银行卡客户群体聚类挖掘研究

银行卡业务利润丰厚.通过数据预处理,建立数据立方体,数据挖掘,分析客户群体特征,有目标地发展银行卡客户,使银行获得更大利益.     

基于案例推理的数据挖掘算法搜索策略的研究

扩大数据挖掘系统的使用人群,使普通用户能够方便地操作数据挖掘系统,是数据挖掘算法搜索策略的主要研究目标。建立案例库存储专家经验,采用面向对象的方法来表示案例库中的案例,利用模糊商空间来描述案例库的组织结构,结合统计启发式搜索技术实现案例检索,缩小检索范围,加快求解速度,提高了运行效率和准确率。以银行客户经理分析客户流失群体为例进行相应的操作,验证了案例推理数据挖掘算法搜索策略的准确性。     

基于数据挖掘技术的银行CRM系统的研究

数据挖掘技术的主要作用是进行客户的信息整合,揭示出潜在的关联性和规律,进行客户细分和沟通,为决策者制定决策提供参考,为客户提供个性化服务,从而提升银行的盈利与竞争力。客户关系管理一种新型的以客户中心的管理机制,银行客户关系管理系统利用数据挖掘技术实现有效的客户关系管理,能够提高市场竞争能力。本文在对客户关系管理和数据挖掘简要介绍的基础上,主要阐述了数据挖掘技术在银行客户关系管理系统的应用。     

数据挖掘在信用卡客户流失分析中的应用

银行信用卡业务属于高收益、高风险的业务,如何实现对信用卡的客户流失控制是发卡银行迫切需要解决的问题。目前,随着银行积累了大量的数据,并建立了数据仓库,使得采用数据挖掘技术来实现信用卡客户流失分析成为了可能。本文提出了银行信用卡领域内基于数据挖掘的决策分析流程:包括数据准备,数据理解和商业理解阶段,帮助信用卡业务部门分析和控制客户流失。     

决策树ID3算法在银行信贷业务中的应用

银行信贷业务是银行的一项重要业务,该业务存在一定的风险,如果我们能够预测客户的违约风险就可以最大程度的降低风险。数据挖掘技术可以很好的解决这一问题。将数据挖掘技术运用到银行客户信用评估,在庞大的数据中将看似无关联的数据进行筛选和净化,提取出有价值的信息,对客户贷款申请做出恰当的回应。数据挖掘是信息技术发展的必然结果,它是指通过挖掘算法从大量数据中抽取挖掘出隐含在其中的有价值的模式或规律等信息的复杂过程。其中,对数据的分类是数据挖掘领域研究的重要课题。本文通过决策树的经典算法ID3算法对银行信贷业务进行分析,并总结了该算法相对于其他算法的优缺点。     

基于WEKA的客户分类信息系统研究

本文采用决策树方法,对客户交易数据和客户基本信息进行数据挖掘分析,降低了数据冗余度,提高了数据集准确率。在RFM模型基础上,从客户交易信息中选取了购买频率和平均每次购买金额作为分类评估指标的补充,得到一组客户交易数据训练集。结合J48算法使用WEKA算法对客户交易数据训练集进行训练、测试和验证,构建了客户分类决策模型,从而有利于客户分类原型系统的系统分析和系统设计。     

数据挖掘技术在银行客户细分中的应用

当前银行的数据信息的管理工作,己从原来的管理系统发展成为当前的分析决策。银行数据需要通过数据仓库和数据挖掘技术细分银行客户群体、分析交易内在规律、保障银行服务等。该文阐述了细分银行的数据仓库的设计,并在此基础上使用聚类算法具体应用于细分用户市场,为银行业务的开展提供了数据依据。     

基于数据挖掘的银行客户关系管理系统的研究

数据挖掘是一种新兴的信息处理技术,客户关系管理是以客户为中心的管理机制和发展策略,银行客户关系管理系统将二者结合起来,能够提高市场竞争能力。本文对银行客户关系管理系统的系统架构进行了介绍,阐述了数据挖掘技术在银行客户关系管理系统的应用。     

基于协同过滤的银行产品推荐系统建模

通过分析银行产品推荐系统与一般推荐系统的区别,采用协同过滤算法,设计银行产品推荐系统模型.首先采用决策树、聚类等数据挖掘方法进行客户分类,提高系统伸缩能力和推荐效率;同时从客户和产品两方面对交易明细进行数据分析,避免早期数据冷起动问题.该系统模型最终生成的客户/产品/时间匹配矩阵,有效辅助银行的客户关系管理及市场营销活动.     

