Search Results (70)

This study addresses the challenges of data imbalance and missing values in credit card transaction datasets by employing mode-based imputation and various machine learning models. We analyzed two distinct datasets: one consisting of European cardholders and the other from American Express, applying multiple machine learning algorithms, including Artificial Neural Networks, Convolutional Neural Networks, and Gradient Boosted Decision Trees, as well as others. Notably, the Gradient Boosted Decision Tree demonstrated superior predictive performance, with accuracy increasing by 4.53%, reaching 96.92% on the European cardholders dataset. Mode imputation significantly improved data quality, enabling stable and reliable analysis of merged datasets with up to 50% missing values. Hypothesis testing confirmed that the performance of the merged dataset was statistically significant compared to the original datasets. This study highlights the importance of robust data handling techniques in developing effective fraud detection systems, setting the stage for future research on combining different datasets and improving predictive accuracy in the financial sector. Full article

In such a dynamic and increasingly digitalized financial sector, many sophisticated fraudulent and cybercrime activities continue to challenge conventional detection systems. This research study explores a decentralized anomaly detection framework using deep autoencoders, designed to meet the dual imperatives of fraud detection effectiveness and user data privacy. Instead of relying on centralized aggregation or data sharing, the proposed model simulates distributed training across multiple financial nodes, with each institution processing data locally and independently. The framework is evaluated using two real-world datasets, the Credit Card Fraud dataset and the NeurIPS 2022 Bank Account Fraud dataset. The research methodology applied robust preprocessing, the implementation of a compact autoencoder architecture, and a threshold-based anomaly detection strategy. Evaluation metrics, such as confusion matrices, receiver operating characteristic (ROC) curves, precision–recall (PR) curves, and reconstruction error distributions, are used to assess the model’s performance. Also, a threshold sensitivity analysis has been applied to explore detection trade-offs at varying levels of strictness. Although the model’s recall remains modest due to class imbalance, it demonstrates strong precision at higher thresholds, which demonstrates its utility in minimizing false positives. Overall, this research study is a practical and privacy-conscious approach to fraud detection that aligns with the operational realities of financial institutions and regulatory compliance toward scalability, privacy preservation, and interpretable fraud detection solutions suitable for real-world financial environments. Full article

The surge in credit fraud incidents poses a critical threat to financial systems, driving the need for robust and adaptive fraud detection solutions. While various predictive models have been developed, existing approaches often struggle with two persistent challenges: extreme class imbalance and delays in detecting fraudulent activity. In this study, we propose an unsupervised Adversarial Autoencoder (AAE) framework designed to tackle these challenges simultaneously. The results highlight the potential of our approach as a scalable, interpretable, and adaptive solution for real-world credit fraud detection systems. Full article

Financial fraud detection is a critical application area within the broader domains of cybersecurity and intelligent financial analytics. With the growing volume and complexity of digital transactions, the traditional rule-based and shallow learning models often fall short in detecting sophisticated fraud patterns. This study addresses the challenge of accurately identifying fraudulent financial activities, especially in highly imbalanced datasets where fraud instances are rare and often masked by legitimate behavior. The existing models also lack interpretability, limiting their utility in regulated financial environments. Experiments were conducted on three benchmark datasets: IEEE-CIS Fraud Detection, European Credit Card Transactions, and PaySim Mobile Money Simulation, each representing diverse transaction behaviors and data distributions. The proposed methodology integrates a transformer-based encoder, multi-teacher knowledge distillation, and a symbolic belief–desire–intention (BDI) reasoning layer to combine deep feature extraction with interpretable decision making. The novelty of this work lies in the incorporation of cognitive symbolic reasoning into a high-performance learning architecture for fraud detection. The performance was assessed using key metrics, including the F1-score, AUC, precision, recall, inference time, and model size. Results show that the proposed transformer–BDI model outperformed traditional and state-of-the-art baselines across all datasets, achieving improved fraud detection accuracy and interpretability while remaining computationally efficient for real-time deployment. Full article

