The rapid development of financial technology (FinTech) has accelerated the integration of artificial intelligence (AI) into smart financial services. AI-driven technologies have significantly improved the efficiency, accuracy, and intelligence of financial decision-making processes. This study reviews and analyzes major AI applications in smart finance, including heterogeneous data fusion models for credit risk assessment, real-time intelligent algorithms for financial fraud detection, and AI-powered robo-advisory systems. A literature review and case analysis approach is adopted to evaluate the practical effectiveness of these technologies. The findings indicate that AI can substantially improve credit evaluation accuracy, enhance fraud detection capability, and support personalized investment management. Nevertheless, challenges related to data privacy, model explainability, and regulatory compliance remain significant barriers to large-scale deployment. Future developments are expected to focus on federated learning, explainable AI, and generative AI technologies. This study provides a comprehensive overview of current applications, challenges, and future directions of AI in smart finance.
This review examines recent progress in large AI models for intelligent manufacturing, covering model architectures, adaptation strategies, system integration, and applications across product development, production processes, equipment maintenance, and manufacturing services.
This review demonstrates that GenAI has substantial potential to support intelligent, sustainable, and data-driven civil engineering practices while emphasizing the need for human expertise and responsible AI implementation.
The rapid development and widespread application of artificial intelligence (AI) have sparked intense discussions on how to deploy responsible AI systems in a manner aligned with human values and ethical standards. Compared to fields like healthcare, energy, or finance, the application of AI in groundwater is relatively limited, and research on responsible AI is even more scarce. Taking the middle reaches of the Heihe River Basin as the study area, this paper proposes six Responsible AI principles: transparency, technical robustness, privacy governance, fairness, accountability, and sustainability. LSTM and Transformer time-series models are developed using multi-source hydrometeorological data, and validated via post-hoc interpretability, Monte Carlo simulation, and scenario analysis. The results show that Transformer outperforms LSTM in accuracy, robustness, and interpretability, demonstrating the operability and practical value of Responsible AI principles in groundwater prediction to support sustainable water management under climate change and human activities.
The proposed framework includes four stages: knowledge acquisition, data preprocessing, hybrid model integration, and predictive decision support, which improves prediction accuracy, reliability, transparency, and decision-making of next-generation intelligent systems.
AI-powered predictive systems for decision support are revolutionizing the way that smart enterprises and industrial organizations are analysing data, predicting future conditions, and making operational and strategic decisions. The systems include machine learning, deep learning, predictive analytics, prescriptive analytics, real-time monitoring, and intelligent recommendation systems to enhance decision-making accuracy, efficiency, and responsiveness. They are used in business forecasting, customer and financial analytics, supply-chain and inventory management, predictive maintenance, production optimization, quality control, energy management, workplace safety and asset monitoring. The addition of new technologies like the Internet of Things, Industrial Internet of Things, digital twins, cloud and edge computing, robotics, blockchain and next generation networks further improve system connectivity, scalability and real-time performance. The successful implementation of these steps needs a structured framework for problem identification, data collection, preprocessing, feature engineering, model selection, training, validation, system integration, deployment, and continual monitoring. Despite these progressions, data quality, interoperability, scalability, algorithmic bias, explainability, privacy, cybersecurity, organizational readiness, and regulatory compliance are all important challenges that still need to be addressed. There is still a need for human oversight, especially when dealing with safety-critical and high-impact decisions. It includes the technological foundations, system architecture, implementation processes, enterprise and industrial applications, performance evaluation, governance requirements, and future directions of AI-supported predictive decision support systems. It concludes that the systems that are trustworthy, secure, transparent, sustainable and intelligent are enterprise and industrial operations.