基于数据挖掘的银行CRM系统的研究

数据挖掘是一种深层次的信息处理技术,客户关系管理一种以客户为中心,旨在改善企业与客户关系的管理机制,银行客户关系管理系统将数据挖掘技术应用于客户关系的管理,有利于开拓新的产品种类,降低运营成本,提高市场竞争能力。本文对银行客户关系管理系统的系统构成进行了介绍,阐述了数据挖掘技术在银行客户关系管理系统的应用。     

A Data Mining Approach for Retailing Bank Customer Attrition Analysis

Deregulation within the financial service industries and the widespread acceptance of new technologies is increasing competition in the finance marketplace. Central to the business strategy of every financial service company is the ability to retain existing customers and reach new prospective customers. Data mining is adopted to play an important role in these efforts. In this paper, we present a data mining approach for analyzing retailing bank customer attrition. We discuss the challenging issues such as highly skewed data, time series data unrolling, leaker field detection etc, and the procedure of a data mining project for the attrition analysis for retailing bank customers. We use lift as a proper measure for attrition analysis and compare the lift of data mining models of decision tree, boosted naïve Bayesian network, selective Bayesian network, neural network and the ensemble of classifiers of the above methods. Some interesting findings are reported. Our research work demonstrates the effectiveness and efficiency of data mining in attrition analysis for retailing bank.     

基于MS聚类分析模型的数据挖掘应用探讨

研究数据挖掘算法中的Microsoft聚类算法以及其在金融领域的应用。从海量的数据里挖掘出潜在的信息是数据挖掘的主要工作,通过对客户交易信息的过滤和挖掘,建立起为银行更好地提供智能决策和建议数据挖掘商业应用实例系统。系统的客户端开发选择的是Visual Studio.NET 2008,并使用ADOMD.NET对象及Web控件对模型的结果进行输出展示。用户可以应用这个系统通过输入客户的一些个人属性以及办理业务的基本情况,查看所关心的信誉情况、业务的办理趋向、银行开展新业务的趋向等信息。在整个实例系统的构建过程中,对聚类分析模型的挖掘过程进行了详细的分析,促进了数据挖掘的应用实践。     

Mining fuzzy association rules in a bank-account database

This paper describes how we applied a fuzzy technique to a data-mining task involving a large database that was provided by an international bank with offices in Hong Kong. The database contains the demographic data of over 320,000 customers and their banking transactions, which were collected over a six-month period. By mining the database, the bank would like to be able to discover interesting patterns in the data. The bank expected that the hidden patterns would reveal different characteristics about different customers so that they could better serve and retain them. To help the bank achieve its goal, we developed a fuzzy technique, called fuzzy association rule mining II (FARM II). FARM II is able to handle both relational and transactional data. It can also handle fuzzy data. The former type of data allows FARM II to discover multidimensional association rules, whereas the latter data allows some of the patterns to be more easily revealed and expressed. To effectively uncover the hidden associations in the bank-account database, FARM II performs several steps which are described in detail in this paper. With FARM II, the bank discovered that they had identified some interesting characteristics about the customers who had once used the bank's loan services but then decided later to cease using them. The bank translated what they discovered into actionable items by offering some incentives to retain their existing customers.     

Adaptive Automated Teller Machines

Nowadays, Automated Teller Machines (ATMs) provide significant online support to bank customers. A limitation of ATM usage is that customers often have to wait in a queue, especially at ATMs installed at busy locations. Also, old people tend to consume more ATM usage time, possibly frustrating customers in the queue. In these situations, ATMs should “adapt” to the behavior of the customers to minimize the usage time. To this end, we apply data mining techniques to an ATM transaction dataset obtained from an international bank based in Kuwait. We pre-process this dataset, and convert it into a specific XML format to mine it through the ProM (process mining) tool. Our results reveal that customers withdraw money most frequently, followed by purchases (through an ATM card) and balance inquiry transactions. Customers re-do these transactions frequently, and also employ them one after the other. We acquire the distributions of the withdrawn amount, based on individual customers, the location (ATM terminal) and time of the withdrawl. Based on these results, we have proposed a set of five adaptive ATM interfaces, which show only frequent transactions and frequently-withdrawn amounts, display the current balance autonomously, and query explicitly for viewing purchase history, or for performing another withdrawl. An online survey on 216 ATM customers reveals that a majority of customers are willing to use these interfaces for minimizing their usage time. Our work has been approved by the banking authority of Pakistan, and we are currently implementing our interfaces for a Pakistani bank.     

An integrated data mining and behavioral scoring model for analyzing bank customers

Analyzing bank databases for customer behavior management is difficult since bank databases are multi-dimensional, comprised of monthly account records and daily transaction records. This study proposes an integrated data mining and behavioral scoring model to manage existing credit card customers in a bank. A self-organizing map neural network was used to identify groups of customers based on repayment behavior and recency, frequency, monetary behavioral scoring predicators. It also classified bank customers into three major profitable groups of customers. The resulting groups of customers were then profiled by customer's feature attributes determined using an Apriori association rule inducer. This study demonstrates that identifying customers by a behavioral scoring model is helpful characteristics of customer and facilitates marketing strategy development.     

