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.     

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。     

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.     

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 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.     

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.     

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.     

Credit card fraud detection: A fusion approach using Dempster–Shafer theory and Bayesian learning

We propose a novel approach for credit card fraud detection, which combines evidences from current as well as past behavior. The fraud detection system (FDS) consists of four components, namely, rule-based filter, Dempster–Shafer adder, transaction history database and Bayesian learner. In the rule-based component, we determine the suspicion level of each incoming transaction based on the extent of its deviation from good pattern. Dempster–Shafer’s theory is used to combine multiple such evidences and an initial belief is computed. The transaction is classified as normal, abnormal or suspicious depending on this initial belief. Once a transaction is found to be suspicious, belief is further strengthened or weakened according to its similarity with fraudulent or genuine transaction history using Bayesian learning. Extensive simulation with stochastic models shows that fusion of different evidences has a very high positive impact on the performance of a credit card fraud detection system as compared to other methods.     

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.     

基于相反分类器的数据流分类方法

目前挖掘概念流动的数据流已经成为研究的热点。概念流动的数据流分类在预防信用卡欺诈,网络入侵发现等应用中具有重要的应用。本文定义了一种相反分类器来从错误中学习,提出了训练一个集合分类器来对具有概念流动的数据流进行分类的算法IWB。通过在合成数据集和benchmark上的实验,与Weighted Baggging算法比较,表明我们的算法具有更高的准确度,更快地收敛到新的目标概念的性能。     

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.     

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。     

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.     

一种校园卡欺诈检测模型

针对校园卡欺诈带来的资金安全问题,提出一种“卡库对账-预处理-神经网络检测”的校园卡欺诈检测工作流程,设计卡库对账算法,该算法能够检测出系统中存在的有异常交易的校园卡,在此基础上结合神经网络算法,建立一种校园卡欺诈检测模型。实验结果表明,该检测模型对校园卡欺诈检测具有较好的适应性。     

一种基于邻域粒度熵的离群点检测算法

离群点检测是数据挖掘领域的重要研究方向之一,其目的是找出数据集中与其他数据对象显著不同的一小部分数据。离群点检测在网络入侵检测、信用卡欺诈检测、医疗诊断等领域有着非常重要的应用。近年来,粗糙集理论被广泛用于离群点检测,然而,经典的粗糙集模型不能有效处理数值型数据。对此,本文利用邻域粗糙集模型来检测离群点,在邻域粗糙集中引入一种新的信息熵模型——邻域粒度熵。基于邻域粒度熵,提出一种新的离群点检测算法OD_NGE。实验结果表明,相对于已有的离群点检测算法,OD_NGE具有更好的离群点检测性能。     

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。     

基于深度集成学习的类极度不均衡数据信用欺诈检测算法

信用欺诈数据分布极度不均衡时, 信息失真、周期性统计误差和报告偏倚所产生的噪声错误对训练模型干扰凸显, 且易产生过拟合现象.鉴于此, 提出一种深度信念神经网络集成算法来解决类极度不均衡的信用欺诈问题.首先, 提出双向联合采样算法克服信息缺失和过拟合问题; 然后, 构造2阶段基分类器簇, 针对支持向量机(support vector machine, SVM)对不均衡数据分布所表现的分类超平面向少数类偏移问题, 利用增强(boosting)算法生成SVM与随机森林(random forest, RF)结合的基分类器簇; 利用深度信念网络(deep belief network, DBN)整合基分类器簇的多元预测, 输出分类结果.考虑传统精度评价指标过度关注多数类样本, 忽视信用欺诈存在违约损失高于利息收益事实, 引入成本-效益指数兼顾正类和负类样本的识别能力, 提高模型对少数类样本预测精度.通过对欧洲信用卡欺诈数据检测发现, 相比于其他相关算法成本-效益指数均值提高3个百分点, 同时, 实验比较样本不均衡比例对算法精度影响, 结果表明在处理极端不均衡数据时所提算法效果更优.     

基于局部偏离因子的孤立点检测算法

孤立点检测是知识发现中的一个活跃领域,如信用卡欺诈、入侵检测等。研究孤立点的异常行为能发现隐藏在数据集中更有价值的知识。该文提出基于局部偏离因子(LDF)的孤立点检测算法,利用每个数据点的LDF衡量该数据点的偏离程度。实验结果表明,该算法能有效检测孤立点,其效率高于LSC算法。     

空间孤立点检测

空间孤立点是指与邻居具有不连续性的空间点,或者是偏离观测值以至使人们认为是由不同的体系产生的。空间孤立点检测在交通、生态、公共安全、卫生健康、地震、海啸等领域有广泛应用。传统的根据一个非空间属性值进行孤立点判断的方法客易引起孤立点判断失误。作者在针对多个属性进行考虑的基础上,提出以空间维确定邻居关系,非空间维定义距离函数,使用Mahalanobis距离检测孤立点,研究一种新的检测空间孤立点的算法。并时时间复杂度进行分析。仿真实验说明算法可以有效地发现大规模空间数据中的孤立点。     

一种基于混合模型的数据流概念漂移检测算法

由于在信用卡欺诈分析等领域的广泛应用,学者们开始关注概念漂移数据流分类问题.现有算法通常假设数据一旦分类后类标已知,利用所有待分类实例的真实类别来检测数据流是否发生概念漂移以及调整分类模型.然而,由于标记实例需要耗费大量的时间和精力,该解决方案在实际应用中无法实现.据此,提出一种基于KNNModel和增量贝叶斯的概念漂移检测算法KnnM-IB.新算法在具有KNNModel算法分类被模型簇覆盖的实例分类精度高、速度快优点的同时,利用增量贝叶斯算法对难处理样本进行分类,从而保证了分类效果.算法同时利用可变滑动窗口大小的变化以及主动学习标记的少量样本进行概念漂移检测.当数据流稳定时,半监督学习被用于扩大标记实例的数量以对模型进行更新,因而更符合实际应用的要求.实验结果表明,该方法能够在对数据流进行有效分类的同时检测数据流概念漂移及相应地更新模型.