Detecting Credit Card Research Articles

DETECTION OF CREDIT CARD FRAUDS WITH MACHINE LEARNING SOLUTIONS: AN EXPERIMENTAL APPROACH

Purpose – propose an experimental way to create ML solutions to the problem of detecting credit card fraud.
 Method or methodology of the work: the article uses machine learning (ML) and data mining methods
 Results: the paper showed that machine learning (ML) and data mining techniques are effective in improving fraud detection accuracy. The study proposes an experimental way to create ML solutions to the problem aimed at minimizing financial losses by monitoring the client’s behavior when using credit cards. The model is tested on a publicly available dataset available to the research community in terms of detection accuracy.
 The sphere of application of the results: in practice, it is advisable to use the results when planning effective strategies for detecting fraud in credit cards.

Credit Card Fraud Detection using Various Machine Learning Algorithms

Abstract: Detecting credit card fraud is probably the most typical problem in the modern day. This is due to a growth in digital shopping as well as electronic-commerce platforms. As digitalization gains popularity in this current world, people are choosing online payment methods for time and transportation ease, among other reasons.Credit card fraud has increased significantly as a result of the tremendous growth in e-commerce use. Fraudsters attempt to exploit the card and the transparency of online payments. Credit card fraud often occurs whenever the card is taken/stolen or when the details of the card are known. As a result, countering fraudsters' activities has become critical. In the modern world, people are facing loads of credit card issues. There are various techniques and Machine Learning Algorithms to overcome this problem such as using Logistic Regression, XgBoost, Naïve Bayesian, K- Nearest Neighbor (KNN), Decision Tree, Random Forest, Support Vector Machine (SVM) etc.The central goal is to safeguard electronic payments, allowing people to use e-banking safely and easily.

Online change-point detection with kernels

Change-points in time series data are usually defined as the time instants at which changes in their properties occur. Detecting change-points is critical in a number of applications as diverse as detecting credit card and insurance frauds, or intrusions into networks. Recently the authors introduced an online kernel-based change-point detection method built upon direct estimation of the density ratio on consecutive time intervals. This paper further investigates this algorithm, making improvements and analyzing its behavior in the mean and mean square sense, in the absence and presence of a change point. These theoretical analyses are validated with Monte Carlo simulations. The detection performance of the algorithm is illustrated through experiments on real-world data and compared to state of the art methodologies.

Credit Card Fake Revealing Analysis and Prediction of Algorithms

Abstract: Credit cards are frequently used in conjunction with the Internet to make payments. The project primarily aims to detect credit card fraud in the real world. Today, our lives have become more and more dependent on online transactions. As technology advances, the number of fraud cases also increases, and ultimately, a fraud detection algorithm needs to be developed to accurately find and eradicate fraudulent activity. Some Machine Learning algorithms can be applied to collect data to address this problem. These machine learning algorithms ultimately optimize the accuracy of the result data. Technique performance is evaluated based on accuracy, f1 score, and precision and recall. Then, processing some of the technique provided attributes identifies the fraud detection and provides the visualization of the graphical model. Classification based algorithms such as logistic regression, random forest and KNN for processing highly unbalanced datasets.

Credit Card Fraud Detection Using Voter Ensemble Technique (VET)

This paper proposes to design a majority vote ensemble classifier for accurate detection of credit card frauds. There is a lack of research studies on analyzing real-world credit card data owing to the issues of confidentiality, some features were hidden. Credit Card Fraud Dataset from kaggle.com was used for this study. The dataset is made up of 284,807 the number of legitimate transactions was found to be 284,315 while the number of fraudulent transactions was found to be 492, this shows that dataset is highly imbalanced, skewed data set like this may lead to an unreliable prediction performance, which is a major classification problem. Hence this study review literatures on methods of addressing the problem imbalance in dataset, and profile a methods of using the Voter Ensemble approach to address the classification problem. The real-world credit card dataset is analyzed to check for missing values and results show that there were no missing or duplicate values in the dataset. In this study, machine learning algorithms are used to detect credit card fraud. The base learners (Logistic regression, Bagging and Naïve Bayes) and the voter ensemble methods are combined. In this experiment, data are splits into 70% training and 30 % testing, the base (learners) classifiers were trained from the training sets, and predictions evaluations were made on data using 10-fold cross-validation. Afterward, the predictions of the learned base classifiers are combined into an ensemble using a stacked generalization strategy. The experimental results positively indicate that the Voter Ensemble method achieves good performances based on Precision, Recall, F1-score and Accuracy rates in detecting fraud cases in credit cards with 95.77%, 99.99%, 97.83%, 99.99% respectively. The result is evaluated and compared with other existing works. Keywords: Voter Ensemble, Logistic Regression (LR), Naïve Bayse, Bagging, imbalance ratio (IR), CCFD.

