Ripple raises $275M for US prime brokerage to meet institutional demand

cointelegraphPublicado em 2026-08-19Última atualização em 2026-08-19

Resumo

Ripple has raised $275 million through a senior unsecured note offering to fund its expansion into U.S. financial services, including prime brokerage, financing, and multi-asset clearing. The private placement by its non-bank prime brokerage, Ripple Prime, attracted diverse institutional investors. Ripple Prime's president views this as a vote of confidence in merging traditional and digital asset infrastructure. This follows Ripple's acquisition of Hidden Road for about $1.25 billion, which established its prime brokerage, and a separate $200 million credit facility secured in May. In July, Ripple launched "Ripple Mint," a platform for institutions to manage its stablecoin, RLUSD, which currently has a market cap of $1.76 billion.

Ripple raised $275 million in a senior note offering that closed on Tuesday to support blockchain enterprise solutions provider’s ongoing US business expansion into financial services.

The senior unsecured notes were issued in a private placement by the company’s non-bank prime brokerage, Ripple Prime, the company announced on Tuesday. Ripple said the note offering attracted a diverse base of institutional investors from financial markets.

Ripple Prime President Noel Kimmel said that the support received during the note offering is a signal of “confidence in our long-term vision for the growing intersection of traditional and digital asset financial infrastructure.”

Ripple said the proceeds of the offering will be used to support its expansion into financial services including prime brokerage, financing and multi-asset clearing.

The company acquired Hidden Road last year in a roughly $1.25 billion deal. That acquisition allowed the Ripple to launch its institutional prime brokerage business, which was later rebranded as Ripple Prime.

In May, Ripple secured a $200 million credit facility from funds managed by Neuberger Berman to expand the lending capacity of its institutional prime brokerage business.

In July, it launched Ripple Mint, a platform that gives institutions new ways to access, mint, redeem and manage its US dollar-pegged stablecoin, Ripple USD (RLUSD). At last look, RLUSD has a market cap of $1.76 billion, according to Coingecko data.

Magazine: What NYSE’s exploration of onchain systems means for financial markets

Perguntas relacionadas

QWhat was the purpose of Ripple's $275 million senior note offering?

AThe proceeds from the $275 million senior note offering will be used to support Ripple's expansion into financial services, including prime brokerage, financing, and multi-asset clearing.

QThrough which entity did Ripple issue the senior unsecured notes?

ARipple issued the senior unsecured notes in a private placement through its non-bank prime brokerage, Ripple Prime.

QWhat was the significance of Ripple's acquisition of Hidden Road, according to the article?

AThe acquisition of Hidden Road in a roughly $1.25 billion deal allowed Ripple to launch its institutional prime brokerage business, which was later rebranded as Ripple Prime.

QWhat is Ripple Mint, as mentioned in the article?

ARipple Mint is a platform launched in July that gives institutions new ways to access, mint, redeem, and manage Ripple's USD-pegged stablecoin, Ripple USD (RLUSD).

QHow did Ripple boost the lending capacity of its prime brokerage business in May?

AIn May, Ripple secured a $200 million credit facility from funds managed by Neuberger Berman to expand the lending capacity of its institutional prime brokerage business.

Leituras Relacionadas

Beyond APR: After Staking, Who Really Owns Your ETH?

