Search Results (188)

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

Globally, forest leisure and recreation (FLR) activities are widely recognized not only for their environmental and social benefits but also for their economic contributions. To better understand these economic contributions, it is vital to examine how the regional economic levels of customers vary when consuming FLR. This study aimed to empirically examine whether the regional economic level of residents (i.e., gross regional domestic product; GRDP) is classifiable using FLR expenditure data, and to interpret which variables contribute to its classification. We acquired anonymized credit card transaction data on residents of two regions with different GRDP levels. The data were preprocessed by identifying FLR-related industries and extracting key spending features for classification analysis. Five classification models (e.g., deep neural network (DNN), random forest, extreme gradient boosting, support vector machine, and logistic regression) were applied. Among the models, the DNN model presented the best performance (overall accuracy = 0.73; area under the curve (AUC) = 0.82). SHAP analysis showed that the “FLR industry” variable was most influential in differentiating GRDP levels across all the models. These findings demonstrate that FLR consumption patterns may vary and are interpretable by economic levels, providing an empirical framework for designing regional economic policies. 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

Although impulsive and hedonic purchasing behaviors may seem to contradict sustainability principles, there are unexplored opportunities through which social media platforms and influencers can redirect these impulses toward sustainable actions. Young consumers, increasingly concerned about the ecological impact of their choices, can be encouraged to adopt responsible and sustainable buying behaviors when these are promoted attractively, enjoyably, and emotionally satisfyingly through social media. This research investigates how social media communication influences hedonic and impulsive purchasing behavior in the Romanian clothing market. In the context where social media is one of the main sources of information and influence for consumers, the research analyzes several determining factors of the purchase decision. Price reductions and the use of credit cards are highlighted as elements that facilitate spontaneous and hedonic targeted purchases, while the attractiveness of clothing items and the need felt play an important role in terms of the desire to buy. In addition, sources of information (such as reviews) have a major impact on consumers’ perceptions and their purchase intentions. Additionally, the study investigates factors such as overall shopping experience and its influence on consumer loyalty. It is approached from two perspectives: attitudinal loyalty, reflected in the preference for brands promoted on social media, and behavioral loyalty, expressed through repeat purchases. The results show that social media acts as an accelerator for hedonic and impulsive buying behaviors, prompting consumers to react quickly to stimuli such as discount campaigns or personalized recommendations. The conclusions highlight the importance of adopting digital marketing strategies that capitalize on the consumers emotional need while also strengthening brand loyalty. These perspectives can guide companies in the clothing industry to adapt their promotion methods to the specifics of the Romanian market and the consumer behavior. Full article

Achieving the mainstream adoption of circular packaging is essential for mitigating the environmental impacts of plastic waste. Its widespread adoption, however, remains hindered by significant user barriers. This study investigates the barriers to user adoption of upstream packaging solutions in Indonesia with the aim of reducing plastic packaging waste. Through a mixed-methods approach including case studies, expert workshops, and focus group discussions, nine key barriers were identified and analysed. These include inconvenience, resistance to changing habits and behaviours, higher costs and deposit schemes, contamination and hygiene concerns, wear and tear, functional and performance limitations, a lack of awareness about the environmental impacts, limited availability and variety, and a lack of trust. This research advances the literature by offering a detailed analysis of these barriers, categorising them into sociocultural, economic, contextual, and regulatory aspects. Additionally, barriers specific to Indonesia were identified such as a shift from being served to self-service refilling, some people not having smartphones, poor cellular signals in rural areas, a preference for plastic packaging due to its resale value, and a preference for cash payments due to limited access to credit or bank cards. The findings highlight the need for tailored, multidisciplinary strategies to overcome these barriers and promote the adoption of circular packaging solutions. This research provides valuable insights for researchers studying circular design, businesses seeking to innovate upstream packaging solutions, and policymakers aiming to develop regulations that support the adoption of circular packaging practices. Full article

This study examines whether the gaps in four financial well-being (FWB) indicators—emergency fund availability, spending less than income, perceived financial comfort, and no credit card debt—between groups with varying levels of financial literacy changed during the economic disruptions of 2020–2022 compared to the more stable period of 2017–2019. Using data from the 2017–2022 waves of the Survey of Household Economics and Decisionmaking conducted by the Federal Reserve Board, this study applies difference-in-differences and event study methods to explore these trends. Descriptive findings, consistent with prior research, show that respondents with higher financial literacy reported greater FWB across all years. Regression estimates based on respondents who provided definitive answers (correct or incorrect) to the Big Three financial literacy questions suggest that the pre-existing gaps in emergency fund availability and perceived financial comfort between respondents with higher and lower financial literacy widened in 2020–2022, whereas the gap in spending less than income remained unchanged. There is some evidence of a widening gap in the likelihood of having no credit card debt, but the estimates are less conclusive. In general, these results indicate that higher financial literacy might have served as a protective factor for some aspects of FWB amid the challenging economic conditions of 2020–2022. However, results based on respondents who provided either correct or “don’t know” answers to the same questions differ in direction from the results of the earlier analysis. The findings of this study have implications for measuring financial literacy and investigating its role in shaping FWB. 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

The Internet Shopping Optimization Problem with multiple item Units looks for the best selection of stores where to buy various or individual units in a required list of items to minimize the final purchase cost. The problem belongs to the most challenging complexity class of optimization problems (NP-Hard). This paper adds to the already complex problem a more difficult situation with a second objective conflicting with the purchase cost minimization. As far as we know, this is the first state-of-the-art proposal with conflicting objectives for the Internet Shopping Optimization Problem or its variants. The objective in conflict with the minimization of the purchase final cost is the cash-back or reward points on personal or corporate credit cards, the most common payment method for online purchases. Due to the nature of the conflicting objectives, this paper proposes using evolutionary multi-objective optimization algorithms. We perform an experimental comparison using eight algorithms from the literature. The experimental results show that NSGA-II achieves the best overall performance for the studied instances from the state of the art. 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