Autonomous credit card fraud detection using machine learning approach☆

Credit card fraud has risen in vulnerable effects in recent years as more people use credit cards to pay for products. This is owing to advancements in technology and growths in internet transactions, both of which have resulted in massive financial losses due to fraud. To reduce such losses, an effective fraud detection system must be designed and implemented. Machine learning approaches used to detect credit card fraud automatically and do not take into account deception process or behavioral problem, which might lead to alerts. The goal of this study is to figure out how to spot credit card fraud. To detect the occurrence of fraud, a Long Short-Term Memory-Recurrent Neural Network (LSTM-RNN) is proposed. In addition, an attention mechanism has been included to increase performance even more. In instances like fraud detection, where the information sequence is made up of vectors with complicated interrelated properties, models with this structure have proven to be particularly efficient. LSTM-RNN is compared to other classifiers such as Naive Bayes, Support Vector Machine (SVM), and Artificial Neural Network (ANN). Experiments reveal that our proposed model produces powerful results and has a high level of accuracy.

An Innovative Sensing Machine Learning Technique to Detect Credit Card Frauds in Wireless Communications

There has been an increase in credit card fraud as e-commerce has become more widespread. Financial transactions are essential to our economy, so detecting bank fraud is essential. Experiments on automated and real-time fraud detection are needed here. There are numerous machine learning techniques for identifying credit card fraud, and the most prevalent are support vector machine (SVM), logic regression, and random forest. When models penalise all errors equally during training, the quality of these detection approaches becomes crucial. This paper uses an innovative sensing method to judge the classification algorithm by considering the misclassification cost and at the same time by employing SVM hyperparameter optimization using grid search cross-validation and separating the hyperplane using the theory of reproducing kernels like linear, Gaussian, and polynomial, and the robustness is maintained. Because of this, credit card fraud has been identified significantly more successful than in the past.

Financial Fraud Detection Approach Based on Firefly Optimization Algorithm and Support Vector Machine

The usage of credit cards is increasing daily for online transactions to buy and sell goods, and this has also increased the frequency of online credit card fraud. Credit card fraud has become a serious issue for financial institutions over the last decades. Recent research has developed a machine learning (ML)-based credit card fraud transaction system, but due to the high dimensionality of the feature vector and the issue of class imbalance in any credit card dataset, there is a need to adopt optimization techniques. In this paper, a new methodology has been proposed for detecting credit card fraud (financial fraud) that is a hybridization of the firefly bio-inspired optimization algorithm and a support vector machine (called FFSVM), which comprises two sequential levels. In the first level, the firefly algorithm (FFA) and the CfsSubsetEval feature section method have been applied to optimize the subset of features, while in the second level, the support vector machine classifier has been used to build the training model for the detection of credit card fraud cases. Furthermore, a comparative study has been performed between the proposed approach and the existing techniques. The proposed approach has achieved an accuracy of 85.65% and successfully classified 591 transactions, which is far better than the existing techniques. The proposed approach has enhanced classification accuracy, reduced incorrect classification of credit card transactions, and reduced misclassification costs. The evaluation results show that the proposed FFSVM method outperforms other nonoptimization machine learning techniques.

