Implementation of Credit Card Fraud Detection System with Concept Drifts Adaptation
01 Jan 2018-pp 467-477
TL;DR: The developed algorithm detects credit card fraud, filters 80% fraudulent transactions and acts as a support system for the society at a large.
Abstract: There is a large number of credit card payments take place that is targeted by fraudulent activities. Companies which are responsible for the processing of electronic transactions need to efficiently detect the fraudulent activity to maintain customers’ trust and the continuity of their own business. In this paper, the developed algorithm detects credit card fraud. Prediction of any algorithm is based on certain attribute like customer’s buying behavior, a network of merchants that customer usually deals with, the location of the transaction, amount of transaction, etc. But these attribute changes over time. So, the algorithmic model needs to be updated periodically to reduce this kind of errors. Proposed System provides two solutions for handling concept drift. One is an Active solution and another one is Passive. Active solution refers to triggering mechanisms by explicitly detecting a change in statistics. Passive solution suggests updating the model continuously in order to consider newly added records. The proposed and developed system filters 80% fraudulent transactions and acts as a support system for the society at a large.
Citations
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TL;DR: The proposed intelligent approach for detecting fraud in credit card transactions using an optimized light gradient boosting machine (OLightGBM) outperformed the other approaches and achieved the highest performance in terms of accuracy.
Abstract: New advances in electronic commerce systems and communication technologies have made the credit card the potentially most popular method of payment for both regular and online purchases; thus, there is significantly increased fraud associated with such transactions. Fraudulent credit card transactions cost firms and consumers large financial losses every year, and fraudsters continuously attempt to find new technologies and methods for committing fraudulent transactions. The detection of fraudulent transactions has become a significant factor affecting the greater utilization of electronic payment. Thus, there is a need for efficient and effective approaches for detecting fraud in credit card transactions. This paper proposes an intelligent approach for detecting fraud in credit card transactions using an optimized light gradient boosting machine (OLightGBM). In the proposed approach, a Bayesian-based hyperparameter optimization algorithm is intelligently integrated to tune the parameters of a light gradient boosting machine (LightGBM). To demonstrate the effectiveness of our proposed OLightGBM for detecting fraud in credit card transactions, experiments were performed using two real-world public credit card transaction data sets consisting of fraudulent transactions and legitimate ones. Based on a comparison with other approaches using the two data sets, the proposed approach outperformed the other approaches and achieved the highest performance in terms of accuracy (98.40%), Area under receiver operating characteristic curve (AUC) (92.88%), Precision (97.34%) and F1-score (56.95%).
134 citations
Cites background from "Implementation of Credit Card Fraud..."
...The proposed approach obtained the highest Accuracy (98.40%), while the Concept Drifts Adaptation [31] achieved the lowest Accuracy (80%)....
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...40%), while the Concept Drifts Adaptation [31] achieved the lowest Accuracy (80%)....
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TL;DR: This paper used asexual reproduction optimization (ARO) algorithm to classify the bank transactions into fraud and legitimate, and achieved a high precision value indicating that if ARO detects a record as a fraud, with a high probability, it is a fraud one.
Abstract: Purpose – The best algorithm that was implemented on this Brazilian dataset was artificial immune system (AIS)
algorithm. But the time and cost of this algorithm are high. Using asexual reproduction optimization (ARO) algorithm,
the authors achieved better results in less time. So the authors achieved less cost in a shorter time. Their framework
addressed the problems such as high costs and training time in credit card fraud detection. This simple and effective
approach has achieved better results than the best techniques implemented on our dataset so far. The purpose of this
paper is to detect credit card fraud using ARO.
Design/methodology/approach – In this paper, the authors used ARO algorithm to classify the bank transactions
into fraud and legitimate. ARO is taken from asexual reproduction. Asexual reproduction refers to a kind of production in
which one parent produces offspring identical to herself. In ARO algorithm, an individual is shown by a vector of variables.
Each variable is considered as a chromosome. A binary string represents a chromosome consisted of genes. It is supposed
that every generated answer exists in the environment, and because of limited resources, only the best solution can remain
alive. The algorithm starts with a random individual in the answer scope. This parent reproduces the offspring named bud.
Either the parent or the offspring can survive. In this competition, the one which outperforms in fitness function remains
alive. If the offspring has suitable performance,it will be the next parent, and the current parent becomes obsolete.Otherwise,
the offspring perishes, and the present parent survives. The algorithm recurs until the stop condition occurs.
Findings – Results showed that ARO had increased the AUC (i.e. area under a receiver operating characteristic (ROC)
curve), sensitivity, precision, specificity and accuracy by 13%, 25%, 56%, 3% and 3%, in comparison with AIS,
respectively. The authors achieved a high precision value indicating that if ARO detects a record as a fraud, with a high
probability, it is a fraud one. Supporting a real-time fraud detection system is another vital issue. ARO outperforms AIS
not only in the mentioned criteria, but also decreases the training time by 75% in comparison with the AIS, which is a
significant figure.
Originality/value – In this paper, the authors implemented the ARO in credit card fraud detection. The authors
compared the results with those of the AIS, which was one of the best methods ever implemented on the benchmark
dataset. The chief focus of the fraud detection studies is finding the algorithms that can detect legal transactions from
the fraudulent ones with high detection accuracy in the shortest time and at a low cost. That ARO meets all these
demands.
4 citations
TL;DR: In this article , an incremental deep neural network (DNN) is employed as a predictive model to predict the maintenance notification as instances of data are incrementally added to the existing pool of data.
1 citations
14 Mar 2023
TL;DR: In this article , the authors proposed a technique for identifying credit card fraud that first accounts for customer spending patterns by aggregating transactions to creative new features based on periodic data, and then, they considered benefits and costs when training an XGBoost classifier in order to achieve maximum benefits.
