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%.     

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.     

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.     

Chip and PIN – biggest UK retail project since decimalisation,but not enough on its own to defeat card fraud

The credit card industry's big idea for tackling fraud, chip and PIN, is being rolled out in earnest within the UK, but security experts warn that on its own it will merely divert criminals to other channels. Chip and PIN does nothing to address cardholder not present (CNP) fraud, notably for online purchases over the Internet, and still leaves the door open for fraudulent transactions from identify theft.     

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.     

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.     

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.     

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.     

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.     

Implementing disposable credit card numbers by mobile phones

Disposable credit card numbers are a recent approach to tackling the severe problem of credit card fraud, nowadays constantly growing, especially in the context of e-commerce payments. Whenever we cannot rely on a secure communication channel between cardholder and issuer, a possibility is to generate new numbers on the basis of some common scheme, starting from a shared secret information. However, in order to make the approach meaningful from a practical point of view, the solution should guarantee backward compatibility with the current system, absence of new investments in dedicated hardware, wide-spectrum usability, and adequate security level. In this paper, we propose a solution based on the use of standard mobile phones, fully meeting the above desiderata. Importantly, our solution does not require any cryptographic support and, as a consequence, the use of PADs or smart phones, opening then its usability to a wider potential market.     

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.     

Using Trusted Email to Prevent Credit Card Frauds in Multimedia Products

This paper focuses on credit card fraud in Multimedia Products, which are soft-products. By soft-products, we mean intangible products that can be used and consumed without having them shipped physically, such as software, music and calling cards (calling time). The demand for soft-products, mainly Multimedia Products, on the Internet has grown in the last few years and is rapidly increasing. Credit card fraudulent transactions on such products are very easy to conduct, while very difficult to recover, compared to the fraud cases in hard-products transactions. This paper classifies the types of products sold on the Internet, and the usual fraud occurred in each type. It summarizes some of the existing best practices to prevent credit card fraud. Finally, it introduces the use of a Trusted Email as a way to authenticate the customer and to simulate his/her physical address (since on these products no actual shipping will happen).     

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.     

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。     

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.     

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.     

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。     

基于双向LSTM的Seq2Seq模型在加油站时序数据异常检测中的应用

加油时序数据包含加油行为的多维信息,但是指定加油站点数据较为稀疏,现有成熟的数据异常检测算法存在挖掘较多假性异常点以及遗漏较多真实异常点的缺陷,并不适用于挖掘加油站时序数据。提出一种基于深度学习的异常检测方法识别加油异常车辆,首先通过自动编码器对加油站点采集到的相关数据进行特征提取,然后采用嵌入双向长短期记忆（Bi-LSTM）的Seq2Seq模型对加油行为进行预测,最后通过比较预测值和原始值来定义异常点的阈值。通过在加油数据集以及信用卡欺诈数据集上的实验验证了该方法的有效性,并且相对于现有方法在加油数据集上均方根误差（RMSE）降低了21.1%,在信用卡欺诈数据集上检测异常的准确率提高了1.4%。因此,提出的模型可以有效应用于加油行为异常的车辆检测,从而提高加油站的管理和运营效率。