The existing financial payment system has inherent credit card fraud problems that must be solved with strong and effective solutions. In this research, a combined deep learning model that incorporates a convolutional neural network (CNN), long-short-term memory (LSTM), and fully connected output layer is proposed to enhance the accuracy of fraud detection, particularly in addressing the class imbalance problem. A CNN is used for spatial features, LSTM for sequential information, and a fully connected output layer for final decision-making. Furthermore, SMOTE is used to balance the data and hyperparameter tuning is utilized to achieve the best model performance. In the case of hyperparameter tuning, the detection rate is greatly enhanced. High accuracy metrics are obtained by the proposed CNN-LSTM (CLST) model, with a recall of 83%, precision of 70%, F1-score of 76% for fraudulent transactions, and ROC-AUC of 0.9733. The proposed model’s performance is enhanced by hyperparameter optimization to a recall of 99%, precision of 83%, F1-score of 91% for fraudulent cases, and ROC-AUC of 0.9995, representing almost perfect fraud detection along with a low false negative rate. These results demonstrate that optimization of hyperparameters and layers is an effective way to enhance the performance of hybrid deep learning models for financial fraud detection. While prior studies have investigated hybrid structures, this study is distinguished by its introduction of an optimized of CNN and LSTM integration within a unified layer architecture. Full article

This study presents an optimization for a distributed machine learning framework to achieve credit card fraud detection scalability. Due to the growth in fraudulent activities, this research implements the PySpark-based processing of large-scale transaction datasets, integrating advanced machine learning models: Logistic Regression, Decision Trees, Random Forests, XGBoost, and CatBoost. These have been evaluated in terms of scalability, accuracy, and handling imbalanced datasets. Key findings: Among the most promising models for complex and imbalanced data, XGBoost and CatBoost promise close-to-ideal accuracy rates in fraudulent transaction detection. PySpark will be instrumental in scaling these systems to enable them to perform distributed processing, real-time analysis, and adaptive learning. This study further discusses challenges like overfitting, data access, and real-time implementation with potential solutions such as ensemble methods, intelligent sampling, and graph-based approaches. Future directions are underlined by deploying these frameworks in live transaction environments, leveraging continuous learning mechanisms, and integrating advanced anomaly detection techniques to handle evolving fraud patterns. The present research demonstrates the importance of distributed machine learning frameworks for developing robust, scalable, and efficient fraud detection systems, considering their significant impact on financial security and the overall financial ecosystem. Full article

In the field of credit card fraud detection, traditional methods often struggle due to their reliance on complex manual feature engineering or their inability to adapt to rapidly changing fraud patterns. This paper introduces an innovative approach called FinGraphFL, which merges graph-based learning with the principles of federated learning and improves security through differential privacy. FinGraphFL utilizes Graph Attention Networks to analyze dynamic relationships between daily credit card transaction records, enhancing its ability to detect fraudulent activities. With the addition of differential privacy, the model allows multiple financial institutions to collaborate to refine the detection model without sharing sensitive data, thus improving adaptability and accuracy. The results are tested in two public datasets that show that FinGraphFL achieves accuracy rates of $0.9780$ and $0.9839$, significantly outperforming traditional methods. Building on these results, FinGraphFL sets the stage for future advances in real-time learning and global financial collaboration, ensuring simultaneous progress in security and privacy protections. Full article

Credit card fraud detection is a critical research area due to the significant financial losses and security risks associated with fraudulent activities. This study presents FraudX AI, an ensemble-based framework addressing the challenges in fraud detection, including imbalanced datasets, interpretability, and scalability. FraudX AI combines random forest and XGBoost as baseline models, integrating their results by averaging probabilities and optimizing thresholds to improve detection performance. The framework was evaluated on the European credit card dataset, maintaining its natural imbalance to reflect real-world conditions. FraudX AI achieved a recall value of 95% and an AUC-PR of 97%, effectively detecting rare fraudulent transactions and minimizing false positives. SHAP (Shapley additive explanations) was applied to interpret model predictions, providing insights into the importance of features in driving decisions. This interpretability enhances usability by offering helpful information to domain experts. Comparative evaluations of eight baseline models, including logistic regression and gradient boosting, as well as existing studies, showed that FraudX AI consistently outperformed these approaches on key metrics. By addressing technical and practical challenges, FraudX AI advances fraud detection systems with its robust performance on imbalanced datasets and its focus on interpretability, offering a scalable and trusted solution for real-world financial applications. Full article

