基于KM-SVMSMOTE-CNN的信用卡欺诈检测

针对信用卡欺诈检测中样本数据规模大, 计算复杂程度高, 数据分布极度不平衡等问题, 提出卷积神经网络(CNN)结合大规模信用卡交易数据进行欺诈检测, 同时为了解决交易数据的极端不平衡性问题, 使用K-means算法进行聚类, 结合支持向量机合成少数类过采样技术(SVMSMOTE)增加少数类样本数量, 最终构建一个KM-SVMSMOTE-CNN的信用卡交易欺诈预测模型. 选取Kaggle平台上发布的信用卡欺诈数据进行验证, 实验结果表明, 基于KM-SVMSMOTE-CNN的融合模型从整体上大大提高了信用卡欺诈检测的识别率.     

Employing transaction aggregation strategy to detect credit card fraud

Credit card fraud costs consumers and the financial industry billions of dollars annually. However, there is a dearth of published literature on credit card fraud detection. In this study we employed transaction aggregation strategy to detect credit card fraud. We aggregated transactions to capture consumer buying behavior prior to each transaction and used these aggregations for model estimation to identify fraudulent transactions. We use real-life data of credit card transactions from an international credit card operation for transaction aggregation and model estimation.     

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%.     

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.     

A Novel Strategy for Mining Highly Imbalanced Data in Credit Card Transactions

The design of an efficient credit card fraud detection technique is, however, particularly challenging, due to the most striking characteristics which are; imbalancedness and non-stationary environment of the data. These issues in credit card datasets limit the machine learning algorithm to show a good performance in detecting the frauds. The research in the area of credit card fraud detection focused on detection the fraudulent transaction by analysis of normality and abnormality concepts. Balancing strategy which is designed in this paper can facilitate classification and retrieval problems in this domain. In this paper, we consider the classification problem in supervised learning scenario by creating a contrast vector for each customer based on its historical behaviors. The performance evaluation of proposed model is made possible by a real credit card data-set provided by FICO, and it is found that the proposed model has significant performance than other state-of-the-art classifiers.     

基于关联规则的购物卡欺诈检测研究

关联规则分析被认为是数据挖掘中最有效的研究模型,能够发现相关项目之间潜在有用的关联规则,从而为决策者提供决策支持或为政策法规的制定提供依据。零售业的竞争越来越激烈,关联规则被广泛地应用到零售行业的数据分析中,基于此,以购物卡为例,为了检测和预防购物卡欺诈,从事务购物卡数据库中抽取知识,分析购物卡欺诈的一般特性,以便得出正常的行为模式,对于零售业业务风险管理的提升有所帮助。     

Credit Card Fraud Detection Using Hidden Markov Model

Due to a rapid advancement in the electronic commerce technology, the use of credit cards has dramatically increased. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising. In this paper, we model the sequence of operations in credit card transaction processing using a hidden Markov model (HMM) and show how it can be used for the detection of frauds. An HMM is initially trained with the normal behavior of a cardholder. If an incoming credit card transaction is not accepted by the trained HMM with sufficiently high probability, it is considered to be fraudulent. At the same time, we try to ensure that genuine transactions are not rejected. We present detailed experimental results to show the effectiveness of our approach and compare it with other techniques available in the literature.     

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.     

一种混合的信用卡欺诈检测模型

信用卡欺诈检测是一个重要的问题,为了提升对于真实世界的信用卡欺诈数据的识别率,提出了一种混合的信用卡欺诈检测模型AWFD(Anomaly weight of credit card fraud detection),首先通过异常检测的方法将数据划分为可信和异常数据,然后利用半监督的方法训练一个集成模型,最终再利用异常检测进一步剔除检测结果中的异常结果。AWFD在保障对于可信数据的学习效果上,通过半监督集成学习的方法,利用异常数据进一步扩充集成模型的多样性,并将异常检测和集成模型融合。实验结果表明,比起一些传统的机器学习方法,AWFD可以提高整体的信用卡欺诈检测的识别率。     

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。     

基于事中反馈的信用卡欺诈检测与防控

信用卡欺诈活动日益猖獗,如何增强欺诈检测能力来规避持卡客户、商家等的经济损失是电子商务发展中至关重要的问题.给出了一个基于事中反馈的信用卡欺诈检测解决方案,其将数据挖掘技术和反馈控制技术联合运用实现实时欺诈检测及防控,以增强欺诈检测能力来减少经济损失.     