Abstract: Abstract Online credit card fraud is an ongoing problem and with the recent COVID-19 pandemic, there has been a surge of merchants moving their businesses online. It is therefore crucial to identify fraudulent activities before it causes loss to both the bank and its customers. Due to the dynamic nature of fraudsters as well as customer spending behavior, machine learning algorithms are appropriate for this task. However, credit card fraud data is typically imbalanced, favoring the positive class (legitimate transactions), causing traditional machine learning algorithms to err on the side of this majority class; since they consider equal costs and benefits for different decision outcomes when training. Nevertheless, it is more beneficial to correctly identify fraudulent transactions. Therefore, in this paper, we propose a technique for identifying credit card fraud that first accounts for customer spending patterns by aggregating transactions to creative new features based on periodic data. Then, we consider benefits and costs when training an XGBoost classifier in order to achieve maximum benefits. We also evaluate the performance of the classifier using benefits and costs. We demonstrate the effectiveness of our approach using data provided by a bank.
TL;DR: A survey of the latest frameworks and techniques proposed by the researchers for the identification of fraudulent transactions and securing online transactions is presented in this article , where the authors present a survey of their work.
Abstract: In this digital era, the trend of online transactions for E-commerce sites and banking services is increasing. By using different online transaction methods users can make payments directly from their bank accounts. But along with the increase of online transactions, there is an increase in fraudulent transactions. These fraudulent transitions have identical features and characteristics of online transactions, so there is a need for the development of frameworks or technologies to detect fraudulent transactions. In this context, this paper represents a survey of the latest frameworks and techniques proposed by the researchers for the identification of fraudulent transactions and securing online transactions.
References
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TL;DR: This paper provides some answers from the practitioner’s perspective by focusing on three crucial issues: unbalancedness, non-stationarity and assessment in fraud detection algorithms.
Abstract: 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.
357 citations
TL;DR: 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.
Abstract: 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.
289 citations
01 Jul 2015
TL;DR: APATE is proposed, a novel approach to detect fraudulent credit card transactions conducted in online stores that combines intrinsic features derived from the characteristics of incoming transactions and the customer spending history using the fundamentals of RFM.
Abstract: In the last decade, the ease of online payment has opened up many new opportunities for e-commerce, lowering the geographical boundaries for retail While e-commerce is still gaining popularity, it is also the playground of fraudsters who try to misuse the transparency of online purchases and the transfer of credit card records This paper proposes APATE, a novel approach to detect fraudulent credit card transactions conducted in online stores Our approach combines (1) intrinsic features derived from the characteristics of incoming transactions and the customer spending history using the fundamentals of RFM (Recency–Frequency–Monetary); and (2) network-based features by exploiting the network of credit card holders and merchants and deriving a time-dependent suspiciousness score for each network object Our results show that both intrinsic and network-based features are two strongly intertwined sides of the same picture The combination of these two types of features leads to the best performing models which reach AUC-scores higher than 098
273 citations
12 Jul 2015
TL;DR: This paper designs two FDSs on the basis of an ensemble and a sliding-window approach and shows that the winning strategy consists in training two separate classifiers (on feedbacks and delayed labels, respectively), and then aggregating the outcomes.
Abstract: Most fraud-detection systems (FDSs) monitor streams of credit card transactions by means of classifiers returning alerts for the riskiest payments. Fraud detection is notably a challenging problem because of concept drift (i.e. customers' habits evolve) and class unbalance (i.e. genuine transactions far outnumber frauds). Also, FDSs differ from conventional classification because, in a first phase, only a small set of supervised samples is provided by human investigators who have time to assess only a reduced number of alerts. Labels of the vast majority of transactions are made available only several days later, when customers have possibly reported unauthorized transactions. The delay in obtaining accurate labels and the interaction between alerts and supervised information have to be carefully taken into consideration when learning in a concept-drifting environment. In this paper we address a realistic fraud-detection setting and we show that investigator's feedbacks and delayed labels have to be handled separately. We design two FDSs on the basis of an ensemble and a sliding-window approach and we show that the winning strategy consists in training two separate classifiers (on feedbacks and delayed labels, respectively), and then aggregating the outcomes. Experiments on large dataset of real-world transactions show that the alert precision, which is the primary concern of investigators, can be substantially improved by the proposed approach.
101 citations
TL;DR: This paper proposes a novel genetic programming (GP) approach to automated feature design called Autofead, a GP variant evolves a population of candidate features built from a library of sequence-handling functions to leverage the power of both numerical optimization and standard pattern recognition algorithms.
Abstract: Pattern recognition methods rely on maximum-information, minimum-dimension feature sets to reliably perform classification and regression tasks. Many methods exist to reduce feature set dimensionality and construct improved features from an initial set; however, there are few general approaches for the design of features from numeric sequences. Any information lost in preprocessing or feature measurement cannot be recreated during pattern recognition. General approaches are needed to extend pattern recognition to include feature design and selection for numeric sequences, such as time series, within the learning process itself. This paper proposes a novel genetic programming (GP) approach to automated feature design called Autofead. In this method, a GP variant evolves a population of candidate features built from a library of sequence-handling functions. Numerical optimization methods, included through a hybrid approach, ensure that the fitness of candidate algorithms is measured using optimal parameter values. Autofead represents the first automated feature design system for numeric sequences to leverage the power and efficiency of both numerical optimization and standard pattern recognition algorithms. Potential applications include the monitoring of electrocardiogram signals for indications of heart failure, network traffic analysis for intrusion detection systems, vibration measurement for bearing condition determination in rotating machinery, and credit card activity for fraud detection.
63 citations