Ranking Classification Algorithms Using MCDM Techniques for Credit Card Fraud Detection with Imbalanced Perspective

The increase of Credit Card (CC) fraud in recent years requires the development of fraud detection systems that are both efficient and robust. This paper explored the utilization of machine learning models, with a particular emphasis on ensemble methods, to advance the detection of CC fraud. We present an ensemble model that incorporates different classifiers to address the dataset imbalance issue that is present in most CC datasets. We employed synthetic over-sampling and under-sampling techniques in certain machine learning algorithms to tackle the same issue. To address the issue of under sampling, we implemented ensemble random sampling, which was subsequently followed by Two-Stage Thresholding (TST) to eliminate outliers. The well-known Synthetic Minority Over-sampling Technique (SMOTE) was used for over-sampling. Data was utilized to train the model and subsequently generate predictions by utilizing testing data following the pre-processing of the dataset. The proposed ensemble model outperformed conventional methods in addressing the challenges associated with CC fraud detection, as demonstrated by a comparative analysis. Our findings suggest that ensemble approaches are effective at fighting fraud, paving the way for more adaptive and resilient fraud detection systems.

With increased internet usage, online transactions have been on the rise. One of the most prevalent problems faced is credit cards frauds. While web applications and mailing services are heavily spammed, the upsurge of handheld mobile devices has led to an outburst of heavy mobile credit card spamming. The matter is more severe in mobile devices due to lesser sophisticated filtering mechanisms in built in mobile operating systems. 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. This work proposes a supervised machine learning algorithm to be trained to detect credit card frauds based on the Bayes Net with penalty based regularization. It is shown that the proposed approach attains higher classification accuracy compared to existing work. Keywords— Credit Card Fraud Detection, Machine Learning, Feature Selection, Imbalanced Datasets, Probabilistic Classifier, Classification Accuracy

As technology evolves rapidly, the world is using credit cards instead of cash in its everyday lives, opening up a new way for fraudulent people to abuse them. Credit card fraud losses reached approximately $28.65 billion in 2019, according to Nilsson’s report, and global card fraud is expected to reach around $32.96 billion by 2023. Providers should therefore develop an efficient model to detect and prevent fraud early. In this paper, we used deep learning techniques as an effective way to detect fraudsters in credit card transactions. Therefore, we present a model for predicting legitimate transactions or fraud on Kaggle's credit card dataset. The proposed model is OSCNN (Over Sampling with Convolution Neural Network) which is based on over-sampling preprocessing and CNN (convolution neural network). The MLP (Multi-layer perceptron) was also applied to the dataset. Comparing the MLP-OSCNN results, they proved that the proposed model achieved better results with 98% accuracy.

As customers tendencies learn rapidly about online transactions due to the outbreak of the Covid-19 pandemic, fraudulent transactions that mimic legitimate transactions will continue to increase unabated. Over the years, fraudulent transactions have been committed but the supervised and unsupervised machine learning classifiers and fraud detection techniques could not stop the fraud from occurring at the onset. However, fraudulent cases are detected after post-occurrence thereby recognizing these techniques as non-smart detection models because fraud could only be detected using past data sets. Various fraud detection or classifier techniques using machine learning techniques such as decision trees, random forests, k-means, and neural networks have been studied. Also, the possibility of overfitting due to the branch growth associated with the decision trees is a classifier performance problem. The application of the probability axiomatic metric (PAM) to determine the accuracy of the models is a classification evaluation problem because of the relatively moderate error rate. To overcome the above shortfalls, we proposed a threshold Mahalanobis distance (TMD) classifier and adopted BETH as a performance evaluation metric. The result revealed that TMD is comparable to neural network (NN), k-means, isolation forest, XGBoost, and random forest based on the credit card data set. The analysis showed that TMD and NN have higher efficiency compared to other classifiers. This study demonstrated that the TMD classifier is unique and could be applied to extract legitimate and illegitimate transactions from customer transaction data sets. The study concludes that TMD is robust and comparable to NN and the classification errors associated with BETH are minimal compared to PAM for both TMD and NN.

