With increasing consumer awareness about environmental sustainability, green products have gained significant market attention. Brand loyalty plays a crucial role in the success of green products, as loyal consumers are more likely to engage in repeat purchases and advocate for environmentally friendly brands. This study explores the key factors that influence brand loyalty among consumers of green products. The research adopts a mixed-methods approach, utilizing a survey of 300 consumers and in-depth interviews with 15 green product consumers. The study finds that factors such as environmental consciousness, perceived product quality, brand trust, and social influence significantly impact brand loyalty in the green product sector. Implications for businesses seeking to enhance loyalty among green product consumers are discussed.

Fraudulent accounting practices pose a significant threat to the stability and integrity of banking systems, leading to financial losses, reputational damage, and systemic risks. This study proposes a deep learning-based framework for detecting fraudulent accounting transactions using a benchmark financial dataset from the UCI repository. The methodology incorporates data preprocessing, feature extraction, and feature engineering to enhance model performance, followed by the development of advanced neural architectures including Deep Neural Networks (DNN), Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN). Comparative evaluation reveals that LSTM outperformed other models with an accuracy of 96.4% and an AUC of 0.981, demonstrating superior capability in identifying complex sequential fraud patterns. The integration of these models into U.S. financial institutions is discussed, highlighting their potential to improve regulatory compliance, strengthen fraud risk management, and ensure greater transparency in financial reporting. This research contributes to the growing body of knowledge in financial fraud detection by showcasing the application of deep learning techniques to fraudulent accounting, providing both academic and practical implications for the banking sector.

The complexity of global supply chains introduces significant risks, ranging from supplier failure to transportation disruptions, which can critically impact industry performance. This study presents a novel approach to supply chain risk detection using Graph Neural Networks (GNNs), leveraging the structural dependencies between suppliers, products, and logistics. Data collected from the UCI Machine Learning Repository was preprocessed and modeled to capture both node-level and relational features. Comparative experiments with baseline models including Logistic Regression, Random Forest, and Gradient Boosting revealed that GNN-based models, particularly Graph Attention Networks (GATs), outperformed traditional methods across all evaluation metrics. The GAT achieved an accuracy of 92.1% and an AUC of 0.97, significantly surpassing classical models in identifying high-risk suppliers. Bar chart and ROC curve analyses further validated the superiority of graph-based learning in extracting meaningful patterns from relational data. These results demonstrate the transformative potential of integrating GNNs into U.S. industry supply chains, where real-time risk detection can be embedded into AI-driven decision-support systems, enabling more resilient and adaptive supply chain management.