The increasing sophistication of fraud tactics necessitates advanced detection methods to protect financial assets and maintain system integrity. Various approaches based on artificial intelligence have been proposed to identify fraudulent activities, leveraging techniques such as machine learning and deep learning. However, class imbalance remains a significant challenge. We propose several solutions based on advanced generative modeling techniques to address the challenges posed by class imbalance in fraud detection. Class imbalance often hinders the performance of machine learning models by limiting their ability to learn from minority classes, such as fraudulent transactions. Generative models offer a promising approach to mitigate this issue by creating realistic synthetic samples, thereby enhancing the model’s ability to detect rare fraudulent cases. In this study, we introduce and evaluate multiple generative models, including Variational Autoencoders (VAEs), standard Autoencoders (AEs), Generative Adversarial Networks (GANs), and a hybrid Autoencoder–GAN model (AE-GAN). These models aim to generate synthetic fraudulent samples to balance the dataset and improve the model’s learning capacity. Our primary objective is to compare the performance of these generative models against traditional oversampling techniques, such as SMOTE and ADASYN, in the context of fraud detection. We conducted extensive experiments using a real-world credit card dataset to evaluate the effectiveness of our proposed solutions. The results, measured using the BEFS metrics, demonstrate that our generative models not only address the class imbalance problem more effectively but also outperform conventional oversampling methods in identifying fraudulent transactions. Full article

With the widespread use of credit cards in online and offline transactions, credit card fraud has become a significant challenge in the financial sector. The rapid advancement of payment technologies has led to increasingly sophisticated fraud techniques, necessitating more effective detection methods. While machine learning has been extensively applied in fraud detection, the application of deep learning methods remains relatively limited. Inspired by brain-like computing, this work employs the Continuous-Coupled Neural Network (CCNN) for credit card fraud detection. Unlike traditional neural networks, the CCNN enhances the representation of complex temporal and spatial patterns through continuous neuron activation and dynamic coupling mechanisms. Using the Kaggle Credit Card Fraud Detection (CCFD) dataset, we mitigate data imbalance via the Synthetic Minority Oversampling Technique (SMOTE) and transform sample feature vectors into matrices for training. Experimental results show that our method achieves an accuracy of 0.9998, precision of 0.9996, recall of 1.0000, and an F1-score of 0.9998, surpassing traditional machine learning models, which highlight CCNN’s potential to enhance the security and efficiency of fraud detection in the financial industry. Full article

This research paper presents novel approaches for detecting credit card risk through the utilization of Long Short-Term Memory (LSTM) networks and XGBoost algorithms. Facing the challenge of securing credit card transactions, this study explores the potential of LSTM networks for their ability to understand sequential dependencies in transaction data. This research sheds light on which model is more effective in addressing the challenges posed by imbalanced datasets in credit risk assessment. The methodology utilized for imbalanced datasets includes the use of the Synthetic Minority Oversampling Technique (SMOTE) to address any imbalance in class distribution. This paper conducts an extensive literature review, comparing various machine learning methods, and proposes an innovative framework that compares LSTM with XGBoost to improve fraud detection accuracy. LSTM, a recurrent neural network renowned for its ability to capture temporal dependencies within sequences of transactions, is compared with XGBoost, a formidable ensemble learning algorithm that enhances feature-based classification. By meticulously carrying out preprocessing tasks, constructing competent training models, and implementing ensemble techniques, our proposed framework demonstrates unwavering performance in accurately identifying fraudulent transactions. The comparison of LSTM and XGBoost shows that LSTM is more effective for our imbalanced dataset. Compared with XGBOOST’s 97% accuracy, LSTM’s accuracy is 99%. The final result emphasizes how crucial it is to select the optimal algorithm based on particular criteria within financial concerns, which will ultimately result in more reliable and knowledgeable credit score decisions. Full article

