As financial systems become increasingly complex and interconnected, traditional fraud detection methods struggle to keep pace with sophisticated fraudulent activities. This paper introduces FraudGNN-RL, an innovative framework that combines Graph Neural Networks (GNNs) with Reinforcement Learning (RL) for adaptive and context-aware financial fraud detection. Our approach models financial transactions as a dynamic graph, where entities (e.g., users, merchants) are nodes and transactions form edges. We propose a novel GNN architecture, Temporal-Spatial-Semantic Graph Convolution (TSSGC), which simultaneously captures temporal patterns, spatial relationships, and semantic information in transaction data. The RL component, implemented as a Deep Q-Network (DQN), dynamically adjusts the fraud detection threshold and feature importance, allowing the model to adapt to evolving fraud patterns and minimize detection costs. We further introduce a Federated Learning scheme to enable collaborative model training across multiple financial institutions while preserving data privacy. Extensive experiments on a large-scale, real-world financial dataset demonstrate that FraudGNN-RL outperforms state-of-the-art baselines, achieving a 97.3% F1-score and reducing false positives by 31% compared to the best-performing baseline. Our framework also shows remarkable resilience to concept drift and adversarial attacks, maintaining high performance over extended periods. These results suggest that FraudGNN-RL offers a robust, adaptive, and privacy-preserving solution for financial fraud detection in the era of Big Data and interconnected financial ecosystems.

With the proliferation of mobile devices and payment systems in modern financial services, there is an increasing need to process and analyze continuous streams of transaction data for credit risk assessment. Leveraging the inherent symmetries in financial markets and data structures, this paper introduces DeepCreditRisk, a symmetry-aware deep learning framework that addresses key challenges while maintaining critical invariance properties in financial data representation. The framework incorporates three main components: an adaptive temporal fusion mechanism, a heterogeneous graph neural network, and an attention-based interpretable output layer. The temporal fusion mechanism effectively models both short-term fluctuations and long-term trends in financial time series, while the heterogeneous graph neural network captures intricate relationships within the financial ecosystem. The framework maintains important symmetrical properties in both temporal and structural representations, ensuring balanced feature learning and invariant risk assessment. The attention-based output layer preserves representation symmetry while enhancing model interpretability. Extensive experiments on a large-scale credit risk dataset demonstrate DeepCreditRisk’s superior performance, achieving a 7.2% improvement in the Area Under the Receiver Operating Characteristic Curve (AUC-ROC) and an 18.6% improvement in the Kolmogorov–Smirnov (KS) statistic over state-of-the-art baseline models. The framework maintains high predictive power across various time horizons and provides interpretable insights into feature importance. DeepCreditRisk represents a significant advancement in applying deep learning to credit risk assessment, offering financial institutions a more accurate, robust, and transparent approach for evaluating creditworthiness and managing risk.

The evaluation of auto insurance risks is a fundamental task for financial institutions, crucial for setting equitable premiums and managing risks effectively. Traditional machine learning algorithms often struggle to capture the intricate relationships necessary for accurate risk assessment. In contrast, deep learning methods, while capable of processing complex data structures, lack the ability to model feature interactions and provide interpretability, which are essential for transparent decision-making in the insurance industry. To address these challenges, we introduce the Actuarial Transformer (AT)–a pioneering model that leverages the self-attention mechanism of the Transformer architecture to meticulously map feature interactions. The AT integrates advanced residual models with tree-based methods, enhancing its predictive accuracy. Additionally, it incorporates the SHAP (SHapley Additive exPlanations) model, which uses Shapley values from cooperative game theory to ensure interpretability and transparency in its risk assessments. Our empirical analysis, conducted on a representative dataset of auto insurance risks, demonstrates the AT’s superior performance in risk prediction. The SHAP analysis further validates the model’s ability to prioritize features logically, providing clear insights into the decision-making process. The AT not only improves the precision of auto insurance risk evaluations but also enhances the interpretability of these evaluations, making it a valuable tool for both industry practitioners and clients.

