A Hybrid Convolutional Neural Network and Support Vector Machine‐Based Credit Card Fraud Detection Model

The widespread use of credit cards has led to an increase in fraud. Credit card fraud detection involves identifying and preventing fraudulent transactions, either in real-time or post-occurrence. This paper seeks to create an advanced credit card fraud detection model via data mining. The proposed method comprises four essential steps: data acquisition, preprocessing, feature selection, and fraud detection. A recent balanced dataset is acquired, containing 28 anonymized features about the credit card transactions, along with the transaction amount and the transaction label (normal or fraud). The dataset is then explored to clean it and ensure its integrity. Feature selection is executed via the Energy Valley Optimization (EVO) metaheuristic method, employing the accuracy value of the Light Gradient Boosting Machine (LGBM) as the fitness function. This results in a 30% reduction in features. The reduced dataset is then input into the classification step, where an ensemble soft voting model is applied. This model encompasses Extra Trees, eXtreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost) classifiers. The proposed model averages the probability of the three classifiers for each label and outputs the label with the highest average probability. The proposed method is assessed using recall, precision, accuracy, and F1-score, attaining 99.89%, 99.58%, 99.74%, and 99.74%, respectively. The proposed approach is evaluated against existing machine learning classifiers and relevant studies using the same dataset, showcasing enhanced performance and confirming its efficacy in identifying credit card fraud.

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.

The usage of credit cards for online purchases has increased exponentially and so has fraud. In recent years, detecting fraud in credit card transactions has become significantly more difficult. As a result, having effective and accurate methods for detecting fraud in credit card transactions is vital. Although supervised learning algorithms have been shown to be effective in detecting credit card fraud, they have not generated substantial results. Hence, a Deep Neural Network (DNN)-based technique is proposed. The sequential model is used to construct the proposed DNN. However, the parameters of the model, such as the number of nodes and the activation function in the hidden layers, can also have an impact on its output. The proposed technique was evaluated on a credit card transaction dataset that comprised fraudulent and genuine transactions. This technique obtained the accuracy, area under curve (AUC), and precision of 99.93%, 99.99%, and 99.89%, respectively. This technique was compared with various models such as Optimized LightGBM, Multilayer Perceptron (MLP), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM).

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.

In today's socio-economic scenario, people rely heavily on credit cards.Moreover, credit cards are a requisite financial tool that enables its holders to make assets.It is true that credit cards, as a new method of payment, have become socially amenable to the masses.But nowadays, improvements in technology lead to growth in illegal activities.During credit card transactions many fraudsters can breach security and make fraudulent transactions to withdraw or transfer funds from one's account or e-wallets.The relevant literature presents many machine learning-based approaches for credit card detection, such as Logistic Regression, Decision Tree, CatBoost, Random Forest, Support Vector Machine, KNN, RNN, and CNN.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.A machine learning algorithm was first applied to the dataset, which improved the accuracy of detection of the fraud to some extent.The proposed approaches can be implemented effectively for the real-world detection of credit card fraud.

Credit card fraud is a significant problem for financial institutions and cardholders and can result in significant financial losses and damage to reputation. Detecting and preventing fraud in credit card transactions is critical to minimizing losses and maintaining customer trust. The main objective of this project is to build a model (a web-based system) that can effectively identify fraudulent transactions. This involves collecting and processing large datasets of credit card transactions, including both legitimate and fraudulent transactions, and using machine learning algorithms like decision trees and random forests to train a model to recognize patterns and anomalies. In addition, the system will have strong user authentication protocols that must be in place to prevent unauthorized access to the system.

With the continuous expansion of the banks' credit card businesses, credit card fraud has become a serious threat to banking financial institutions. So, the automatic and real-time credit card fraud detection is the meaningful research work. Because machine learning has the characteristics of non-linearity, automation, and intelligence, so that credit card fraud detection can improve the detection efficiency and accuracy. In view of this, this paper proposes a credit card fraud detection model based on heterogeneous ensemble, namely CUS-RF (cluster-based under-sampling boosting and random forest), based on clustering under-sampling and random forest algorithm. CUS-RF-based credit card fraud detection model has the following advantages. Firstly, the CUS-RF model can better overcome the issue of data imbalance. Secondly, based on the idea of heterogeneous ensemble learning, the clustering under-sampling method and random forest model are fused to achieve a better performance for credit card fraud detection. Finally, through the verification of real credit card fraud dataset, the CUS-RF model proposed in this paper has achieved better performance in credit card fraud detection compared with the benchmark model.

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.

It is now customary to pay by credit cards due to the ease of purchase and the ability to keep customers' credit history. At the same time, credit card fraud emerges as a widely-concerned issue in the electronic payment sector, urging the development of fraud detection techniques. In this paper, I develop a credit card fraud detection model using an autoencoder-based deep neural network. To ensure accuracy and robustness, I train two models on normal transactions and fraud transactions respectively. After that, I combine these two individual models into a hybrid model, which is the major innovation of this creation. ROC score along with the AUC value is the evaluation method here. It turns out that the mean of the AUC of the hybrid model is 0.9577, which is between that of the two individual models and shows the ensured accuracy and increased stability.