Financial fraud detection: the use of visualization techniques in credit card fraud and money laundering domains

PurposeThis paper aims to reviews the literature on applying visualization techniques to detect credit card fraud (CCF) and suspicious money laundering transactions.Design/methodology/approachIn surveying the literature on visual fraud detection in these two domains, this paper reviews: the current use of visualization techniques, the variations of visual analytics used and the challenges of these techniques.FindingsThe findings reveal how visual analytics is used to detect outliers in CCF detection and identify links to criminal networks in money laundering transactions. Graph methodology and unsupervised clustering analyses are the most dominant types of visual analytics used for CCF detection. In contrast, network and graph analytics are heavily used in identifying criminal relationships in money laundering transactions.Originality/valueSome common challenges in using visualization techniques to identify fraudulent transactions in both domains relate to data complexity and fraudsters’ ability to evade monitoring mechanisms.

Credit Card Fraud Detection Using a New Hybrid Machine Learning Architecture

The negative effect of financial crimes on financial institutions has grown dramatically over the years. To detect crimes such as credit card fraud, several single and hybrid machine learning approaches have been used. However, these approaches have significant limitations as no further investigation on different hybrid algorithms for a given dataset were studied. This research proposes and investigates seven hybrid machine learning models to detect fraudulent activities with a real word dataset. The developed hybrid models consisted of two phases, state-of-the-art machine learning algorithms were used first to detect credit card fraud, then, hybrid methods were constructed based on the best single algorithm from the first phase. Our findings indicated that the hybrid model Adaboost + LGBM is the champion model as it displayed the highest performance. Future studies should focus on studying different types of hybridization and algorithms in the credit card domain.

An Innovative Method to Enhance the Accuracy of Credit Card Fraud Detection Using Logistic Regression Algorithm by Comparing Random Forest Algorithm

Aim: The objective is to develop a model to predict which transactions could be fraud with a high accuracy rate by using machine learning(ML) methods, such as the innovative Random Forest Algorithm. Materials and Methods: F1 score and accuracy rate are performed on fraud rates transactions in a dataset. The two groups (LR) the sample size is (N=20) and (RF) is (N=20) with G power value = 80. Results: To perform the transactions in the credit card by utilizing the card number, financial balance and client details, and if it is offline, the fraud has to steal the user's details in online transactions, which is not easily recognized by the client and bank authority, which prompts loss of sensitive data. (p<0.05) statistically significant value is compared to the amount and time since last purchase with a confidence level of 95% (=.519). (RF) algorithm is used for detecting credit card fraud. The accuracy is analyzed based on the dataset of 99.3%, where the (LR) has the accuracy of 99.1%. Conclusion: The performance of strategy is measured based on accuracy, F1 score, Precision, Recall. The results seem to be better accuracy for (RF) is 93.3%.

Analysis of Supervised Machine Learning Algorithms in the Context of Fraud Detection

In today’s era, where ‘time’ is considered as ‘money,’ people are completely depending on e-commerce and online banking for their routine purchases, shopping, and financial transactions. This increasing dependency on e-commerce are increasing fraud in online transactions, and credit card fraud is one example. Such malicious and unethical practices may cause identity theft and monitory loss to the people across the world. In this research paper, our effort is to identify the best Supervised Machine Learning algorithm that helps in classifying fraudulent and non-fraudulent transactions under credit card fraud on an imbalanced dataset. To conduct this research and compare the results, we have used five different Supervised Machine Learning Classification techniques. On implementing these machine learning techniques, it has been observed that both Supervised Vector Classifier and Logistic Regression Classifier perform better for detecting credit card fraud in an imbalanced dataset.

Credit Card Fraud Detection Using Selective Class Sampling and Random Forest Classifier

The incidences of credit card fraud are increasing. It puts the hard-earned money of users at risk. The financial institutions and governing bodies are looking forward to some robust and reliable ways of detecting credit card fraudulent transactions. The task is challenging due to non-availability of enough data, high class imbalance, and high stake on false negative rates (FNR). Most of the methods available in literature perform well on the accuracy-based performance metrics. However, they fail to yield satisfactory ROC-AUC performance due to the high false positive or false negative rates. Mitigating this issue is the real challenge for the task of fraudulent credit card transaction detection. This paper investigates the problem of credit card fraudulent transaction detection and proposes a technique for it. The proposed method uses a custom selective class sampling-based class balancing technique, and subsequently, it uses random forest for classification. The experimental results show that the proposed technique has better AUC score, accuracy, precision, and recall values as compared with other similar approaches.

