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.

Fossil fuel power plants are a significant contributor to global carbon dioxide (CO₂) and nitrogen oxide (NO_x) emissions. Accurate monitoring and effective reduction of these emissions are crucial for mitigating climate change. This systematic review examines the current state of research on the application of machine learning techniques in evaluating the emissions from fossil fuel power plants. This review first briefly introduces the continuous emission monitoring (CEM) systems and predictive emission monitoring (PEM) systems that are commonly used in power plants and highlights that machine learning models can significantly improve PEM systems through their capability to process and interpret large datasets intelligently to transform traditional emission monitoring systems by enhancing their precision, effectiveness, and cost-efficiency. Compared to previously published review articles, the key contribution and innovation in this present review is the discussion of machine learning models in CO₂/NO_x emissions according to the different algorithms used, including their advantages and disadvantages in a systematic way, which aims to help future researchers to develop more effective machine learning models. The most popular machine learning model includes reinforcement learning, a forward neural network, a long short-term memory neural network, and support vector regression. While each model method has its own advantages and disadvantages, we noted that training data quality, as well as the proper selection of model parameters, plays an important role. The challenges and research gaps, such as model transferability, a deep understanding of the physics of CO₂/NO_x emissions, and the availability of high-quality data for training machine learning models, are identified, and recommendations as well as potential future research directions to address these challenges are proposed and discussed.

In the rapidly evolving online marketplace, accurately predicting the demand for pre-owned items presents a significant challenge for sellers, impacting pricing strategies, product presentation, and marketing investments. Traditional demand prediction methods, while foundational, often fall short in addressing the dynamic and heterogeneous nature of e-commerce data, which encompasses textual descriptions, visual elements, geographic contexts, and temporal dynamics. This paper introduces a novel approach utilizing the Graph Neural Network (GNN) to enhance demand prediction accuracy by leveraging the spatial relationships inherent in online sales data, named SGNN. Drawing from the rich dataset provided in the fourth Kaggle competition, we construct a spatially aware graph representation of the marketplace, integrating advanced attention mechanisms to refine predictive accuracy. Our methodology defines the product demand prediction problem as a regression task on an attributed graph, capturing both local and global spatial dependencies that are fundamental to accurate predicting. Through attention-aware message propagation and node-level demand prediction, our model effectively addresses the multifaceted challenges of e-commerce demand prediction, demonstrating superior performance over traditional statistical methods, machine learning techniques, and even deep learning models. The experimental findings validate the effectiveness of our GNN-based approach, offering actionable insights for sellers navigating the complexities of the online marketplace. This research not only contributes to the academic discourse on e-commerce demand prediction but also provides a scalable and adaptable framework for future applications, paving the way for more informed and effective online sales strategies.