Real time SOA based credit card fraud detection system using machine learning techniques

The rapid growth of online transactions has led to an increase in credit card fraud, posing significant challenges to financial institutions and consumers. In this study, we propose a robust credit card fraud detection system utilizing machine learning algorithms, specifically Support Vector Machine (SVM) and Random Forest. The dataset used in this research consists of a comprehensive collection of credit card transactions, including both legitimate and fraudulent activities. Feature engineering techniques are employed to extract relevant information from the dataset, such as transaction amount, location, time, and type. we implement SVM and Random Forest classifiers to train and evaluate the model's performance in detecting fraudulent transactions. SVM is known for its effectiveness in handling high dimensional data and finding optimal decision boundaries, while Random Forest excels in handling large datasets with high variability. the evaluation of the proposed models is conducted using standard metrics such as precision, recall, and F1-score. Additionally, we employ techniques such as cross-validation to ensure the robustness of the models and avoid overfitting. experimental results demonstrate the effectiveness of both SVM and Random Forest in accurately identifying fraudulent credit card transactions. Furthermore, ensemble techniques such as bagging and boosting could potentially enhance the performance of the classifiers further. the proposed credit card fraud detection system provides a practical solution for financial institutions and e-commerce platforms to mitigate the risks associated with fraudulent activities. By leveraging machine learning algorithms such as SVM and Random Forest, it offers an efficient and scalable approach to safeguarding financial transactions in the digital era.

Financial fraud associated with the transactions of credit card leads to unauthorized access of performing credit card transactions in different platforms by intercepting important card credentials. In order to curb this problem, an effective fraud detection system is of primary importance for any financial institution. In the proposed methodology, a web-based fraud detection system has been designed considering two different protocols for the web-based services such as simple object access protocol (SOAP) and representational state transfer (REST). Further, for detecting the fraudulent transactions, these services are associated with five different machine learning techniques such as support vector machine (SVM), multilayer perceptron (MLP), random forest regression, autoencoder and isolation forest. The performance analysis of each machine learning algorithm associated with SOAP and REST services are critically assessed. The web services have been designed based on concepts of service oriented architecture (SOA) by considering a middleware family of software products i.e., Oracle SOA suite which is very often used by the software architects.

Credit card fraud has caused a huge loss to both banks and consumers in recent years. Thus, an effective Fraud Detection System (FDS) is important to minimize the loss for banks and cardholders. Based on our analysis, the credit card transaction dataset is very skewed, there are much fewer samples of frauds than legitimate transactions. Furthermore, due to the data security and privacy, different banks are usually not allowed to share their transaction datasets. These problems make FDS difficult to learn the patterns of frauds and also difficult to detect them. In this paper, we propose a framework to train a fraud detection model using behavior features with federated learning, we term this detection framework FFD (Federated learning for Fraud Detection). Different from the traditional FDS trained with data centralized in the cloud, FFD enables banks to learn fraud detection model with the training data distributed on their own local database. Then, a shared FDS is constructed by aggregating locally-computed updates of fraud detection model. Banks can collectively reap the benefits of shared model without sharing the dataset and protect the sensitive information of cardholders. Furthermore, an oversampling approach is combined to balance the skewed dataset. We evaluate the performance of our credit card FDS with FFD framework on a large scale dataset of real-world credit card transactions. Experimental results show that the federated learning based FDS achieves an average of test AUC to 95.5%, which is about 10% higher than traditional FDS.

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.

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 rapid development of the mobile Internet, financial technology, especially artificial intelligence and blockchain technology, has profoundly changed our consumer behavior and the development model of the traditional financial industry. However, the accompanying risks are also being transmitted with unprecedented speed and complexity. Credit card fraud, as an important form of financial fraud, has caused huge economic losses and a crisis of trust for both financial institutions and consumers. Therefore, the establishment of an efficient and secure credit card fraud detection system has become an inevitable requirement for the continuous development of financial technology in the new era. The purpose of this paper is to study and develop a credit card fraud detection system based on federated learning, which can balance the positive and negative samples in credit card transaction data, protect the privacy of bank data, and effectively improve the accuracy and efficiency of fraud detection.

With the continuous development of the economy, the widespread use of credit cards shows a growing trend. However, simultaneously, the risk of credit card fraud still exists and cannot be ignored. This article utilizes bank card data for model training, with a particular focus on addressing the high dimensionality and imbalance of data in credit card transactions. In terms of model construction, it introduces a reinforcement learning-based Long Short-Term Memory (LSTM) network. Additionally, to improve the classification and recognition performance of the model, ensemble learning algorithms and convolutional neural networks are employed for enhancement. The research results indicate that, compared to other methods, the approach proposed in this article demonstrates a higher level of performance in the AUC (Area Under the Curve) metric of 0.92, effectively preventing financial risks, and providing a more reliable solution for credit card fraud detection. In conclusion, after comparing numerous models, it is evident that LSTM is an effective algorithm choice, particularly suitable for credit card fraud detection tasks.

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.

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.

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 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).

The swift progression of technology has increased the complexity of cyber fraud, posing an escalating challenge for the banking sector to reliably and efficiently identify fraudulent credit card transactions. Conventional detection approaches fail to adapt to the advancing strategies of fraudsters, resulting in heightened false positives and inefficiency within fraud detection systems. This study overcomes these restrictions by creating an innovative stacking hybrid machine learning (ML) approach that combines decision trees (DT), random forests (RF), support vector machines (SVM), XGBoost, CatBoost, and logistic regression (LR) within a stacking ensemble framework. This method uses stacking to integrate diverse ML models, enhancing predictive performance, with a meta-model consolidating base model predictions, resulting in superior detection accuracy compared to any single model. The methodology utilizes sophisticated data preprocessing techniques, such as correlation-based feature selection and principal component analysis (PCA), to enhance computing efficiency while preserving essential information. Experimental assessments of a credit card transaction dataset reveal that the stacking ensemble model exhibits higher performance, achieving an F1-score of 88.14%, thereby efficiently balancing precision and recall. This outcome highlights the significance of ensemble methods such as stacking in attaining strong and dependable cyber fraud detection, emphasizing its capacity to markedly enhance the security of financial transactions.

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.

Credit card fraud is a common occurrence in today’s society because the majority of us use credit cards as a form of payment more frequently. This is the outcome of developments in technology and an increase in online transactions, which have given rise to frauds that have caused significant financial losses. In order to detect fraud in credit card transactions, efficient and effective approaches are needed. In this study, we developed a hybrid CNN‐SVM model for detecting fraud in credit card transactions. The effectiveness of our suggested hybrid CNN‐SVM model for detecting fraud in credit card transactions was tested using real‐world public credit card transaction data. The architecture of our hybrid CNN‐SVM model was developed by replacing the final output layer of the CNN model with an SVM classifier. The first classifier is a fully connected layer with softmax that is trained using an end‐to‐end approach, whereas the second classifier is a support vector machine that is piled on top by deleting the final fully connected and softmax layer. According to experimental results, our hybrid CNN‐SVM model produced classification performances with accuracy, precision, recall, F1‐score, and AUC of 91.08%, 90.50%, 90.34%, 90.41, and 91.05%, respectively.

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.