Multilayer perceptron artificial neural networks-based model for credit card fraud detection

Nowadays, <span>credit card fraud has emerged as a major problem. People are becoming increasingly using credit cards to pay for their transactions, it has become more popular and essential in our lives. Fraudsters are developing new strategies and techniques over time, and it is not easy for humans to manually check out all transactions. The cost of fraudulent transactions is significant and without prevention mechanisms it is rising. Finding the best methodology to detect fraudulent transactions is a crucial asset to the industry to reduce the fraud financial loss. Artificial neural networks (ANN) technique is considered as one of the effective techniques that has proved its efficiency in detecting credit card fraud transactions with high precision and minimum cost. In this paper, we propose a multilayer perceptron (MLP) ANN-based model solution to improve the accuracy of the detection process. The performance of the methodology is measured based on the precision, sensitivity, specificity, accuracy, F-measure, area under curve (AUC) and root mean square error (RMSE). Moreover, we illustrate the performance results of these measures with a descriptive analysis. Experimental results have shown that the proposed ANN-based model is efficient and does improve the accuracy of the detection of fraudulent transactions.</span>

Developing an Intelligent Credit Card Fraud Detection System with Machine Learning

Credit card fraud is a significant issue in the economic services sector. Each year, billions of rupees are lost due to credit card fraud. Due to confidentiality concerns, there are an absence of studies examining actual credit card records. In this paper, machine learning algorithms are employed to detect credit card fraud. First, standard models are utilized. Then, hybrid techniques consisting of Random Forest, AdaBoost, XGBoost, and majority voting are implemented. To evaluate the effectiveness of the version, a set of publicly accessible credit card records is utilized. Then, credit card records from a real-world economic institution are analyzed. The experimental results suggest that Random Forest and majority voting accomplish precise accuracy estimates for detecting credit card fraud instances.

Mitigating unbalanced and overlapped classes in credit card fraud data with enhanced stacking classifiers system

Background: Credit cards remain the preferred payment method by many people nowadays. If not handled carefully, people may face severe consequences such as credit card frauds. Credit card frauds involve the illegal use of credit cards without the owner’s knowledge. Credit card fraud was estimated to exceed a $35.5 billion loss globally in 2020, and results in direct or indirect financial loss to the owners. Hence, a detection system capable of analysing and identifying fraudulent behaviour in credit card activities is highly desirable. Credit card data are not easy to handle due to their inherited problems: (i) unbalanced class distributions and (ii) overlapping classes. General learning algorithms may not be able to address and handle the problems well. Methods: This study addresses these problems using an Enhanced Stacking Classifiers System (ESCS) that comprises two sequential levels. The first level is an excellent classifier for detecting normal credit card transactions (the majority class), while the second level contains stacking classifiers that distinguish credit card frauds (the minority class). The ESCS can improve the fraud detection via the second level, which contains sensitive classifiers to identify the misclassified fraud transactions as normal transactions from the first classifier. The meta-classifier then combines the decisions of the base classifiers from the levels to produce the final detections. Results: We evaluated the ESCS using the benchmark credit card fraud dataset (CCFD) that exhibits the two problems. The highest true positive rate (TPR) for detecting credit card frauds was 0.8841, which outperformed the single classifiers, bagging, boosting, and other researchers’ works. Conclusions: This study proves that the ESCS, with an additional level added to the stacking classifiers, can improve fraud detection on credit card data.

A Neural Network Ensemble With Feature Engineering for Improved Credit Card Fraud Detection

