Combined rough set theory and flow network graph to predict customer churn in credit card accounts

Customer churn has become a critical issue, especially in the competitive and mature credit card industry. From an economic and risk management perspective, it is important to understand customer characteristics in order to retain customers and differentiate high-quality credit customers from bad ones. However, studies have not yet adequately introduced rules based on customer characteristics and churn forms of original data. This study uses rough set theory, a rule-based decision-making technique, to extract rules related to customer churn; then uses a flow network graph, a path-dependent approach, to infer decision rules and variables; and finally presents the relationships between rules and different kinds of churn. An empirical case of credit card customer churn is also illustrated. In this study, we collect 21,000 customer samples, equally divided into three classes: survival, voluntary churn and involuntary churn. The data from these samples includes demographic, psychographic and transactional variables for analyzing and segmenting customer characteristics. The results show that this combined model can fully predict customer churn and provide useful information for decision-makers in devising marketing strategy.     

Neural fraud detection in credit card operations

This paper presents an online system for fraud detection of credit card operations based on a neural classifier. Since it is installed in a transactional hub for operation distribution, and not on a card-issuing institution, it acts solely on the information of the operation to be rated and of its immediate previous history, and not on historic databases of past cardholder activities. Among the main characteristics of credit card traffic are the great imbalance between proper and fraudulent operations, and a great degree of mixing between both. To ensure proper model construction, a nonlinear version of Fisher's discriminant analysis, which adequately separates a good proportion of fraudulent operations away from other closer to normal traffic, has been used. The system is fully operational and currently handles more than 12 million operations per year with very satisfactory results.     

Neural network application in predicting advanced manufacturing technology implementation performance

Advanced Manufacturing Technology (AMT) adoption can be complex, costly, and risky. Companies need to assess and evaluate their current conditions with that of AMT requirements to identify the gaps and predict their performance. Such an approach will facilitate companies not only in their investment decisions, but on the actions needed to improve performance. The lack of such an approach prompted this study to develop an Artificial Neural Network (ANN) classification and prediction model that can assist companies especially Small and Medium size Enterprises (SMEs) in evaluating AMT implementation. Data were collected from a survey of 140 SMEs. Using cluster analysis, the companies were classified into three groups based on their performance. Then, a feed-forward NN was developed and trained with back-propagation algorithm. The results showed that the model can classify companies with 72% accuracy rate into the three clusters. This model is suitable to evaluate AMTs implementation outcomes and predict company performance as high, low, or poor in technology adoption.     

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.     

基于银行账户亲密度网络推理的团伙预测研究

近年来,针对涉众型非法金融活动在资金交易规律的研究引起了研究者的高度关注。为解决利用银行交易数据进行异常账户犯罪团伙主动发现的问题,提出一种基于银行账户非对称亲密度网络的团伙预测方法。首先,建立银行账户交易通用网络模型,将时序交易数据嵌入网络结构中。然后,利用节点的直接和间接交易关系信息,提出一种账户非对称亲密度计算方法。最终,利用节点在亲密度网络上的非对称交互信息,得到节点的异常倾向性指标。在包含传销团伙的真实数据上的实验结果表明,基于亲密度网络的团伙预测方法能有效发现潜在传销人员。     

Predicting repayment of the credit card debt

The recent economic crisis not only reduces the profit of retailer stores but also incurs the significant losses caused by increasing the late-payment rate of credit cards. Under this pressure, the scope of credit prediction needs to be broadened to the customer management after delinquency occurs. In doing so, this study clusters the credit card debtors in a retail company into homogeneous segments by using a self-organizing map. This study then develops credit prediction models to recognize the repayment patterns of each segment by using a Cox proportional hazard analysis. The credit prediction models are evaluated and the managerial implications of the study are provided.     

Feature engineering strategies for credit card fraud detection

Every year billions of Euros are lost worldwide due to credit card fraud. Thus, forcing financial institutions to continuously improve their fraud detection systems. In recent years, several studies have proposed the use of machine learning and data mining techniques to address this problem. However, most studies used some sort of misclassification measure to evaluate the different solutions, and do not take into account the actual financial costs associated with the fraud detection process. Moreover, when constructing a credit card fraud detection model, it is very important how to extract the right features from the transactional data. This is usually done by aggregating the transactions in order to observe the spending behavioral patterns of the customers. In this paper we expand the transaction aggregation strategy, and propose to create a new set of features based on analyzing the periodic behavior of the time of a transaction using the von Mises distribution. Then, using a real credit card fraud dataset provided by a large European card processing company, we compare state-of-the-art credit card fraud detection models, and evaluate how the different sets of features have an impact on the results. By including the proposed periodic features into the methods, the results show an average increase in savings of 13%.     

