cointelegraphPublished on 2026-07-31Last updated on 2026-07-31
国际清算银行（BIS）宣布，其Agorá项目已成功完成了具有真实价值的代币化批发跨境支付测试。此次试验有28家金融机构和央行参与，通过17种交易场景，结算了约80万瑞士法郎（约合100万美元）的资金。测试利用代币化的央行储备和商业银行存款，完成了以瑞士法郎、欧元、英镑、日元、韩元和美元计价的支付，平均结算时间仅约80秒。参与方包括英国央行、法国央行、日本央行、韩国央行和瑞士国家银行，以及摩根大通、花旗银行、德意志银行、法国巴黎银行、瑞银集团、渣打银行和三菱日联金融集团等商业银行。Agorá项目于2024年启动，旨在探索代币化商业银行存款和央行储备如何改善跨境批发支付。今年5月，项目曾报告其原型系统展示了在多货币、多司法管辖区的原子结算能力。BIS表示，7月的试验标志着该项目的一个重要里程碑，随着项目推进，相关测试将持续进行。
国际清算银行表示，项目阿果拉完成了代币化批发跨境支付的真实价值测试，有28家金融机构和央行在17种交易场景下结算了约80万瑞士法郎（约合100万美元）。
国际清算银行周四表示，测试使用代币化的央行准备金和商业银行存款来结算以瑞士法郎、欧元、英镑、日元、韩元和美元计价的支付，平均结算时间约为80秒。
参与者包括英格兰银行、法兰西银行、日本银行、韩国银行和瑞士国家银行，以及摩根大通、花旗银行、德意志银行、法国巴黎银行、瑞银集团、渣打银行和三菱日联金融集团等商业银行。
项目阿果拉是国际清算银行于2024年发起的一项倡议，旨在探索代币化的商业银行存款和央行准备金如何改进跨境批发支付。今年5月，该项目报告称其原型展示了跨多种货币和司法管辖区的原子结算。
国际清算银行表示，7月份的试验标志着一个重要的里程碑，随着项目阿果拉的推进，测试将继续进行。
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QBIS的Agorá项目在批发跨境支付试验中结算了多少金额？
ABIS的Agorá项目在批发跨境支付试验中，结算了约800,000瑞士法郎（约合100万美元）。
QAgorá项目的试验涉及哪些主要中央银行？
A试验的主要中央银行参与者包括：英格兰银行、法兰西银行、日本银行、韩国银行以及瑞士国家银行。
Q试验中使用了哪些技术来改进支付结算？
A试验使用了代币化的中央银行准备金和商业银行存款来进行结算，并且能够实现跨多种货币和司法管辖区的原子结算，平均结算时间约为80秒。
QAgorá项目是哪一年发起的，主要目标是什么？
AAgorá项目于2024年由BIS发起，其主要目标是探索代币化的商业银行存款和中央银行准备金如何改善批发跨境支付。
Q除了中央银行，还有哪些类型的金融机构参与了此次试验？
A除了中央银行，还有多家商业银行参与了试验，包括摩根大通、花旗银行、德意志银行、法国巴黎银行、瑞银集团、渣打银行和三菱日联金融集团。
Bank AI: A Revolutionary Step in the Future of Banking Introduction In an era marked by rapid advancements in technology, Bank AI stands at the intersection of artificial intelligence (AI) and banking services. This innovative project seeks to redefine the financial landscape, enhancing operational efficiency, security measures, and customer experiences through the power of AI. As we embark on this exploration of Bank AI, we will delve into what the project entails, its operational dynamics, its historical context, and significant milestones. What is Bank AI? At its core, Bank AI represents a transformative initiative aimed at integrating artificial intelligence into various banking operations. This project harnesses the capabilities of AI to automate processes, improve risk management protocols, and enhance customer interaction through personalized services. The primary objectives of Bank AI include: Automation of Banking Functions: By leveraging AI technologies, Bank AI aims to automate routine tasks, reducing the burden on human resources and enhancing efficiency. Enhanced Risk Management: The project utilises AI algorithms to predict and identify risks, thereby fortifying security measures against fraud and other threats. Personalization of Banking Services: Bank AI focuses on offering tailored financial products and services by analysing customer data and behaviours. Improving Customer Experience: The implementation of AI-driven solutions, such as chatbots and virtual assistants, aims to provide users with more human-like interactions, revolutionising the way customers engage with banks. With these goals, Bank AI positions itself as a crucial player in rendering banking more efficient, secure, and user-centric. Who is the Creator of Bank AI? Details regarding the creator of Bank AI remain unknown. As such, no specific individual or organisation has been identified in the available information. The anonymity surrounding the project's inception raises questions but does not detract from its ambitious vision and objectives. Who are the Investors of Bank AI? Similar to the project's creator, specific information regarding the investors or supporting organisations of Bank AI has not been disclosed. Without this information, it is challenging to outline the financial backing and institutional support that might be propelling the project forward. Nevertheless, the importance of having a robust investment foundation is pivotal for sustaining