AI Agent技术凭借环境感知、自主决策与协同执行能力,为财务共享服务中心的智能化升级提供了新型技术方案。财务共享中心作为集约化财务管理载体,在业务规模扩张与数据量激增的背景下,面临着风险识别滞后与资金结算效率不足等挑战,AI Ag...AI Agent技术凭借环境感知、自主决策与协同执行能力,为财务共享服务中心的智能化升级提供了新型技术方案。财务共享中心作为集约化财务管理载体,在业务规模扩张与数据量激增的背景下,面临着风险识别滞后与资金结算效率不足等挑战,AI Agent通过构建智能识别预警系统、动态评估管控体系、自动化应急处置机制及全程追踪管理框架,实现了交易异常、信用风险、资金异常与合规风险的精准防控。展开更多
Artificial intelligence(AI)is reshaping financial systems and services,as intelligent AI agents increasingly form the foundation of autonomous,goal-driven systems capable of reasoning,learning,and action.This review s...Artificial intelligence(AI)is reshaping financial systems and services,as intelligent AI agents increasingly form the foundation of autonomous,goal-driven systems capable of reasoning,learning,and action.This review synthesizes recent research and developments in the application of AI agents across core financial domains.Specifically,it covers the deployment of agent-based AI in algorithmic trading,fraud detection,credit risk assessment,roboadvisory,and regulatory compliance(RegTech).The review focuses on advanced agent-based methodologies,including reinforcement learning,multi-agent systems,and autonomous decision-making frameworks,particularly those leveraging large language models(LLMs),contrasting these with traditional AI or purely statistical models.Our primary goals are to consolidate current knowledge,identify significant trends and architectural approaches,review the practical efficiency and impact of current applications,and delineate key challenges and promising future research directions.The increasing sophistication of AI agents offers unprecedented opportunities for innovation in finance,yet presents complex technical,ethical,and regulatory challenges that demand careful consideration and proactive strategies.This review aims to provide a comprehensive understanding of this rapidly evolving landscape,highlighting the role of agent-based AI in the ongoing transformation of the financial industry,and is intended to serve financial institutions,regulators,investors,analysts,researchers,and other key stakeholders in the financial ecosystem.展开更多
The remediation of arsenic(As)-contaminated soil is essential for achieving sustainable environmental and agricultural development.Among various techniques,soil washing has emerged as a promising method due to its rap...The remediation of arsenic(As)-contaminated soil is essential for achieving sustainable environmental and agricultural development.Among various techniques,soil washing has emerged as a promising method due to its rapid,efficient,and thorough decontamination capabilities.This review critically examines the application of soil washing technology in the treatment of As-contaminated soil.Specifically,this paper discusses the mechanisms of four types of washing agents(inorganic detergents,chelating agents,surfactants,and microbial agents),focusing on processes such as acid dissolution,electrostatic interaction,ion exchange,and chelation,and the factors affecting washing efficiency.The concentration of washing agent and the initial p H are the key factors influencing the washing effect.The paper also summarizes the application conditions and the corresponding removal rates for different individual washing agents and compares their effectiveness,biodegradability,and environmental impacts.Among these,natural chelating agents are highlighted for their promising potential in As removal.While individual washing agents show certain effectiveness,the combined use of multiple washing agents and the optimization of washing sequence are necessary to achieve superior remediation outcomes.The synergistic effects of combining natural chelating agents with reducing agents,surfactants,and inorganic washing agents,as well as the integration of nanomaterials with chelating agents and microbial agents are summarized,demonstrating their efficiency and stability in soil remediation.By reviewing the current state of research,this paper provides essential insights for the selection of washing agents and the optimization of washing parameters in the remediation of As-contaminated soil.展开更多
Artificial intelligence has experienced a significant boom with the emergence of agentic AI,where autonomous agents are increasingly replacing human intervention,enabling systems to perceive,reason,and act independent...Artificial intelligence has experienced a significant boom with the emergence of agentic AI,where autonomous agents are increasingly replacing human intervention,enabling systems to perceive,reason,and act independently to achieve specific goals.Despite its transformative potential,comprehensive information on agentic AI remains scarce in the literature.This paper provides the first comprehensive review of agentic AI,focusing on its evolution and three core aspects:patterns,types,and environments.The evolution of agentic AI is traced through five phases to the current era of multi-modal and collaborative agents,driven by advancements in reinforcement learning,neural networks,and large language models(LLMs).Five key patterns:tool use,reflection,ReAct,planning,and multi-agent collaboration(MAC)define how agentic AI systems interact and process tasks.These systems are categorized into seven categories,each tailored for specific operational styles and autonomy in decision making.The environments in which these agents operate are classified as static,dynamic,fully observable,partially observable,deterministic,stochastic,single-agent,and multiagent,emphasizing the impact of environmental complexity on agent behavior.Agentic AI has revolutionized systems through autonomous decision making and resource optimization,yet challenges persist in aligning AI with human values,ensuring adaptability,and addressing ethical constraints.Future research focuses on multidomain agents,human–AI collaboration,and self-improving systems.This work provides researchers,practitioners,and policymakers with a structured approach to understanding and advancing the rapidly evolving landscape of agentic AI systems.展开更多