Along with increasing credit cards and growing trade volume in China, credit card fraud rises sharply. How to enhance the detection and prevention of credit card fraud becomes the focus of risk control of banks. This paper proposes a credit card fraud detection model using outlier detection based on distance sum according to the infrequency and unconventionality of fraud in credit card transaction data, applying outlier mining into credit card fraud detection. Experiments show that this model is feasible and accurate in detecting credit card fraud.

Financial fraud is a serious threat that is expanding effects on the financial sector. The use of credit cards is growing as digitization and internet transactions advance daily. The most common issues in today's culture are credit card scams. This kind of fraud typically happens when someone uses someone else's credit card details. Credit card fraud detection uses transaction data attributes to identify credit card fraud, which can save significant financial losses and affluence the burden on the police. The detection of credit card fraud has three difficulties: uneven data, an abundance of unseen variables, and the selection of an appropriate threshold to improve the models' reliability. This study employs a modified Logistic Regression (LR) model to detect credit card fraud in order to get over the preceding difficulties. The dataset sampling strategy, variable choice, and detection methods employed all have a significant impact on the effectiveness of fraud detection in credit card transactions. The effectiveness of naive bayes, k-nearest neighbour, and logistic regression on highly skewed credit card fraud data is examined in this research. The accuracy of the logistic regression technique will be closer to 0.98%; with this accuracy, frauds may be easily detected. The fact that LR receives the highest classifier score illustrates how well LR predicts credit card theft.

Machine learning approaches are widely used to analyze and detect the increasingly serious problem of credit card fraud. However, typical credit card datasets present imbalanced classification situations because of severely skewed class distributions. Although researchers have proposed some strategies to deal with these imbalances, disadvantages remain. We propose an oversampling method based on variational automatic coding (VAE), combined with classic deep learning techniques, to solve this problem. The VAE method is used to generate a large amount of diverse cases from minority groups in an imbalanced dataset, which are then used to train the classification network. The proposed method is tested on an open credit card fraud dataset, which contains transactions conducted by European cardholders over two days in September 2013. Experimental results show that the VAE method performs better than synthetic minority oversampling techniques and traditional deep neural network methods. In addition, it outperforms recent oversampling methods based on generative adversarial network (GAN) models. After submitting the extended dataset to the baseline for training, the test of the VAE model performs well on indicators including precision, F-measure, accuracy and specificity. These experimental results suggest that the VAE-based oversampling method can be effectively applied to imbalanced classification problems.

In ordinary credit card datasets, there are far fewer fraudulent transactions than ordinary transactions. In dealing with the credit card imbalance problem, the ideal solution must have low bias and low variance. The paper aims to provide an in-depth experimental investigation of the effect of using a hybrid data-point approach to resolve the class misclassification problem in imbalanced credit card datasets. The goal of the research was to use a novel technique to manage unbalanced datasets to improve the effectiveness of machine learning algorithms in detecting fraud or anomalous patterns in huge volumes of financial transaction records where the class distribution was imbalanced. The paper proposed using random forest and a hybrid data-point approach combining feature selection with Near Miss-based undersampling technique. We assessed the proposed method on two imbalanced credit card datasets, namely, the European Credit Card dataset and the UCI Credit Card dataset. The experimental results were reported using performance matrices. We compared the classification results of logistic regression, support vector machine, decision tree, and random forest before and after using our approach. The findings showed that the proposed approach improved the predictive accuracy of the logistic regression, support vector machine, decision tree, and random forest algorithms in credit card datasets. Furthermore, we found that, out of the four algorithms, the random forest produced the best results.

