Ripple Enters Singapore Central Bank Initiative With RLUSD Pilot

bitcoinistPublished on 2026-03-26Last updated on 2026-03-26

Abstract

Ripple has joined the Monetary Authority of Singapore's (MAS) BLOOM initiative, partnering with Unloq to pilot a programmable cross-border trade settlement system using RLUSD and the XRP Ledger. The pilot, part of MAS's effort to expand tokenized settlement capabilities, aims to reduce friction in trade finance by automating payments upon meeting commercial conditions like shipment verification. This initiative highlights Ripple's focus on positioning RLUSD as an institutional settlement asset within regulated frameworks, enhancing efficiency and transparency for global trade, particularly for SMEs.

Ripple has joined BLOOM, a new initiative from the Monetary Authority of Singapore (MAS), the country’s central bank, and is partnering with trade finance technology firm Unloq on a pilot that uses RLUSD and the XRP Ledger to test programmable settlement in cross-border trade. For crypto markets, the move adds another real-world institutional use case around stablecoin-based settlement infrastructure, this time inside a central bank-led framework.

Announced Wednesday, the pilot sits within MAS’s BLOOM initiative, short for Borderless, Liquid, Open, Online, Multi-currency. The program is designed to expand settlement capabilities using tokenized bank liabilities and regulated stablecoins, positioning Singapore as a testing ground for interoperable payment rails in regulated financial environments.

Ripple Joins Singapore Central Bank Project

Ripple’s specific role in the initiative comes through a joint project with Unloq, a supply chain finance technology provider. The two companies plan to pilot a trade finance workflow built around Unloq’s SC+ infrastructure, which combines trade obligations, settlement conditions and financing workflows into a single execution layer. Ripple said the setup will use its institutional infrastructure, the XRP Ledger and RLUSD.

The core pitch is straightforward: use digital settlement assets to reduce frictions that still slow cross-border trade. In the model described by the companies, payments are released only when commercial conditions are met, such as shipment verification. That creates a more conditional, programmable settlement flow, while also aiming to improve risk visibility and financing access for small and medium-sized businesses.

Fiona Murray, Ripple’s managing director for Asia Pacific, framed the initiative as a regulatory and utility play. “Singapore continues to take a leading role globally in providing the regulatory clarity necessary for the digital asset space to thrive. Ripple is incredibly excited to be part of BLOOM, an initiative that perfectly aligns with our commitment to compliant, real-world utility for blockchain technology.”

She then tied the pilot directly to the mechanics of the platform. “Built on the XRP Ledger, SC+ Solution, Unloq’s smart-contract-driven trade finance platform uses RLUSD to automatically trigger payments the moment the shipment is verified. This partnership combines Unloq’s supply chain expertise with Ripple’s secure technology to make global trade faster and more transparent.”

That matters because the release is not pitching blockchain as a parallel system detached from existing finance. Instead, the emphasis is on integrating digital settlement rails into current trade and financing processes without forcing counterparties to rebuild commercial relationships from scratch. In other words, the pilot is less about replacing trade finance than about reducing operational lag and settlement uncertainty inside it.

Unloq made that case explicitly. Letitia Chau, the company’s president and chief risk officer, said, “BLOOM represents an important step toward modernising trade finance infrastructure in a controlled and regulated environment. Through SC+, we are demonstrating how digital settlement rails can be integrated into existing trade and financing workflows without disrupting commercial relationships.”

She added that the pilot is also meant to test whether the model can scale beyond a narrow proof of concept. “Collaboration with MAS and Ripple enables us to explore scalable, interoperable models for cross-border trade.”

For Ripple, the announcement extends a broader push to position RLUSD as a settlement asset for enterprise use cases rather than a simple exchange-traded stablecoin. The release repeatedly places RLUSD alongside tokenized bank liabilities, suggesting the company wants the stablecoin discussed in the same institutional conversation as other regulated digital cash instruments being explored for settlement.

At press time, XRP traded at $1.4227.

XRP must rise above the 0.618 Fib, 1-week chart | Source: XRPUSDT on TradingView.com

Trending Cryptos

Related Questions

QWhat is the name of the initiative launched by the Monetary Authority of Singapore (MAS) that Ripple has joined?

AThe initiative is called BLOOM, which stands for Borderless, Liquid, Open, Online, Multi-currency.

QWhich two companies are partnering on the pilot project within the BLOOM initiative?

ARipple is partnering with Unloq, a supply chain finance technology provider, on the pilot.

QWhat is the primary goal of using RLUSD and the XRP Ledger in this pilot program?

AThe primary goal is to test programmable settlement in cross-border trade to reduce frictions, where payments are automatically released only when pre-defined commercial conditions (like shipment verification) are met.

QAccording to Fiona Murray, what role does Singapore play in the global digital asset space?

AFiona Murray stated that Singapore continues to take a leading role globally in providing the regulatory clarity necessary for the digital asset space to thrive.

QHow does Ripple aim to position its RLUSD stablecoin through this initiative, according to the article?

ARipple aims to position RLUSD as a settlement asset for enterprise use cases and institutional digital cash instruments, rather than just a simple exchange-traded stablecoin.