基金supported by the Ministry of Education and Science of the Republic of North Macedonia through the project“Utilizing AI and National Large Language Models to Advance Macedonian Language Capabilties”。
文摘Artificial intelligence(AI)is reshaping financial systems and services,as intelligent AI agents increasingly form the foundation of autonomous,goal-driven systems capable of reasoning,learning,and action.This review synthesizes recent research and developments in the application of AI agents across core financial domains.Specifically,it covers the deployment of agent-based AI in algorithmic trading,fraud detection,credit risk assessment,roboadvisory,and regulatory compliance(RegTech).The review focuses on advanced agent-based methodologies,including reinforcement learning,multi-agent systems,and autonomous decision-making frameworks,particularly those leveraging large language models(LLMs),contrasting these with traditional AI or purely statistical models.Our primary goals are to consolidate current knowledge,identify significant trends and architectural approaches,review the practical efficiency and impact of current applications,and delineate key challenges and promising future research directions.The increasing sophistication of AI agents offers unprecedented opportunities for innovation in finance,yet presents complex technical,ethical,and regulatory challenges that demand careful consideration and proactive strategies.This review aims to provide a comprehensive understanding of this rapidly evolving landscape,highlighting the role of agent-based AI in the ongoing transformation of the financial industry,and is intended to serve financial institutions,regulators,investors,analysts,researchers,and other key stakeholders in the financial ecosystem.
基金supported by the National Key R&D Program of China(No.2023YFC3706703)the National Natural Science Foundation of China(No.51874018)。
文摘The remediation of arsenic(As)-contaminated soil is essential for achieving sustainable environmental and agricultural development.Among various techniques,soil washing has emerged as a promising method due to its rapid,efficient,and thorough decontamination capabilities.This review critically examines the application of soil washing technology in the treatment of As-contaminated soil.Specifically,this paper discusses the mechanisms of four types of washing agents(inorganic detergents,chelating agents,surfactants,and microbial agents),focusing on processes such as acid dissolution,electrostatic interaction,ion exchange,and chelation,and the factors affecting washing efficiency.The concentration of washing agent and the initial p H are the key factors influencing the washing effect.The paper also summarizes the application conditions and the corresponding removal rates for different individual washing agents and compares their effectiveness,biodegradability,and environmental impacts.Among these,natural chelating agents are highlighted for their promising potential in As removal.While individual washing agents show certain effectiveness,the combined use of multiple washing agents and the optimization of washing sequence are necessary to achieve superior remediation outcomes.The synergistic effects of combining natural chelating agents with reducing agents,surfactants,and inorganic washing agents,as well as the integration of nanomaterials with chelating agents and microbial agents are summarized,demonstrating their efficiency and stability in soil remediation.By reviewing the current state of research,this paper provides essential insights for the selection of washing agents and the optimization of washing parameters in the remediation of As-contaminated soil.
文摘Artificial intelligence has experienced a significant boom with the emergence of agentic AI,where autonomous agents are increasingly replacing human intervention,enabling systems to perceive,reason,and act independently to achieve specific goals.Despite its transformative potential,comprehensive information on agentic AI remains scarce in the literature.This paper provides the first comprehensive review of agentic AI,focusing on its evolution and three core aspects:patterns,types,and environments.The evolution of agentic AI is traced through five phases to the current era of multi-modal and collaborative agents,driven by advancements in reinforcement learning,neural networks,and large language models(LLMs).Five key patterns:tool use,reflection,ReAct,planning,and multi-agent collaboration(MAC)define how agentic AI systems interact and process tasks.These systems are categorized into seven categories,each tailored for specific operational styles and autonomy in decision making.The environments in which these agents operate are classified as static,dynamic,fully observable,partially observable,deterministic,stochastic,single-agent,and multiagent,emphasizing the impact of environmental complexity on agent behavior.Agentic AI has revolutionized systems through autonomous decision making and resource optimization,yet challenges persist in aligning AI with human values,ensuring adaptability,and addressing ethical constraints.Future research focuses on multidomain agents,human–AI collaboration,and self-improving systems.This work provides researchers,practitioners,and policymakers with a structured approach to understanding and advancing the rapidly evolving landscape of agentic AI systems.