Credit card fraud detection strategies with consumer incentives

Credit card fraud resulted in the loss of $3 billion to North American financial institutions in 2017. The rise of digital payments systems such as Apple Pay, Android Pay, and Venmo has meant that loss due to fraudulent activity is expected to increase. Deep Learning presents a promising solution to the problem of credit card fraud detection by enabling institutions to make optimal use of their historic customer data as well as real-time transaction details that are recorded at the time of the transaction. In 2017, a study found that a Deep Learning approach provided comparable results to prevailing fraud detection methods such as Gradient Boosted Trees and Logistic Regression. However, Deep Learning encompasses a number of topologies. Additionally, the various parameters used to construct the model (e.g. the number of neurons in the hidden layer of a neural network) also influence its results. In this paper, we evaluate a subsection of Deep Learning topologies — from the general artificial neural network to topologies with built-in time and memory components such as Long Short-term memory — and different parameters with regard to their efficacy in fraud detection on a dataset of nearly 80 million credit card transactions that have been pre-labeled as fraudulent and legitimate. We utilize a high performance, distributed cloud computing environment to navigate past common fraud detection problems such as class imbalance and scalability. Our analysis provides a comprehensive guide to sensitivity analysis of model parameters with regard to performance in fraud detection. We also present a framework for parameter tuning of Deep Learning topologies for credit card fraud detection to enable financial institutions to reduce losses by preventing fraudulent activity.

The increase in the volume and velocity of credit card transactions causes class imbalance and concept deviation problems in data sets where credit card fraud is detected. These problems make it very difficult for traditional approaches to produce robust detection models. In this study, a different perspective has been developed for this problem and a novel approach named Fraud Detection with Image Conversion (FDIC) is proposed. FDIC handles credit card transactions as time series and transforms them into images. These images, which comprise temporal correlations and bilateral relationships of features, are classified by a convolutional neural network architecture as fraudulent or legitimate. When the obtained results are compared with the related studies, FDIC has the best F1-score and recall values, which are 85.49% and 80.35%, respectively. Since the images created during the FDIC process are difficult to interpret, a new explainable artificial intelligence approach is also presented. In this way, feature relationships that have a dominant effect on fraud detection are revealed.

The explosive expansion of E-Commerce has led to a corresponding surge in credit card fraud as more people use their cards to make transactions online. Credit card fraud is on the rise as credit cards become the preferred method of payment for both online and in-store purchases. In practice, fraudulent and legitimate transactions coexist, and basic pattern-matching approaches are typically insufficient to identify fraud effectively. While Artificial Neural Networks (ANNs) offer several advantages, it's important to note that they require careful tuning, validation, and ongoing monitoring to ensure optimal performance. Additionally, the interpretability of ANNs can be a challenge, and efforts are being made to enhance model interpretability in the context of fraud detection. Identifying fraudulent transactions while avoiding false positives is the main objective of a credit card fraud detection system that uses ANNs effectively and correctly. Therefore, for credit card issues to reduce their loss, it is crucial that they implement reliable fraud detection systems. Various forms of credit card fraud may now be detected using cutting-edge methods based on Artificial Intelligence (AI), Data mining, Fuzzy logic, Machine learning (ML), sequence alignments, Genetics Programming, etc. An effective system for detecting credit card fraud requires a thorough grasp of all these methods. Using a set of predetermined criteria, this study examines the various neural network techniques currently in use for credit card fraud detection and provides an overall rating for each. The result shows 98% accuracy and 96% recall for the credit card fraud detection system.

Abstract: With the increasing reliance on online transactions across sectors such as banking, telecommunications, insurance, and e-commerce, credit card fraud has emerged as a critical concern. The widespread use of electronic payment systems, particularly credit and debit cards, has made fraudulent activities more prevalent and sophisticated. Credit card fraud detection presents a complex classification problem that can be effectively addressed using machine learning (ML) techniques. This study explores the capabilities of ML algorithms in detecting fraudulent credit card transactions by examining both the challenges and opportunities in this domain. Adopting a mixed-method approach that combines a comprehensive literature review with experimental evaluation, the study investigates how past transaction data distinguishing between legitimate and fraudulent activities can be modeled to classify new transactions accurately. The research advocates for a progressive transition towards hybrid models that integrate machine learning with deep learning techniques to enhance the robustness and adaptability of fraud detection systems.

peer reviewed

Application of Credit Card Fraud Detection: Based on Bagging Ensemble Classifier