银行POS收单业务的数据分析和研究

阐述了从银行卡POS收单业务系统、外部数据源提取数据,过滤和归一数据,建立多维模型的数据仓库,进行信息处理、OLAP分析和数据挖掘的过程。通过分析商户特征,有目标地发展特约商户,实现银行手续费收益最大化。     

数据挖掘的关联分析在研究信用卡客户消费行为中的应用

介绍了数据挖掘技术的关联规则，并在信用卡客户消费行为的实际研究中加以运用。发现了基于日期的特约商户之间的和基于信用卡帐号的特约商户之间的关联规则，并提出了相应的营销战略，为国内商业银行开展有针对性的信用卡个性化营销提供了有益的启示。     

工行携手企业保网上安全

8月31日,联想、8848、首信等8家互联网公司共同与中国王商银行签署了合作协议,联合推出B2C牡丹卡网上支付业务。该业务是通过CA认证服务器发放特约商户证书和银行证书,数据加密采用基于128位的安全套接层协议(SSL)的安全方案,商户与银行之间建立     

银监会对信用卡非法套现说不

在信用卡非法套现活动大量出现，循环信用账户透支余额和循环信用使用户数猛增，不法中介进行空卡套现导致商业银行信用卡循环信用业务环节存在较大风险隐患的背景之下，中国银监会于近日下发一则《关于信用卡套现活跃风险提示的通知》，要求银行切实加强对信用卡透支额度、签约商户以及对持卡人领卡用卡行为的管理，从三方面着手严控信用卡非法套现行为。     

网状数据库系统设计的体会—银行信用卡实时数据库系统

TKIDONDS是我们为银行开发的信用卡数据库系统。本文主要介绍了银行信用卡的业务、数据库结构的设计以及实现过程中的一些体会。     

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。     

数据仓库在商业银行中的运用

随着信息系统在社会各企业中的广泛运用,特别是在银行业,信息系统数年中收集了海量数据,且数据还正以指数级增长,在这信息化的大潮中,银行高层都希望用数据分析来了解银行的经营状况和外部环境,做出科学的决策,在现代激烈的银行业竞争中占有一席之地。本文主要对数据仓库环境的组成、维度建模做一个简要的介绍,结合银行的特定业务,对相应的业务需求介绍银行信用卡数据仓库的基本构架,同时简要论述银行信用卡数据仓库对银行信用卡业务的巨大作用。通过一个基于银行数据仓库项目的完整流程,具体地描述了数据采集,数据转换和数据加载的具体实现方法。对一个具体的业务需求的分析,设计相应的维度模型,最终完成一个客户等级MIS,并输出相应的日报表。最后简要介绍数据仓库的深层应用。     

银行信用卡实时数据库系统的设计

本文结合我们为银行开发的ＴＫＩＤＯＮＤＳ信用卡数据库系统的实践，介绍了银行信用卡的管理业务，网状数据库系统及其结构设计以及实现过程中的一些体会。     

基于大型机的银行信用卡管理系统的设计与实现

采用三层架构模式对基于大型机的银行业务系统中的信用卡储蓄模块进行了整体的框架设计。该系统结合了批处理过程(Batch)和在线联机交易(Online),较好地实现了银行业务的自动化,将日常业务转化到主机系统上,实现了人机交互,为银行客户提供了基本的功能,以减少银行工作人员的工作量,并提供全天服务,提高了银行工作效率。通过对银行信用卡业务系统设计与实现的剖析,结合基于OS/390开发环境的实际经验,从应用系统整体架构的设计、系统需求分析和设计方面阐述了基于大型机银行核心业务信用卡应用系统的开发设计框架。     

中国19家银行信用卡服务调查

本调查由（（互联网周刊》与和讯网（www．hexun．com）联合奉献。本次测评共有19家银行信用卡业务参评,测评数据截止日期为2008年7月31日,数据采集方式以银行官方网站、客服电话咨询、各行账单为主。参评原则以银行发行的标准信用卡为主,就当前大众消费者在信用卡使用过程中最为关心的四个方面,即信用卡的各种收费（费用）、信用卡办理过程中的相关事务（卡务）、信用卡的功能（使用）、信用卡附带的各种增值服务,对国内发行信用卡的19家商业银行的信用卡业务进行测评。     

基于数据挖掘技术的信用卡审批模型研究

针对分类预测建模数据的非对称性,提出一种基于神经网络和决策树技术结合的非对称性数据集合预测分类建模方法,建立了信用卡审批模型.结果表明:增加预测类标识决策属性后,在用不同比例的建模数据集建立的所有模型中,比例为33.33%:66.67%的数据集建立的神经网络模型最好,模型的准确率达到88.49%.     

A cost-sensitive decision tree approach for fraud detection

With the developments in the information technology, fraud is spreading all over the world, resulting in huge financial losses. Though fraud prevention mechanisms such as CHIP&PIN are developed for credit card systems, these mechanisms do not prevent the most common fraud types such as fraudulent credit card usages over virtual POS (Point Of Sale) terminals or mail orders so called online credit card fraud. As a result, fraud detection becomes the essential tool and probably the best way to stop such fraud types. In this study, a new cost-sensitive decision tree approach which minimizes the sum of misclassification costs while selecting the splitting attribute at each non-terminal node is developed and the performance of this approach is compared with the well-known traditional classification models on a real world credit card data set. In this approach, misclassification costs are taken as varying. The results show that this cost-sensitive decision tree algorithm outperforms the existing well-known methods on the given problem set with respect to the well-known performance metrics such as accuracy and true positive rate, but also a newly defined cost-sensitive metric specific to credit card fraud detection domain. Accordingly, financial losses due to fraudulent transactions can be decreased more by the implementation of this approach in fraud detection systems.     