In recent years, with the rapid development of technology and the economy, online transaction fraud has become more and more frequent. In the face of massive records of online transaction data, manual detection methods are long outdated, and machine learning methods have become mainstream. However, although traditional machine learning methods perform well in fraud detection tasks, the lack of interpretability and class imbalance issues have always been pain points that are difficult to resolve for such methods. Unlike traditional methods, the belief rule base, as a rule-based expert system model, can integrate expert knowledge and has excellent interpretability. In this paper, we propose an innovative ensemble BRB (belief rule base) model to solve the credit card fraud detection problem by combining an ensemble learning framework with the BRB model. Compared with traditional machine learning methods, the proposed model has the advantage of high interpretability. And compared with traditional BRB models, the ensemble framework enables better performance in dealing with highly imbalanced classification tasks. In an experimental study, two datasets of credit card fraud detection from Kaggle are used to validate the effectiveness of this work. The results show that this new method can achieve excellent performance in the application of fraud detection and is capable of effectively mitigating the impact of an imbalanced dataset. Full article

The use of credit cards plays a crucial role in cash management and in meeting the needs for individual and commercial customers due to the spread of risks to the future by making monthly instalments instead of cash transactions. The use of credit cards therefore provides benefits not only to the customers but also to the banks as it enables and sustains a long-term relationship in between them. Despite the increase in the use of credit cards, there is also a significant increase in fraud transactions. To detect and prevent possible fraud operations, banks generally use rule-based techniques or analytical models. In this respect, analytical models have an important place due to their effectiveness, performance, and fast response. The main aim of this paper is therefore to enhance the theoretical and practical understanding of credit card fraud operations, review basic approaches, and propose a more comprehensive approach utilizing the agents. Note that in this study, static analytic modelling (existing approaches) and dynamic analytic modelling (emerging approaches) techniques are compared in terms of methodology, performance, and respective approaches. Since fraud methods and transactions are constantly changing over time, it is thought that there will be an increase in the use of agent-based models with dynamic analytical capabilities. Additionally, in this paper, a proposed model and empiric study are presented for an agent-based intelligent credit card fraud detection system. Full article

The rapid advancement of technology has increased the complexity of cyber fraud, presenting a growing challenge for the banking sector to efficiently detect fraudulent credit card transactions. Conventional detection approaches face challenges in adapting to the continuously evolving tactics of fraudsters. This study addresses these limitations by proposing an innovative hybrid model that integrates Machine Learning (ML) and Deep Learning (DL) techniques through a stacking ensemble and resampling strategies. The hybrid model leverages ML techniques including Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Logistic Regression (LR) alongside DL techniques such as Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory Network (BiLSTM) with attention mechanisms. By utilising the stacking ensemble method, the model consolidates predictions from multiple base models, resulting in improved predictive accuracy compared to individual models. The methodology incorporates robust data pre-processing techniques. Experimental evaluations demonstrate the superior performance of the hybrid ML+DL model, particularly in handling class imbalances and achieving a high F1 score, achieving an F1 score of 94.63%. This result underscores the effectiveness of the proposed model in delivering reliable cyber fraud detection, highlighting its potential to enhance financial transaction security. Full article

The Nielsen Report points out that credit card fraud caused business losses of USD 28.65 billion globally in 2019, with the US accounting for more than one-third of the high share, and that insufficient identification of credit card fraud has brought about a serious loss of financial institutions’ ability to do business. In small sample data environments, traditional fraud detection methods based on prototype network models struggle with the loss of time-series features and the challenge of identifying the uncorrected sample distribution in the metric space. In this paper, we propose a credit card fraud detection method called the Time-Series Attention-Boundary-Enhanced Prototype Network (TABEP), which strengthens the temporal feature dependency between channels by incorporating a time-series attention module to achieve channel temporal fusion feature acquisition. Additionally, nearest-neighbor boundary loss is introduced after the computation of the prototype-like network model to adjust the overall distribution of features in the metric space and to clarify the representation boundaries of the prototype-like model. Experimental results show that the TABEP model achieves higher accuracy in credit card fraud detection compared to five existing baseline prototype network methods, better fits the overall data distribution, and significantly improves fraud detection performance. This study highlights the effectiveness of open innovation methods in addressing complex financial security problems, which is of great significance for promoting technological advancement in the field of credit card security. Full article