基于數(shù)據(jù)挖掘的商業(yè)銀行CRM系統(tǒng)研究與設(shè)計
發(fā)布時間：2018-10-23 17:29
【摘要】：隨著科技的進步、互聯(lián)網(wǎng)的發(fā)展，CRM已經(jīng)從管理理論體系逐漸轉(zhuǎn)化為全新的商業(yè)理念。相關(guān)統(tǒng)計顯示，全球CRM系統(tǒng)軟件市場在2012年到2016年之間的年復(fù)合增長率預(yù)計達到9.09%。由此可見，企業(yè)對CRM系統(tǒng)的需求在不斷增長。在各行各業(yè)中，最早實施CRM系統(tǒng)的是金融業(yè)。隨著各種高端的信息技術(shù)被運用到CRM系統(tǒng)中，例如互聯(lián)網(wǎng)、人工智能、數(shù)據(jù)挖掘和數(shù)據(jù)倉庫等，金融行業(yè)憑借自身強大的技術(shù)支持，在CRM系統(tǒng)實施的過程中獲得了顯著成效。商業(yè)銀行是金融行業(yè)中最具有代表性的一支，其客戶之多更加顯示出對CRM系統(tǒng)的需求。隨著銀行數(shù)據(jù)的日積月累，數(shù)據(jù)倉庫逐漸取代了數(shù)據(jù)庫的地位，成為數(shù)據(jù)挖掘的主要信息載體被運用到CRM系統(tǒng)中。在商業(yè)銀行，數(shù)據(jù)增長的速度令人嘆為觀止，如何能夠有效的從海量數(shù)據(jù)中發(fā)現(xiàn)潛在的客戶信息成為當(dāng)今商業(yè)銀行關(guān)注的焦點。 本文針對上述背景進行商業(yè)銀行CRM系統(tǒng)設(shè)計。文中首先對國內(nèi)外商業(yè)銀行實施CRM系統(tǒng)的現(xiàn)狀進行研究，，而后針對系統(tǒng)設(shè)計涉及到的理論進行研究。文中針對銀行客戶分類算法從兩方面進行研究。一方面，本文提出一種基于決策樹的銀行客戶分類方法，對原先的方法與新提出的方法進行比較，發(fā)現(xiàn)新提出的方法在效率上占有一定的優(yōu)勢。另一方面，本文提出基于聚類的銀行客戶聚類算法，通過模糊聚類的方式對小樣本客戶進行分類，然后運用基于決策樹的銀行客戶分類算法對大樣本客戶進行全面的分類，并能夠確定分類原因。最后，通過對商業(yè)銀行CRM系統(tǒng)的需求分析，本文基于客戶統(tǒng)一視圖理論設(shè)計了商業(yè)銀行CRM系統(tǒng)的邏輯架構(gòu)、數(shù)據(jù)倉庫，并對CRM系統(tǒng)的主要業(yè)務(wù)模塊進行設(shè)計，將數(shù)據(jù)挖掘技術(shù)應(yīng)用到商業(yè)銀行CRM系統(tǒng)中。
[Abstract]:With the progress of science and technology and the development of Internet, CRM has been transformed from management theory system to new business idea. The global CRM software market is expected to grow at a compound annual rate of 9.09% between 2012 and 2016, according to statistics. Thus, the demand for CRM system is growing. In a variety of industries, the earliest implementation of the CRM system is the financial industry. With the application of various high-end information technologies to CRM systems, such as the Internet, artificial intelligence, data mining and data warehouse, the financial industry, with its own strong technical support, has achieved remarkable results in the implementation of the CRM system. Commercial bank is the most representative branch in the financial industry, and the number of customers shows the demand for CRM system. With the accumulation of bank data, data warehouse has gradually replaced the status of database and become the main information carrier of data mining in CRM system. In commercial banks, the speed of data growth is amazing, how to effectively find potential customer information from massive data has become the focus of commercial banks. This article carries on the commercial bank CRM system design according to the above background. In this paper, the current situation of commercial banks implementing CRM system at home and abroad is studied, and then the theory involved in the system design is studied. In this paper, the bank customer classification algorithm is studied from two aspects. On the one hand, this paper proposes a bank customer classification method based on decision tree, compares the original method with the new one, and finds that the new method has some advantages in efficiency. On the other hand, this paper proposes a clustering algorithm for bank customers, which classifies small sample customers by fuzzy clustering, and then makes a comprehensive classification of large sample customers by using the decision tree based bank customer classification algorithm. And can determine the reasons for classification. Finally, through the requirement analysis of commercial bank CRM system, this paper designs the logic structure, data warehouse and the main business module of CRM system based on the customer unified view theory. The data mining technology is applied to the commercial bank CRM system.