基于AP聚类与随机森林的客户流失预测研究

利率市场化、大数据迅速发展,银行业均表现出明显的“二八定律”现象,20%的优质客户占据了银行的大部分资产。那么,如何防止银行客户流失,尤其是优质客户的流失,已经成为银行越来越关注的问题。因此,建立优质客户流失预警模型就显得尤为重要。以某商业银行为例,重新对客户流失进行定义,重点关注银行优质客户的流失预警,首先使用AP聚类算法进行属性选择,然后使用随机森林方法建立客户流失预警模型,预测零售优质客户未来3个月流失的可能性。为了验证该方法的有效性,首先在UCI数据集上进行验证,得到了较好的效果,然后使用该方法构建银行业优质客户流失预测模型,实验结果表明该模型的实际预测效果相较于一般的决策树方法,具有更高的准确性。     

支持向量机在银行贷款客户分类中的应用研究

随着客户关系管理系统的不断发展和应用,使用先进的算法进行客户分析变得越来越重要。尤其是象银行这种以客户为导向的行业,客户分析是十分必要的。当前,支持向量机方法作为一种统计学习理论的分类方法已经发展的比较成熟而且成功应用到了很多领域。文章解决的主要问题是对银行的客户数据根据其属性对客户进行分类,为银行的客户关系管理系统提供一种可靠的分类方法。文中主要介绍了银行的客户分类学习的过程和结果,如,客户数据清洗,数据预处理,SVM进行数据分类,多类分类处理,客户属性选择等问题。     

Discovering recency,frequency, and monetary (RFM) sequential patterns from customers’ purchasing data

In response to the thriving development in electronic commerce (EC), many on-line retailers have developed Web-based information systems to handle enormous amounts of transactions on the Internet. These systems can automatically capture data on the browsing histories and purchasing records of individual customers. This capability has motivated the development of data-mining applications. Sequential pattern mining (SPM) is a useful data-mining method to discover customers’ purchasing patterns over time. We incorporate the recency, frequency, and monetary (RFM) concept presented in the marketing literature to define the RFM sequential pattern and develop a novel algorithm for generating all RFM sequential patterns from customers’ purchasing data. Using the algorithm, we propose a pattern segmentation framework to generate valuable information on customer purchasing behavior for managerial decision-making. Extensive experiments are carried out, using synthetic datasets and a transactional dataset collected by a retail chain in Taiwan, to evaluate the proposed algorithm and empirically demonstrate the benefits of using RFM sequential patterns in analyzing customers’ purchasing data.     

Constructing credit auditing and control & management model with data mining technique

The 2008 financial tsunami, hitting the globe across all types of industries, causing tides of bankruptcies and severe unemployment, had its epicenter at American subprime in the housing market. In fact, the US subprime storm was just a premonition, while the root cause of the financial tsunami lied in the oversupply of structured credit products. Credit card business, one of the structured credit products, which under an intensively competitive environment, have been released by many banks with high spread, high return, and easy-to-apply appeals to carter to consumers needs. In order to allure the customers, some banks even go to the extent as simplify the credit rating, which in turn has increased credit risk, causing high non-performing ratio, increased debt collection cost, and growing bad debt counts. Accordingly, credit risk auditing plays a vital role in the successful management of credit card business. In response to such needs, the present study aims to conduct analysis and investigation on the current status of the industry with CRISP-DM model. First, customers’ demographic data and payment-related statistics were analyzed to identify feature variables, which were then sorted out as demographic data, debt data, payment rating etc. Next, by utilizing artificial neural network of data mining technique, the study tries to predict customer’s regular pattern of consumption, payment and/or default and bad debt, and to develop a set of credit granting principle by employing the decision tree technique. Since data mining classification model has a greater power in discriminating credit card granting, it can thus be used to construct accurate credit variable rules and predictive model, to further improve credit checking effect and credit risk control. Using the credit auditing data of a certain bank as a case study, the study intends to verify that the model constructed by the researcher can effectively identify the potential key factors of its credit card granting rule, to minimize the cost loss of Model I and Model II credit business, and eventually enhance the stability and profitability of the bank’s credit card business.     

A dynamic understanding of customer behavior processes based on clustering and sequence mining

In this paper, a novel approach towards enabling the exploratory understanding of the dynamics inherent in the capture of customers’ data at different points in time is outlined. The proposed methodology combines state-of-art data mining clustering techniques with a tuned sequence mining method to discover prominent customer behavior trajectories in data bases, which — when combined — represent the “behavior process” as it is followed by particular groups of customers. The framework is applied to a real-life case of an event organizer; it is shown how behavior trajectories can help to explain consumer decisions and to improve business processes that are influenced by customer actions.