BLAST-SSAHA Hybridization for Credit Card Fraud Detection

A phenomenal growth in the number of credit card transactions, especially for online purchases, has recently led to a substantial rise in fraudulent activities. Implementation of efficient fraud detection systems has thus become imperative for all credit card issuing banks to minimize their losses. In real life, fraudulent transactions are interspersed with genuine transactions and simple pattern matching is not often sufficient to detect them accurately. Thus, there is a need for combining both anomaly detection as well as misuse detection techniques. In this paper, we propose to use two-stage sequence alignment in which a profile analyzer (PA) first determines the similarity of an incoming sequence of transactions on a given credit card with the genuine cardholder's past spending sequences. The unusual transactions traced by the profile analyzer are next passed on to a deviation analyzer (DA) for possible alignment with past fraudulent behavior. The final decision about the nature of a transaction is taken on the basis of the observations by these two analyzers. In order to achieve online response time for both PA and DA, we suggest a new approach for combining two sequence alignment algorithms BLAST and SSAHA.     

The Real Cost of Fraud to E-tailers

With 27 billion credit card transactions conducted annually, credit card spending is at its highest rate ever. Estimates suggest that 2%, or 540 million, of these transactions are conducted over the Internet. Unfortunately, the boom in online spending comes hand in hand with an increase in E-commerce credit card fraud. The statistics say that E-commerce fraud is 10 to 20 times more likely than face-to-face, with some research claiming rates as high as 5-10%. The actual cost truly shows the shortcomings, with companies like Expedia incurring a $4 million fraud bill last year.     

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.     

Predictive-Analysis-based Machine Learning Model for Fraud Detection with Boosting Classifiers

Fraud detection for credit/debit card, loan defaulters and similar types is achievable with the assistance of Machine Learning (ML) algorithms as they are well capable of learning from previous fraud trends or historical data and spot them in current or future transactions. Fraudulent cases are scant in the comparison of non-fraudulent observations, almost in all the datasets. In such cases detecting fraudulent transaction are quite difficult. The most effective way to prevent loan default is to identify non-performing loans as soon as possible. Machine learning algorithms are coming into sight as adept at handling such data with enough computing influence. In this paper, the rendering of different machine learning algorithms such as Decision Tree, Random Forest, linear regression, and Gradient Boosting method are compared for detection and prediction of fraud cases using loan fraudulent manifestations. Further model accuracy metric have been performed with confusion matrix and calculation of accuracy, precision, recall and F-1 score along with Receiver Operating Characteristic (ROC )curves.     

现代支付系统信息安全的反欺诈监测模型研究

计算机技术、通讯技术的迅猛发展与金融支付方式的信息化创新,使中国现代支付系统既越来越高效便捷,也面临日益加剧且监测颇难的金融信息安全威胁。这种威胁会影响我国现代支付系统信息化进程,还将影响国家金融命脉的信息安全与稳健发展。为此,提出了一种现代支付系统信息安全的反欺作监测模型,该模型基于计算机链路挖掘新技术对现代支付系统海量信息进行动态反欺作监测。对现代支付系统主要支付工具之一的信用卡进行反欺作监测模拟的结果表明,该模型对提高信用卡欺作判别的动态性、准确性和有效性,降低现代支付系统金融风险具有积极的意义。     

Learned lessons in credit card fraud detection from a practitioner perspective

Billions of dollars of loss are caused every year due to fraudulent credit card transactions. The design of efficient fraud detection algorithms is key for reducing these losses, and more algorithms rely on advanced machine learning techniques to assist fraud investigators. The design of fraud detection algorithms is however particularly challenging due to non-stationary distribution of the data, highly imbalanced classes distributions and continuous streams of transactions.At the same time public data are scarcely available for confidentiality issues, leaving unanswered many questions about which is the best strategy to deal with them.In this paper we provide some answers from the practitioner’s perspective by focusing on three crucial issues: unbalancedness, non-stationarity and assessment. The analysis is made possible by a real credit card dataset provided by our industrial partner.     

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.     

一个公平、有效的安全电子交易协议

SET（安全电子交易）协议是由MasterCard和VISA制定的，基于信用卡的安全支付协议。在SET协议基础上提出了一种有效公平的安全电子交易协议（SET－1），该协议不仅保持了SET原有安全和有效的特性，而且实现了交易有效证据的生成和保存，从而保证了交易的公平性，同时还引入交易状态机制。最后，讨论该协议的安全性、有效性和公平性。     

CICS在银行信用卡系统中的应用

建立在IBM主机上的银行信用卡系统在确保给用户提供良好的信用环境的同时,更要注重提高并发处理的能力。CICS在联机事务管理上的优越性,使得其在信用卡系统中得到广泛的应用,为用户提供了高可靠性、高安全性和高稳定性的信用卡环境。该文通过分析银行信用卡系统,并结合CICS自身的一些功能和特性,讨论了如何在银行信用卡系统中应用CICS。