【學(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP311.13;F832.2
本文編號：2289974
[Abstract]:With the progress of science and technology and the development of Internet, CRM has been transformed from management theory system to new business idea. The global CRM software market is expected to grow at a compound annual rate of 9.09% between 2012 and 2016, according to statistics. Thus, the demand for CRM system is growing. In a variety of industries, the earliest implementation of the CRM system is the financial industry. With the application of various high-end information technologies to CRM systems, such as the Internet, artificial intelligence, data mining and data warehouse, the financial industry, with its own strong technical support, has achieved remarkable results in the implementation of the CRM system. Commercial bank is the most representative branch in the financial industry, and the number of customers shows the demand for CRM system. With the accumulation of bank data, data warehouse has gradually replaced the status of database and become the main information carrier of data mining in CRM system. In commercial banks, the speed of data growth is amazing, how to effectively find potential customer information from massive data has become the focus of commercial banks. This article carries on the commercial bank CRM system design according to the above background. In this paper, the current situation of commercial banks implementing CRM system at home and abroad is studied, and then the theory involved in the system design is studied. In this paper, the bank customer classification algorithm is studied from two aspects. On the one hand, this paper proposes a bank customer classification method based on decision tree, compares the original method with the new one, and finds that the new method has some advantages in efficiency. On the other hand, this paper proposes a clustering algorithm for bank customers, which classifies small sample customers by fuzzy clustering, and then makes a comprehensive classification of large sample customers by using the decision tree based bank customer classification algorithm. And can determine the reasons for classification. Finally, through the requirement analysis of commercial bank CRM system, this paper designs the logic structure, data warehouse and the main business module of CRM system based on the customer unified view theory. The data mining technology is applied to the commercial bank CRM system.
【學(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP311.13;F832.2
【參考文獻】
相關(guān)期刊論文 前10條
1 呂寶林;張同建;;我國商業(yè)銀行CRM戰(zhàn)略與銀行績效改進的相關(guān)性研究[J];金融理論與實踐;2010年05期
2 陳廣花;王正群;劉風(fēng);俞振州;;一種多變量決策樹的構(gòu)造與研究[J];計算機工程與應(yīng)用;2010年25期
3 李寶東,宋瀚濤;數(shù)據(jù)挖掘在客戶關(guān)系管理(CRM)中的應(yīng)用[J];計算機應(yīng)用研究;2002年10期
4 陳曉云;蘭聰花;;一種基于粗糙集的屬性值約簡方法[J];計算機應(yīng)用與軟件;2010年08期
5 胡侃,夏紹瑋;基于大型數(shù)據(jù)倉庫的數(shù)據(jù)采掘:研究綜述[J];軟件學(xué)報;1998年01期
6 王夢雪;;數(shù)據(jù)挖掘綜述[J];軟件導(dǎo)刊;2013年10期
7 隋莉萍;;銀行業(yè)幾種CRM應(yīng)用方案比較分析[J];現(xiàn)代管理科學(xué);2006年10期
8 方明;薛天助;;數(shù)據(jù)倉庫在銀行信息系統(tǒng)中的研究與應(yīng)用[J];信息與電腦(理論版);2011年02期
9 陳倩媚;;中國銀行業(yè)營銷模式發(fā)展回顧與展望[J];廣東農(nóng)工商職業(yè)技術(shù)學(xué)院學(xué)報;2010年02期
10 王旭剛;;數(shù)據(jù)倉庫技術(shù)在銀行數(shù)據(jù)系統(tǒng)中的設(shè)計與應(yīng)用[J];科技創(chuàng)新導(dǎo)報;2012年04期
本文編號：2289974
本文鏈接：http://www.wukwdryxk.cn/jingjilunwen/guojijinrong/2289974.html
最近更新
教材專著
