【作者】 于飞鸣；

【导师】 刘衍珩；

【作者基本信息】 吉林大学 ， 软件工程， 2009， 硕士

【摘要】 数据挖掘技术是一门运用了人工智能、机器学习、统计学等多个领域理论和技术的新兴交叉学科,可以为企业提取隐含在大量历史数据中,但却潜在有用的信息和知识。准确运用这项技术,可以为企业决策提供强大有力的支持。数据挖掘技术发展到今天,已经日臻成熟,被广泛应用于金融、电信、保险、电力等多个领域并取得丰硕成果。本文运用数据挖掘技术,提出了一个基于数据挖掘的信贷分析系统的设计和实现方法。并通过具体的数据挖掘实验,对信贷业务数据进行挖掘,并对挖掘结果进行了解释与评估,证明了挖掘模型的可行性和有效性。更多还原

【Abstract】 In the past, due to the constraints of the level of data processing means, databases capacity, computer running speed and so on, the branches of domestic commercial banks have their own customer information databases, credit information databases and report the raw data after a simple statistics summary. Such lagging analytical methods and tools can only provide superficial credit business data for the upper leaders to make decisions. Because managers are unable to fully grasp the internal and external information and lack of information exchange, they can’t correctly evaluate the credit assets risks which lead to wrong decisions making.At present there are a variety of information systems in China’s banking industry which generally are used to complete a wide range of counter services, such as savings system, accounting systems, credit card system, etc. Some banks are developing the comprehensive counter business system that integrates all kinds of counter businesses, focusing on improving the management efficiency of business operating. As long as we observe and analyze the systems of all banks, we will find the contents, models and basic functions of various systems are same and at most the selected hardware and software platform are different, so the large capital all banks invested are repeated constructions. All banks did not outsource transaction processing system, not break away from the tie of transaction processing, not pay attention or never query and analyze the existing customer information in order to identify potentially useful information. For a long time all banks stay on the scale benefit stage by enlarging scale and preempting sites to obtain the scale benefit. Through the organization establishment, branch setting, and personnel inputs, they find the real output benefits did not reach the desired effect. Currently competition becomes more severe with the increasing number of domestic financial institutions and foreign banks that have compete for China’s market, scale expansion is no longer an effective management tool. Major banks will have to turn their attention to mining and re-use of information to pursuit the depth benefits. Banks must shift from the blindfold hardware investment to the purposive software investment, pay attention to customer relationships and customer value, risk management but not the quantity of transactions, keep long-term relationships with key customer , attract and lock specific client base; pay attention to customer orientation and customer information analysis but not the casting-net-style business extension, accurately choose a separate customer base by the analytical tools and experience, sell different bank products and service to different customers purposefully. These have become urgent problems need to solve in the process of information construction for domestic commercial banks ,such as how to effectively make use of such a large quantity of business data and supply effective intelligent support for business decision-making; how to establish application system such as business analysis and forecasting on the base of processing information to provide accurate and efficient decision-making support services for banking staff . More and more people have recognized these problems, and made a lot thought and research on data mining applications in the banking business.The rise and development of data mining technology provided a new starting point for the information construction of banking industry. Data mining uses of cluster analysis, neural networks, decision trees and other technologies to extract potential, unknown useful information, patterns and trends from large amounts of data by means of artificial intelligence and advanced statistical techniques. At present, management based on data mining have been widely used in many advanced enterprises. In banking industry , as bank products have a fairly homogeneity, so the difference among banks often lies in which bank controls the customer relationship, as well as vast amounts of business and the unique business rules behind customer information, and it can make decision scientifically decision-making .While this is just the problem that the data mining technology will solve. The combination of data warehouse and data mining has been the research focus to solve this problem.This article describes the concepts of data mining, data warehouse and the main techniques and methods to use; analyzes the present situation of data management and application in China’banking industry; summarizes the construction approaches of subject-oriented banking data warehouse ; discusses the construction methods of system models of customer classification, risk prediction and performance evaluation on the demand background of China Construction Bank Hongshan branch. Establishing data warehouse is the base of performance evaluation, customer classification and risk prediction. This article uses Microsoft SQL Server 2000 and Analysis Services data warehouse solutions. In the system of customer classification and risk prediction , we use method of MS Decision Tree provided by Analysis Services to generate decision tree. Through this decision tree, a simple prediction strategy can form. This rule used for judging new customers ,we can quickly get a rough classification results, and then predict risk. In addition, the article discusses the decision tree generation algorithms and pruning algorithms, and analyzes the advantages and disadvantages of a general decision tree method and some improved algorithm. The use of multi-dimensional sequential pattern method can analyze and forecast the customer’s behavior sequence models according to the customer in the past records of transactions and customer basic information , and achieve the purpose of risk prediction at the same. This paper presents the PFP-tree-based multi-dimensional sequential pattern mining method that is FP-Tree Algorithm, and at last proves the effective, compactness and completeness of the PFP-tree method. In the customer Profitability system, we use the OLAP data warehouse approach based on the theme of customer manager, as well as the agencies. This method is based on the multi-dimensional data sets of data warehouse, and Analysis Services provides "deepening", "shallow" and a series of analysis tools for data.更多还原