本文關(guān)鍵詞：光大銀行信貸風(fēng)險(xiǎn)分類決策支持系統(tǒng)的研究　出處：《西安理工大學(xué)》2006年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 知識(shí)管理 決策支持系統(tǒng) 人工智能 貸款風(fēng)險(xiǎn)分類

【摘要】： 在當(dāng)今的世界上,信息技術(shù)是提高競爭力和經(jīng)濟(jì)增長的關(guān)鍵,信息技術(shù)對(duì)經(jīng)濟(jì)運(yùn)作效率的提高、產(chǎn)業(yè)結(jié)構(gòu)變革和新興產(chǎn)業(yè)發(fā)展發(fā)揮著巨大作用。信貸風(fēng)險(xiǎn)是銀行業(yè)面臨的共同風(fēng)險(xiǎn),如何控制和管理信貸風(fēng)險(xiǎn)是銀行業(yè)一個(gè)永恒的主題。為了進(jìn)一步加強(qiáng)金融監(jiān)管,我國于1998年開始提出了與國際通行做法協(xié)調(diào)的貸款風(fēng)險(xiǎn)五級(jí)分類方法。并倡導(dǎo)采用現(xiàn)代化信息技術(shù)手段進(jìn)行風(fēng)險(xiǎn)防范控制研究,把提高信貸資產(chǎn)質(zhì)量、防范信貸風(fēng)險(xiǎn)作為金融控制的目標(biāo)。 光大銀行是全國性股份制商業(yè)銀行,經(jīng)過多年的發(fā)展,逐步形成了與現(xiàn)代商業(yè)銀行相適應(yīng)的多元化股權(quán)結(jié)構(gòu)、日益完善的公司經(jīng)營機(jī)制。然而,在其迅速發(fā)展的同時(shí),防范和化解資產(chǎn)風(fēng)險(xiǎn)的壓力也在不斷增加。一方面該行不良資產(chǎn)的歷史包袱沉重,另一方面,不良信貸資產(chǎn)的數(shù)量在不斷累加,其信貸風(fēng)險(xiǎn)的管理模式已不能適應(yīng)形勢的發(fā)展。研究應(yīng)用于信貸風(fēng)險(xiǎn)分類管理的決策支持系統(tǒng),以提高其的信貸風(fēng)險(xiǎn)管理水平,為科學(xué)決策和管理提供支持顯得尤為必要。 本文以光大銀行為背景,通過對(duì)知識(shí)管理、人工智能及決策支持系統(tǒng)理論的研究,并將人工智能的專家系統(tǒng)和神經(jīng)網(wǎng)絡(luò)技術(shù)相結(jié)合,提出了光大銀行信貸風(fēng)險(xiǎn)分類決策模型;構(gòu)建了以知識(shí)管理為基礎(chǔ)、人工智能技術(shù)為手段、面向貸款風(fēng)險(xiǎn)分類的信貸風(fēng)險(xiǎn)管理和控制研究的信貸風(fēng)險(xiǎn)分類管理決策支持系統(tǒng)。這對(duì)于提高光大銀行信貸資產(chǎn)管理質(zhì)量,增強(qiáng)自身競爭力,實(shí)現(xiàn)可持續(xù)發(fā)展意義重大。
[Abstract]:In today's world, information technology is the key to improve competitiveness and economic growth, information technology to improve the efficiency of economic operations. The transformation of industrial structure and the development of new industries play a great role. Credit risk is the common risk faced by the banking industry. How to control and manage credit risk is an eternal theme of banking. In order to further strengthen financial supervision. In 1998, China began to put forward a five-level classification method of loan risk, which is coordinated with international common practice, and advocated the use of modern information technology means to carry out risk prevention and control research to improve the quality of credit assets. Preventing credit risk is the target of financial control. Everbright Bank is a national joint-stock commercial bank, after many years of development, gradually formed with the modern commercial banks to adapt to the diversification of equity structure, increasingly perfect corporate management mechanism. At the same time of its rapid development, the pressure to prevent and resolve the risk of assets is also increasing. On the one hand, the bank has a heavy historical burden of non-performing assets, on the other hand, the amount of non-performing credit assets is accumulating. Its credit risk management model can not adapt to the development of the situation. This paper studies the decision support system applied to the classification of credit risk management in order to improve its credit risk management level. It is necessary to provide support for scientific decision-making and management. This paper takes Everbright Bank as the background, through the knowledge management, the artificial intelligence and the decision support system theory research, and unifies the artificial intelligence expert system and the neural network technology. The classification decision model of credit risk in Everbright Bank is put forward. Based on knowledge management and artificial intelligence technology as a means. Credit risk management decision support system based on credit risk management and control for credit risk classification, which can improve the quality of credit asset management and enhance the competitiveness of Everbright Bank. Achieving sustainable development is of great significance.
【學(xué)位授予單位】：西安理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2006
【分類號(hào)】：F832.4

【引證文獻(xiàn)】

相關(guān)期刊論文 前1條

1 丁文高;;我國商業(yè)銀行金融風(fēng)險(xiǎn)的分析[J];黑龍江科技信息;2009年02期

相關(guān)碩士學(xué)位論文 前2條

1 栗秋佳;基于質(zhì)量管理工具的交行包頭分行信貸風(fēng)險(xiǎn)管理研究[D];內(nèi)蒙古科技大學(xué);2010年

2 林鳳桐;金融數(shù)據(jù)統(tǒng)計(jì)及風(fēng)險(xiǎn)預(yù)警分析系統(tǒng)設(shè)計(jì)與實(shí)現(xiàn)[D];復(fù)旦大學(xué);2009年

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本文編號(hào)：1372327