先介紹了信用風(fēng)險(xiǎn)的相關(guān)概念,從而引出了信用風(fēng)險(xiǎn)管理；又對(duì)國內(nèi)外常用的信用評(píng)級(jí)理論和方法進(jìn)行了介紹和綜述,并分析了我國商業(yè)銀行信用評(píng)估及實(shí)踐綠色信貸的現(xiàn)狀與不足,接著概述了支持向量機(jī)的理論基礎(chǔ)。最后,在理論探討的基礎(chǔ)上進(jìn)行實(shí)證分析,建立了基于綠色信貸的信用評(píng)級(jí)綜合指標(biāo)體系,構(gòu)建了基于支持向量機(jī)算法的一個(gè)信用評(píng)估模型,使用Matlab軟件為平臺(tái)實(shí)現(xiàn)對(duì)企業(yè)的二分類。 本文采用了理論剖析和實(shí)證研究并重的研究方法。將影響商業(yè)銀行信用風(fēng)險(xiǎn)的貸款企業(yè)作為主要的研究對(duì)象,然后采用上市公司數(shù)據(jù)進(jìn)行了實(shí)證分析,應(yīng)用支持向量機(jī)方法建立了全面的商業(yè)銀行信用評(píng)級(jí)模型。模型運(yùn)行后得出如下結(jié)論：SVM模型的信用分類能力不錯(cuò)；采用了綠色信貸的信用評(píng)級(jí)模型的評(píng)級(jí)分類準(zhǔn)確率更優(yōu)；linear核函數(shù)是一種效果比較好的核函數(shù)。最后本文對(duì)未來的研究及綠色信貸的發(fā)展進(jìn)行了展望與建議。
[Abstract]:With the development of financial liberalization, economic globalization and financial innovation, credit risk management of commercial banks becomes increasingly urgent and important. As an important social capital hub, commercial banks face the credit risk caused by environmental and social crisis while supporting low-carbon economy with credit. Green credit provides a new direction and indicators for the risk management of commercial banks. It can help commercial banks to assess and manage the environmental and social risks involved in the process of project financing. At the same time, the application of economic means to achieve sustainable development of the environment and society. For a long time, the credit risk management system of commercial banks in our country is not perfect, and there is no effective credit risk model to control and measure the default risk of enterprises. Therefore, the establishment of a bank credit risk assessment model and an effective enterprise credit evaluation system are common research topics faced by domestic banks. Nowadays, with the development of information technology, artificial intelligence method and machine learning model are applied to solve the problem of credit rating. This paper attempts to use support vector machine method to do some research on green credit rating of commercial banks. This paper first introduces the related concepts of credit risk, thus leads to the management of credit risk, and then introduces and summarizes the common theories and methods of credit rating at home and abroad. The present situation and deficiency of credit evaluation and practice of green credit of commercial banks in China are analyzed, and then the theoretical basis of support vector machine is summarized. Finally, on the basis of theoretical analysis, a credit rating index system based on green credit is established, and a credit evaluation model based on support vector machine (SVM) algorithm is constructed. Using Matlab software as the platform to achieve the two-classification of enterprises. This paper adopts the research method of both theoretical analysis and empirical research. This paper takes the loan enterprises which affect the credit risk of commercial banks as the main research object, then uses the listed company data to carry on the empirical analysis, uses the support vector machine method to establish the comprehensive commercial bank credit rating model. After the operation of the model, the following conclusions are drawn: the SVM model has good credit classification ability; the credit rating model with green credit has better classification accuracy; and the linear kernel function is a better kernel function. Finally, the future research and the development of green credit are prospected and suggested.
【學(xué)位授予單位】：中南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：F832.4

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