Credit card churn forecasting by logistic regression and decision tree

In this paper, two data mining algorithms are applied to build a churn prediction model using credit card data collected from a real Chinese bank. The contribution of four variable categories: customer information, card information, risk information, and transaction activity information are examined. The paper analyzes a process of dealing with variables when data is obtained from a database instead of a survey. Instead of considering the all 135 variables into the model directly, it selects the certain variables from the perspective of not only correlation but also economic sense. In addition to the accuracy of analytic results, the paper designs a misclassification cost measurement by taking the two types error and the economic sense into account, which is more suitable to evaluate the credit card churn prediction model. The algorithms used in this study include logistic regression and decision tree which are proven mature and powerful classification algorithms. The test result shows that regression performs a little better than decision tree.     

Neural network for predicting the performance of credit card accounts

This paper reports the interim results of an experimental project using neural networks as a decision support tool for credit card risk assessment within a major bank. Two prototype neural network systems have been developed: one which emulates the decisions of the current risk assessment system, and another which attempts to predict the performance of credit card accounts based on the accounts historical data. This paper focuses on the development of the neural network model for credit card account performance prediction. The study has shown that such a tool can help in discovering the potential problems with credit card applicants at the very early stage of the credit account life cycle.     

A Novel Cardholder Behavior Model for Detecting Credit Card Fraud

Because credit card fraud costs the banking sector billions of dollars every year, decreasing the losses incurred from credit card fraud is an important driver for the sector and end-users. In this paper, we focus on analyzing cardholder spending behavior and propose a novel cardholder behavior model for detecting credit card fraud. The model is called the Cardholder Behavior Model (CBM). Two focus points are proposed and evaluated for CBMs. The first focus point is building the behavior model using single-card transactions versus multi-card transactions. As the second focus point, we introduce holiday seasons as spending periods that are different from the rest of the year. The CBM is fine-tuned by using a real credit card transaction data-set from a leading bank in Turkey, and the credit card fraud detection accuracy is evaluated with respect to the abovementioned two focus points.     

Optimization strategy of credit line management for credit card business

Adjusting the credit lines of card users is an important issue. It is essential to establish an optimized approach for credit card companies to identify the proper amount of credit to offer for their customers. Most of the related research concentrated on the prediction of credit card users׳ default. Our contribution is a consideration of a holistic and heuristic approach that looks at the credit line that maximizes the net profits of the credit card companies. We first apply regression models to find the probability of default of customer and customer׳s current balance as a function of credit line. Next we use a regression tree to identify groups of customers assigned with the same credit line. The results are then used to formulate the net profit and genetic algorithm is used to find optimally adjusted credit line for each group of customers. It is expected that our study can contribute to present strategic guidelines for the management of credit lines for card companies.     

Securing on-line credit card payments without disclosing privacy information

Two revisions of the original Secure Electronic Transaction (SET) protocol are proposed to conceal cardholders' identities in the electronic marketplace in which cardholders' trust for banks can be reduced to a minimum. Constrained by being extensions of the existing card payment networks to the Internet, most on-line credit card payment schemes in use or proposed in recent papers assume the sensitive card information could be disclosed to all the participating banks. The assumption used to work well in traditional credit card payments before. However, negative impacts such as banking scandals, closure programs due to poor management, and security problems with Internet banking are all undermining cardholders' trust in banks. The issuer is the trusted bank selected by the cardholder, but the acquirer is not. To reveal the cardholder's sensitive card information to every possible acquirer implies potential risk. Based on the need-to-know principle, the two revisions are proposed to relax the assumption mentioned above.In our solutions, the sensitive card information is well protected along the way and can be extracted only by the issuer. A cardholder needs only to select a trustworthy issuer, instead of worrying about the possible breakdowns of every involved acquirer. The cost to achieve our more secure schemes demands only minor information modifications on the legacy system.     

How Safe is your Business from Credit Card Fraud?

Alan Timothy, CEO of data engineering consultancy Rocket ScienceAccording to Card Watch, the UK banking industry’s body set up to help fight plastic card crime, in 2001 credit card fraud cost the UK $411.4 million, of which $95.7 million was accounted for by cardholder-not-present transactions. In other words, the type of transactions businesses do with their customers either on the phone or on the Internet, when the card nor the card holder is present at the point of purchase.     

Applying genetic programming technique in classification trees

Classification problems are often encountered in many applications. In the past, classification trees were often generated by decision-tree methods and commonly used to solve classification problems. In this paper, we have proposed an algorithm based on genetic programming to search for an appropriate classification tree according to some criteria. The classification tree obtained can be transferred into a rule set, which can then be fed into a knowledge base to support decision making and facilitate daily operations. Two new genetic operators, elimination and merge, are designed in the proposed approach to remove redundancy and subsumption, thus producing more accurate and concise decision rules than that without using them. Experimental results from the credit card data also show the feasibility of the proposed algorithm.     

Integrated Decision Model for Credit Product Outsourcing

Integrated Decision Model for Credit Product Outsourcing The European banking landscape seems to be evolving from a highly vertically integrated banking landscape into a value network of specialized banks. This can also be seen in the credit product industry where consumer credit specialists take over portfolios from other banks. While decision models for IS, operations and production outsourcing are discussed in the literature, we lack a model for credit product outsourcing which reflects the change of risk capital as discussed in the literature about credit sales. In combining these two literature strands, we develop a model to determine whether it is beneficial to internally produce or externally source a credit product.