Related Reads

Dan Koe: The Counterintuitive Truth—You Don't Need to Remember Everything You Read

Dan Koe: The Counterintuitive Truth — You Don't Need to Remember Everything You Read The central idea is that deliberately trying to remember information is often misguided. True learning isn't about memorizing facts but about having important knowledge surface naturally when needed through use. Most forgetting is normal, not a failure. The article reframes learning using a control theory framework—a four-step feedback loop: having a clear Goal, accurately Sensing your current state, Comparing the gap, and Acting to close it. Most learning stalls because people only do step 2 (blind input) without a goal to create the necessary "error signal" for focused action. The most effective method is to start with output, not input. Begin a meaningful personal project first, and learn only what's necessary to complete it. This project-driven, "just-in-time" learning ensures knowledge is contextual and retained. The concept of a "Second Brain" often fails because it becomes a digital graveyard—over-collected and under-utilized. The goal should be building a "Second Subconscious"—a dynamic system that proactively surfaces relevant ideas during creation, not a static storage vault. Tools like Obsidian+Claude or Eden can help by automating organization and enabling semantic search, but their value depends on linking knowledge to active projects. Ultimately, what matters is not what you store, but what you filter and internalize. Focus on ideas that shape your worldview, use projects as filters, and transform collected material through writing and sharing. AI should be used to reduce friction in research and editing, not to formulate your core views. In conclusion, remembering is a byproduct, not the goal. Knowledge that sticks comes from pursuing personal goals, applying it in real projects, and digesting it through creation. The tools are merely aids; the crucial step is to start doing meaningful work and let the necessary knowledge find you.

marsbit55m ago

Dan Koe: The Counterintuitive Truth—You Don't Need to Remember Everything You Read

marsbit55m ago

A New Era: The Fundamental Transformation of China's Entrepreneurs

A profound generational shift is underway among Chinese entrepreneurs. The wealth and influence once dominated by real estate and internet giants is now being claimed by a new wave of founders driving breakthroughs in AI, semiconductors, and robotics. This change is vividly reflected in 2026's wealth rankings. Figures like Zhang Yiming (ByteDance), Liang Wenfeng (DeepSeek), Chen Tianshi (Cambricon), and Wang Xingxing (Unitree Robotics) are ascending. Their wealth stems not from traditional business models but from market expectations for future technological competitiveness, with AI, chips, and smart hardware becoming the primary engines of wealth creation. Their common trait is a foundational focus on technology, often starting from the laboratory rather than a business plan. Examples include Chen Tianshi's decade-long push in AI chips, Liang Wenfeng's core algorithmic innovations at DeepSeek with a compact team, and Zhu Yiming's "no salary until profitable" 9-year journey to build Changxin Memory into a global DRAM player. This transition marks a fundamental shift in China's economic imperative: from commercial expansion and learning to indigenous innovation and deep industrial capability. While the previous generation built the foundational market and infrastructure, this new cohort is tasked with achieving global leadership in core technologies, moving China from "keeping pace" to pioneering original, breakthrough innovations that are industrialized at scale. The baton is being passed to those competing on the world stage through technological originality.

marsbit55m ago

A New Era: The Fundamental Transformation of China's Entrepreneurs

marsbit55m ago

Once-Popular Web3 Enters Wave of Layoffs

The once-hot Web3 industry is experiencing a severe wave of layoffs. While many companies attribute job cuts to AI-driven restructuring, the primary reason is often financial pressure. The Web3 sector, at the intersection of tech and finance, has been hit particularly hard. Employees at major cryptocurrency exchanges report sudden, impersonal layoffs—often with system access revoked overnight—and minimal or no severance. Common tactics include setting impossible performance targets or terminating employees for minor policy violations. The working atmosphere has become toxic, marked by intense monitoring, excessive meetings, and management obsessed with control and internal politics rather than product innovation. The industry's core business model is collapsing. Exchange revenue from trading fees and listing charges has plummeted due to a decline in quality projects and retail investor exodus. Events like the massive forced liquidation on October 10th further shattered confidence. Competition from on-chain derivatives platforms and prediction markets is intensifying the downturn. As layoffs continue, displaced workers struggle to find new opportunities. Many transition to the AI sector, but face significant bias from traditional finance and even some AI firms, which view crypto industry experience with suspicion. The current downturn appears more structural than cyclical, driven by unsustainable practices, internal strife, and a failure to innovate, raising questions about the industry's future trajectory.

marsbit1h ago

Once-Popular Web3 Enters Wave of Layoffs

marsbit1h ago

Trading

Spot

Hot Articles

What is $BANK

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 personalised 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. Personalisation 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 conceptualisation 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 emphasises these areas, aiming to shift the burden of routine tasks while enhancing security frameworks through predictive analytics. Personalised 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.

266 Total ViewsPublished 2024.04.06Updated 2024.12.03

What is $BANK

Discussions

Welcome to the HTX Community. Here, you can stay informed about the latest platform developments and gain access to professional market insights. Users' opinions on the price of BANK (BANK) are presented below.

活动图片