防止信用卡欺诈的系统设计

本文探讨了数据挖掘技术在银行系统中的应用,并设计了一个信用卡反欺诈系统。文内首先针对金融欺诈的巨大危害性,论述实施反欺诈系统的必要性,详述了银行交易系统的功能划分和反欺诈系统的具体设计,其中着重阐述采用贝叶斯分类器对海量的信用卡客户数据分类,预测该用户行为是否欺诈并做出及时处理。     

Neural network performance on the bankruptcy classification problem

Due to the recent changes in the world economy and as more firms, large and small, seem to fail now than ever, the bankruptcy classification problem is of increasing importance. Unfortunately, there are no easy-to-use and accurate tools to help make bankruptcy classification decisions. In this study, artificial neural network (ANN) technology is used to predict the going concern of firms based on financial ratios of 300 companies. The results indicate that ANN is as accurate or more accurate as a multiple regression model in predicting bankruptcy in addition to being easier to use and readily adapting to the changing environment.     

Transaction aggregation as a strategy for credit card fraud detection

The problem of preprocessing transaction data for supervised fraud classification is considered. It is impractical to present an entire series of transactions to a fraud detection system, partly because of the very high dimensionality of such data but also because of the heterogeneity of the transactions. Hence, a framework for transaction aggregation is considered and its effectiveness is evaluated against transaction-level detection, using a variety of classification methods and a realistic cost-based performance measure. These methods are applied in two case studies using real data. Transaction aggregation is found to be advantageous in many but not all circumstances. Also, the length of the aggregation period has a large impact upon performance. Aggregation seems particularly effective when a random forest is used for classification. Moreover, random forests were found to perform better than other classification methods, including SVMs, logistic regression and KNN. Aggregation also has the advantage of not requiring precisely labeled data and may be more robust to the effects of population drift.     

贷记卡系统设计与实现

介绍了已成功开发并推广应用的中国建设银行湖南省分行贷记卡系统的设计目标和设计原则，系统技术实现与系统运行环境，系统功能模块设计和系统特点。     

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.     

Sequential credit card fraud detection: A joint deep neural network and probabilistic graphical model approach

With the wide usage of e-banking in recent years, and by increased opportunities for fraudsters subsequently, we are witnessing a loss of billions of Euros worldwide due to credit card fraud every year. Therefore, credit card fraud detection has become a critical necessity for financial institutions. Several studies have used machine learning techniques for proposing a method to address the problem. However, most of them did not take into account the sequential nature of transactional data. In this paper, we proposed a novel credit card fraud detection model using sequence labelling based on both deep neural networks and probabilistic graphical models (PGM). Then by using two real-world datasets, we compared our model with the baseline model and examined how considering hidden sequential dependencies among transactions and also among predicted labels can improve the results. Moreover, we introduce a novel undersampling algorithm, which helps to maintain the sequential patterns of data during the random undersampling process. Our experiments demonstrate that this algorithm achieves promising results compared to the state-of-the-art methods in oversampling and undersampling.     

A hybrid neural network approach for credit scoring

The development of an effective credit scoring model has become a very important issue as the credit industry is confronted with ever‐intensifying competition and aggravating bad debt problems. During the past few years, a substantial number of studies in the field of statistics have been conducted to improve the accuracy of credit scoring models. In order to refine the classification and decrease misclassification, this paper presents a two‐stage model. Focusing on classification, the first stage aims at constructing an artificial neural network (ANN)‐based credit scoring model to categorize applicants into the group of accepted (good) credit and the group of rejected (bad) credit. Switching from classification to reassignment, the second stage proceeds to reduce the Type I error by retrieving the originally rejected good credit applicants to conditional acceptance using the Case‐Based Reasoning (CBR) classification technique. The proposed model (RST–ANN–CBR) is applied to a credit card dataset to verify its effectiveness. As the results indicate, the proposed model is able to achieve more accurate credit scoring than four other methods; more importantly, it is validated to recover potentially lost customers and to increase business revenues.     