Recent advancements in electronic commerce and communication systems have significantly increased the use of credit cards for both online and regular transactions. However, there has been a steady rise in fraudulent credit card transactions, costing financial companies huge losses every year. The development of effective fraud detection algorithms is vital in minimizing these losses, but it is challenging because most credit card datasets are highly imbalanced. Also, using conventional machine learning algorithms for credit card fraud detection is inefficient due to their design, which involves a static mapping of the input vector to output vectors. Therefore, they cannot adapt to the dynamic shopping behavior of credit card clients. This paper proposes an efficient approach to detect credit card fraud using a neural network ensemble classifier and a hybrid data resampling method. The ensemble classifier is obtained using a long short-term memory (LSTM) neural network as the base learner in the adaptive boosting (AdaBoost) technique. Meanwhile, the hybrid resampling is achieved using the synthetic minority oversampling technique and edited nearest neighbor (SMOTE-ENN) method. The effectiveness of the proposed method is demonstrated using publicly available real-world credit card transaction datasets. The performance of the proposed approach is benchmarked against the following algorithms: support vector machine (SVM), multilayer perceptron (MLP), decision tree, traditional AdaBoost, and LSTM. The experimental results show that the classifiers performed better when trained with the resampled data, and the proposed LSTM ensemble outperformed the other algorithms by obtaining a sensitivity and specificity of 0.996 and 0.998, respectively.

Credit Card Fraud Detection Based on Random Forest Model

This paper uses a classifier named random forest to detect credit card fraud. Credit card fraud is one of the main issues in the economic industry. To construct a credit card fraud detection system, a certain amount of samples is required. In this paper, a dataset containing 284,807 credit card transactions is used. This dataset has gone through the PCA transformation and includes 492 frauds out of 284,807 transactions. Based on the huge amount of data and imbalanced samples, this paper compresses the dataset and uses the synthetic minority over-sampling technique (SMOTE) to address the problem of imbalanced samples. Also, in this paper, we use random forest as a classification model while constructing the fraud detection system/method.

RNN-BD: an approach for fraud visualisation and detection using deep learning

The evolution of banking information systems considerably increases fraud activities, which can have a negative impact on banking financial services. The use of credit cards has increased significantly due to electronic funding, electronic services and e-commerce activities. Massive amounts of data from credit card transactions can result in big data. Researchers are now using machine learning algorithms to detect and analyse fraud in online transactions. One of the major concerns of the banking industry is the visualisation and detection of credit card fraud. Machine learning techniques only work well when the dataset is small and does not have complex models. Deep learning, on the other hand, processes large and complex data sets. The objective of this paper is to visualise and detect credit card fraud by incorporating deep learning and dimensionality reduction techniques. A real dataset is used to assess the effectiveness of the intended work. The results show that our proposed model is more efficient in identifying fraudulent transactions to reduce fraud and income loss. We found that our deep learning model can be used to identify fraudulent transactions and reduce fraud losses to protect customer interests.

Credit Card Fraud Detection Using State-of-the-Art Machine Learning and Deep Learning Algorithms

People can use credit cards for online transactions as it provides an efficient and easy-to-use facility. With the increase in usage of credit cards, the capacity of credit card misuse has also enhanced. Credit card frauds cause significant financial losses for both credit card holders and financial companies. In this research study, the main aim is to detect such frauds, including the accessibility of public data, high-class imbalance data, the changes in fraud nature, and high rates of false alarm. The relevant literature presents many machines learning based approaches for credit card detection, such as Extreme Learning Method, Decision Tree, Random Forest, Support Vector Machine, Logistic Regression and XG Boost. However, due to low accuracy, there is still a need to apply state of the art deep learning algorithms to reduce fraud losses. The main focus has been to apply the recent development of deep learning algorithms for this purpose. Comparative analysis of both machine learning and deep learning algorithms was performed to find efficient outcomes. The detailed empirical analysis is carried out using the European card benchmark dataset for fraud detection. A machine learning algorithm was first applied to the dataset, which improved the accuracy of detection of the frauds to some extent. Later, three architectures based on a convolutional neural network are applied to improve fraud detection performance. Further addition of layers further increased the accuracy of detection. A comprehensive empirical analysis has been carried out by applying variations in the number of hidden layers, epochs and applying the latest models. The evaluation of research work shows the improved results achieved, such as accuracy, f1-score, precision and AUC Curves having optimized values of 99.9%,85.71%,93%, and 98%, respectively. The proposed model outperforms the state-of-the-art machine learning and deep learning algorithms for credit card detection problems. In addition, we have performed experiments by balancing the data and applying deep learning algorithms to minimize the false negative rate. The proposed approaches can be implemented effectively for the real-world detection of credit card fraud.