Beyond APR: Who Actually Controls Your ETH After Staking? The article challenges the common focus on Annual Percentage Rate (APR) when choosing ETH staking services. It argues that as staking yields converge, the more critical question is who controls the staked assets. The core of non-custodial staking lies in Ethereum's key separation design. A validator uses two keys: a **Signing Key** (for online consensus tasks) and a **Withdrawal Credential** (the ultimate control over funds). Service providers can manage the Signing Key to run the validator but cannot access the staked ETH. The user retains the Withdrawal Credential, which can be kept offline. Post-EIP-7002, users can even force a validator exit directly from their wallet if a service provider disappears. Four roles are involved: 1. **User:** Retains ultimate fund control via the Withdrawal Credential. 2. **Wallet:** Acts as an interface for managing permissions, not an asset owner. 3. **Node Service Provider:** Manages validator operation (Signing Key), carrying risks related to performance and slashing, but not fund theft. 4. **Ethereum Protocol:** Governs the immutable rules for activation, exit, and withdrawal. The piece contrasts this with Liquid Staking like Lido. Users receive a liquid token (e.g., stETH) representing a claim on pooled staked ETH, enabling DeFi composability. However, they do not control individual validator withdrawal credentials; redemption relies on the protocol's withdrawal queue. This trades direct control for liquidity and lower entry barriers. The conclusion is pragmatic: Liquid staking suits users with smaller amounts or high liquidity needs. For long-term holders with 32+ ETH, where yield differentials are minimal, the security model and direct control of a non-custodial, native stake become paramount. The essential question shifts from "What's the APR?" to "Who holds the keys to my staked ETH?"

marsbitHá 59m

Beyond APR: After Staking, Who Really Owns Your ETH?

marsbitHá 59m

Riemann Hypothesis Pushed to 99.55% of Theoretical Boundary: Meta-Architecture AI Reshapes Its 'Brain' Through Thinking

When we marvel at large language models writing poems or coding, scientists face a more challenging question: how can AI tackle complex, long-term scientific problems that require months or years of sustained, uncertain reasoning? Traditional AI approaches use a fixed architecture—like forcing a mathematician, physicist, and engineer to share the same brain. While versatile, such a system often lacks specialization for specific, complex tasks. Recently, an international academic team introduced the Eureka framework, a groundbreaking solution. Instead of a fixed brain, Eureka allows AI to dynamically grow task-specific architectures during problem-solving—creating the optimal dedicated brain for each challenge. In rigorous testing, Eureka completed all 170 complex recursive long-horizon tasks, generating 3,948 verifiable certificates without a single instance of erroneous self-approval or false completion. Eureka demonstrated remarkable adaptability across two distinct scientific challenges: 1. Advancing the Riemann Hypothesis: For this mathematical milestone, Eureka constructed a specialized agent that expanded the scope of a key proof path—local Weil quadratic form positivity—from a ≤ 1/4 to a ≤ 69/200 (0.345). This reaches 99.55% of the theoretical first barrier, representing a significant, verifiable step forward. 2. Reconstructing Physical Theory: In exploring quantum processes and spacetime theory, Eureka generated a theory-discovery agent. It produced five progressive structural findings, rigorously proving that behavioral equivalence does not imply essential equivalence—providing a stricter standard for comparing physical theories. Why Fixed Architectures Fall Short: Predefined AI structures—whether single- or multi-agent—are efficient for known problems but struggle with the long-term uncertainty and structural variability of scientific discovery. They face issues like architecture mismatch and planning failures when early assumptions are invalidated. The Eureka Approach: A Meta-Architecture for Self-Evolution Eureka integrates task execution and brain shaping. Its workflow includes: - Dynamic task compilation into a flexible "obligation graph." - Identifying "architectural hotspots" where specific tools, memory, or collaboration patterns are repeatedly needed. - "Architectural promotion": compiling dedicated "macro-agents" with their own memory, tools, verification standards, and workflows to handle specialized subproblems. - Controlled self-evolution: upgrading macro-agents only when benefits clearly outweigh costs. Results and Outlook: Eureka reduced core contextual overhead by 57.8% and avoided 65.38% of redundant computations. In concurrent testing with 16,000 tasks, it ensured perfect consistency with sequential execution, eliminating data conflicts. This approach paves the way for AI in high-difficulty scientific domains—accelerating mathematical research, aiding theoretical physics discovery, and managing large-scale engineering problems like chip design or drug development. Eureka represents a paradigm shift: future AI capability may depend less on model scale and more on dynamic, task-aware brain shaping.

marsbitHá 1h

Riemann Hypothesis Pushed to 99.55% of Theoretical Boundary: Meta-Architecture AI Reshapes Its 'Brain' Through Thinking

marsbitHá 1h

Trading

Spot
活动图片