With the advancement of mobile and cloud technologies, there is a sharp increase in online transactions. Detecting fraudulent credit card transactions on a timely basis is a very critical and challenging problem in Financial Industry. Although online transactions are very convenient, they bring the risk of fraudulence on many aspects. Some of the key challenges in detecting fraudulence in online transactions include irregular behavioral patterns, skewed dataset i.e. high normal transaction to fraudulent transaction ratio, limited availability of data and dynamically changing environment. Every year people lose millions of dollars due to credit card fraud. There is a lack of quality research in this domain. We have used a dataset comprising of European cardholders which has 284,807 transactions to model our system. In this paper, we will design and develop credit card fraudulence detection system by training and testing two ML algorithms: Decision Forest (DF) and Decision Jungle (DJ) classifiers. Our results successfully demonstrate that DJ classifier delivers higher performance compared to DF classifier.

Credit card transactions have increased significantly every year. Along with the increasing use of credit cards, the risk of fraud by irresponsible people also increases. Credit card fraud can be detected with the help of machine learning. The main problem that often encountered is the transaction data has very large dimensions, unbalanced classes, and requires a detection process with a short computation time. Therefore we need a model that can produce good performance with short computation time using the support vector machine (SVM) method with grid search and genetic algorithm optimization. From the three models built, it was found that the SVM model using an initial dataset which was balanced using ADASYN and searching for the best parameters using grid search as a hyperparameter optimization technique was able to carry out good detection and short computing time. This model is able to detect fraudulent transactions with 99% sensitivity and 99% specificity and the shortest model training time among the other two models.

This study aims to determine the effect of debit card transactions, credit card transactions and e-money transactions to currency demand in Indonesia. This research using model estimation Vector Error Correction models (VECM). The data was collected using secondary data obtained from the website of Bank Indonesia and Bureau Central of Statistic in Indonesia. The data were obtained from January 2009 to August 2017 consisting of debit card transaction, credit card transaction, e-money transaction, and cash amount data in circulation data. The findings showed that debit card transactions have a significant negative effect on the demand for currency in Indonesia in the long term, credit card transactions have a significant positive effect on the demand for currency in Indonesia in the long term, while e-money transactions have a significant positive effect on currency demand in Indonesia in the short and long term. Keywords: E-money; Currency demand; Debit card; Credit card JEL Codes: E41 ; E51

The quick spurt in online transactions has also brought with it a parallel surge in fraud. With digital payments, the scope for fraud remains very high. Credit card fraud, among others, can cause heavy losses to customers and erode the confidence of consumers in Internet transactions. Furthermore, the detection of fraudsters in the online world poses big challenges. For one thing, there is an imbalance in data: fraud transactions are very few compared to genuine transactions. In this research paper, we intend to classify fraud through a supervised machine learning method. SVM is utilized for classification purposes. Based on the available dataset, we performed data analysis to obtain valuable information concerning fraud detection. To solve the problem of data imbalance, we first preprocessed the raw data by randomly choosing some legitimate transactions and normalizing the features. We also used feature selection and scaling methods to improve the accuracy of the model. After training the SVM on the sanitized dataset, we tested the model using performance measures like accuracy, precision, recall, and F1-score. These measures are especially important when working with skewed data. The findings show that the SVM model can detect fraudulent transactions with high precision and a good rate of recall. This shows that it can assist in reducing false alarms while being able to detect most fraud cases. In addition, we touch upon the tradeoff between false negatives and false positives because both pose very significant implications within financial institutions.

Credit card is considered as one of the most popular paying methods for online and regular purchases, due to the advancement in communication and electronic commerce systems. Thus, the fraud associated with these transactions increased significantly. The great utilization of electronic payment is highly affected by this fraudulent transactions, which requires urgent detection to solve this issue. Therefore, effective and efficient approaches to detect fraud in credit card transactions are needed. To catch the fraudulent transaction, a good fitting model is needed, hence researchers recommends the use of various Machine Learning (ML) techniques, because of its beneficial characteristics. The main aim of the research work is to implement an ensemble based ML techniques for Credit Card Fraud Detection (CCFD). The strength of our model is a combination of the forces of the three subsystems; Recursive Feature Elimination (RFE), CCFD's using ensemble classifiers, and Synthetic Minority Oversampling (S MOTE) to deal with the problem of unbalanced data to identify the most effective prediction features. The proposed model run typical tests on two real databases of public credit card transactions, including fraudulent and official ones. Based on the comparison of other ML methods, the extra tree classifier has performed better and achieved high efficiencies such as 96% of accuracy and 57.95% of F1-measure.

Financial transactions using credit card are being observed to be commonly accepted by the users as well as by the financial institutions. At the same time, a good number of fraudsters adopt various techniques to create inconvenience to the users and in the process the financial institutions lose goodwill or trustworthiness. However, a number of methodologies have been proposed by various researchers to counter and revoke the fraudulent activities in credit card transactions. The crux of the problem is that the fraudulent activities need to be curbed instantly if at all can be identified, when the fraudsters initiate them. In this study, a real time fraud detection system based on service oriented architecture (SOA) has been proposed to analyze the fraudulent activities in credit card transactions. The architecture is designed on the basis of Apache Kafka tool, which is used for real time streaming of transactional data to detect the fraudulent transactions. This process considers different services which form the backbone of SOA. Further, five different machine learning classifiers namely support vector machine (SVM), multilayer perceptron (MLP), random forest regressor, autoencoder and isolation forest have been considered to identify the fraudulent activities instantly with the help of SOA based real time architecture.