development in such an innovative field. How Does Bank AI Work? Bank AI operates on several innovative fronts, focusing on unique factors that differentiate it from traditional banking frameworks. Below are key operational features: Automation: By applying machine learning algorithms, Bank AI automates various manual processes within banks. This results in reduced operational costs and allows human workers to redirect their efforts towards more strategic activities. Advanced Risk Management: The integration of AI into risk management practices equips banks with tools to accurately predict potential threats such as fraud, ensuring that customer information and assets remain secure. Tailored Financial Recommendations: Through continuous learning from customer interactions, the AI systems develop a nuanced understanding of user needs, enabling them to offer tailored advice on financial decisions. Enhanced Customer Interactions: Utilizing chatbots and virtual assistants powered by AI, Bank AI enables a more engaging customer experience, allowing users to have their queries resolved quickly, thus reducing wait times and improving satisfaction levels. Together, these operational features position Bank AI as a pioneer in the banking sector, establishing new benchmarks for service delivery and operational excellence. Timeline of Bank AI Understanding the trajectory of Bank AI requires a look at its historical context. Below is a timeline highlighting important milestones and developments: Early 2010s: The conceptualization of AI integration into banking services began to gain attention as banking institutions recognised the potential benefits. 2018: A marked increase in the implementation of AI technologies occurred when banks started using AI tools like chatbots for basic customer service and risk management systems for improved security handling. 2023: The sophistication of AI continued to advance, with generative AI being introduced for more complex tasks such as document processing and real-time investment analysis. This year marked a significant leap in the capabilities afforded to banks by AI technology. 2024-Current Status: As of this year, Bank AI is on an upward trajectory, with ongoing research and developments poised to further enhance capabilities in banking operations. Continued exploration of AI applications hints at exciting developments yet to come. Key Points About Bank AI Integration of AI in Banking: Bank AI focuses on adopting artificial intelligence to streamline banking processes and improve user experiences. Automation and Risk Management Focus: The project strongly emphasizes these areas, aiming to shift the burden of routine tasks while enhancing security frameworks through predictive analytics. Personalized Banking Solutions: By harnessing customer data, Bank AI enables tailored banking services that cater to individual user needs. Commitment to Development: Bank AI remains committed to ongoing research and development efforts, ensuring its adaptability and ongoing relevance as technology continues to evolve. Conclusion In summary, Bank AI exemplifies a crucial step forward in the banking industry, leveraging artificial intelligence to reshape operational paradigms, enhance security, and promote customer satisfaction. Despite gaps in information surrounding the creator and investors, the clear objectives and functional mechanisms of Bank AI provide a strong foundation for its ongoing evolution. As AI technology continues to advance and merge with the banking sector, Bank AI is well-positioned to significantly impact the future of financial services, enhancing the way we understand and interact with banking.
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