This study introduces a novel application of a temporal convolutional network (TCN) for projecting carbon tax prices, addressing the critical need for accurate forecasting in climate policy. Utilizing data from the World Carbon Pricing Database, we demonstrate that the TCN significantly outperformed traditional time series models in capturing the complex dynamics of carbon pricing. Our model achieved a 31.4% improvement in mean absolute error over ARIMA baselines, with an MAE of 2.43 compared to 3.54 for ARIMA. The TCN model also showed superior performance across different time horizons, demonstrating a 30.0% lower MAE for 1-year projections, and enhanced adaptability to policy changes, with only a 39.8% increase in prediction error after major shifts, compared to ARIMA’s 95.6%. These results underscore the potential of deep learning for enhancing the precision of carbon price projections, thereby supporting more informed and effective climate policy decisions. Our findings have significant implications for policymakers and stakeholders in the realm of carbon pricing and climate change mitigation strategies, offering a powerful tool for navigating the complex landscape of environmental economics.

The complexity of tax regulations and the sheer volume of legislative updates present significant challenges for taxpayers, tax professionals, and revenue authorities seeking to ensure compliance and enforce tax laws effectively. Intelligent tax systems leveraging natural language processing (NLP) technologies offer promising solutions by automating the interpretation of tax regulations, extracting relevant provisions from legal texts, and supporting compliance decision-making. This review examines the current state and future potential of NLP applications in tax regulatory interpretation, analyzing how machine learning (ML), deep learning (DL), and transformer-based language models can process statutory texts, judicial decisions, and administrative guidance to generate actionable insights. The paper explores various NLP techniques including named entity recognition (NER), text classification, information extraction, question answering, and semantic analysis in tax contexts. While NLP systems demonstrate remarkable capabilities in parsing legal language, identifying relevant provisions, and supporting research activities, significant challenges remain regarding the interpretation of ambiguous language, resolution of conflicting authorities, and handling of context-dependent meanings that characterize tax law. This comprehensive analysis synthesizes recent research on intelligent tax systems, examines the technical foundations of NLP approaches, discusses implementation challenges including data availability and model interpretability, and identifies future research directions. The findings suggest that NLP technologies will increasingly augment human expertise in tax regulatory interpretation, though complete automation remains distant due to the inherent complexity and contextual nature of legal reasoning. Successful deployment requires careful attention to data quality, model validation, integration with existing workflows, and preservation of professional judgment in interpretive processes.

Credit card fraud detection remains a critical challenge in financial security, characterized by severe class imbalance and the need to capture complex temporal patterns in transaction sequences. Traditional machine learning approaches treat transactions as independent events, failing to model the sequential nature of user behavior and suffering from inadequate handling of minority class samples. In this paper, we propose an integrated framework that combines generative modeling and time-aware sequential learning for credit card fraud detection. Our approach addresses two fundamental limitations: (1) we model transaction histories as temporal sequences using a Transformer-based architecture that captures both long-term dependencies and abrupt behavioral changes through multi-head self-attention mechanisms, and (2) we employ CTGAN to generate high-quality synthetic fraudulent samples, providing more effective oversampling than conventional techniques like SMOTE. The Time-Aware Transformer incorporates temporal encoding and position-aware attention to preserve transaction order and time intervals, while CTGAN learns the complex conditional distributions of fraudulent transactions to produce realistic synthetic samples. We evaluate our framework on the IEEE-CIS Fraud Detection dataset, demonstrating significant improvements over representative classical and sequential deep-learning baselines. Experimental results show that our method achieves superior performance with an AUC-ROC of 0.982, precision of 0.891, recall of 0.876, and F1-score of 0.883, outperforming the representative baselines considered in this study, including traditional machine learning models, standalone deep learning architectures, and supervised sequential neural models. Ablation studies confirm the individual contributions of both the sequential modeling component and the generative oversampling strategy. Our work demonstrates that combining temporal sequence modeling with generative synthesis provides a robust solution for imbalanced fraud detection, with potential applications extending to other domains requiring sequential pattern recognition under extreme class imbalance.