Neural network techniques for financial performance prediction: integrating fundamental and technical analysis

This research project investigates the ability of neural networks, specifically, the backpropagation algorithm, to integrate fundamental and technical analysis for financial performance prediction. The predictor attributes include 16 financial statement variables and 11 macroeconomic variables. The rate of return on common shareholders' equity is used as the to-be-predicted variable. Financial data of 364 S&P companies are extracted from the CompuStat database, and macroeconomic variables are extracted from the Citibase database for the study period of 1985–1995. Used as predictors in Experiments 1, 2, and 3 are the 1 year's, the 2 years', and the 3 years' financial data, respectively. Experiment 4 has 3 years' financial data and macroeconomic data as predictors. Moreover, in order to compensate for data noise and parameter misspecification as well as to reveal prediction logic and procedure, we apply a rule extraction technique to convert the connection weights from trained neural networks to symbolic classification rules. The performance of neural networks is compared with the average return from the top one-third returns in the market (maximum benchmark) that approximates the return from perfect information as well as with the overall market average return (minimum benchmark) that approximates the return from highly diversified portfolios. Paired t tests are carried out to calculate the statistical significance of mean differences. Experimental results indicate that neural networks using 1 year's or multiple years' financial data consistently and significantly outperform the minimum benchmark, but not the maximum benchmark. As for neural networks with both financial and macroeconomic predictors, they do not outperform the minimum or maximum benchmark in this study. The experimental results also show that the average return of 0.25398 from extracted rules is the only compatible result to the maximum benchmark of 0.2786. Consequentially, we demonstrate rule extraction as a postprocessing technique for improving prediction accuracy and for explaining the prediction logic to financial decision makers.     

信用卡风险防范与监控系统的设计与实现

信用卡作为一种全新的支付手段和信用工具,已经被我国的广大消费者所接受和采用.但是伴随着国内信用卡业务的跨越式发展,信用卡风险问题也随之出现并呈现出逐步扩大的趋势.通过对目前国内存在的信用卡风险现状进行剖析,在此基础上设计出了一个信用卡风险防范与监控系统模型,并且成功地对该系统进行了实现.     

Neural network algorithms for analyzing multidimensional time series for predicting events and their application to study of Sun-Earth relations

In the paper, an original neural network algorithm for analysis of time series is presented. This algorithm allows predicting the occurrence of a certain event and finding a time interval to which a phenomenon (a precursor or a cause of the event) belongs. The characteristics of the algorithm functioning are investigated applied to the study of the solar-terrestrial relationship. Yu. V. Orlov. Candidate in Physics and Mathematics. Researcher at the Institute of Nuclear Physics, Moscow State University. Scientific interests: neural networks, genetic algorithms, algorithms of pattern recognition and image analysis. Yu. S. Shugai. Researcher at the Institute of Nuclear Physics, Moscow State University. Scientific interests: neural networks, genetic algorithms, algorithms of pattern recognition and image analysis, algorithms of classification and prediction. I. G. Persiantsev. Professor, Doctor in Mathematics and Physics. Head of the Laboratory, Leading Researcher at the Institute of Nuclear Physics, Moscow State University Scientific interests: neural networks, genetic algorithms, algorithms of pattern recognition and image analysis, algorithms of classification and prediction, inverse problems. Laureate of the USSR State Prize. S. A. Dolenko. Candidate in Physics and Mathematics. Senior Researcher at the Institute of Nuclear Physics, Moscow State University. Scientific interests: neural networks, genetic algorithms, algorithms of pattern recognition and image analysis, algorithms of classification and prediction, inverse problems.     

Neural network guidance based on pursuit-evasion games with enhanced performance

This paper addresses a neural network guidance based on pursuit-evasion games, and performance enhancing methods for it. Two-dimensional pursuit-evasion games solved by the gradient-based method are considered. The neural network guidance law employs the range, range rate, line-of-sight rate, and heading error as its input variables. Additional pattern selection methods and a hybrid guidance method are proposed for the sake of the interception performance enhancement. Numerical simulations are accompanied for the verification of the neural network approximation, and of the improved interception performance by the proposed methods. Moreover, all proposed guidance laws are compared with proportional navigation.