Ripple Plans BC Payments Acquisition to Expand in Australia

TheNewsCryptoPublished on 2026-03-11Last updated on 2026-03-11

On March 10, Ripple publicised that it has plans to acquire BC Payments to have an Australian Financial Services Licence (AFSL) as it looks to expand its presence in the Asia Pacific region.

In the statement, Ripple added that having the AFSL via the acquisition will help the firm to provide Ripple Payments, an end-to-end payments platform that handles the “full lifecycle” of a transaction and amalgamates both traditional banking and crypto services.

The managing director at APAC Ripple mentions that Australia remains the prominent market for Ripple and an AFSL makes the ability of scaling Ripple Payments across the region possible.

The statement does not give any hint regarding the financial terms of the BC Payments acquisition. Ripple mentioned that currently it has more than 75 regulatory licences around the world, which positions the firm in a strong position to work with institutions looking to expand into digital asset solutions and infrastructure.

The Robust Position of Ripple in the Industry

In February, Ripple got a full EU electronic money institution licence in Luxembourg. At the end of 2025, the U.S. Office of the Comptroller of the Currency gave Ripple a conditional approval to become a national trust bank charter.

Ripple’s creation and highly promoted token XRP is now the fifth-largest crypto asset in the world, having $85.1 billion in market capitalisation. At the time of writing, it was trading at $1.38, up 1.24% in the last 24 hours and 4.01% down in the past month, as per CoinMarketCap.

At the same time Ripple’s dollar-pegged stablecoin, RLUSD, has about $1.6 billion in market cap, positioning it as the 10th-biggest stablecoin. Recently, it was also reported that the stablecoins generated around $33 trillion in 2025.

In January 2026, Ripple also secured a great collaboration with LMAX Group to widen the institutional usage of RLUSD.

Highlighted Crypto News Today:

Pump.fun Price Analysis: PUMP Holds Near $0.00207 as Platform Seeks Lawsuit Dismissal

TagsAustraliaLicenseRipple

Related Questions

QWhat is the main reason Ripple plans to acquire BC Payments in Australia?

ARipple plans to acquire BC Payments to obtain an Australian Financial Services Licence (AFSL), which will help the company expand its presence in the Asia Pacific region and scale its Ripple Payments platform.

QHow many regulatory licenses does Ripple currently hold worldwide according to the statement?

ARipple currently holds more than 75 regulatory licenses around the world.

QWhat significant license did Ripple obtain in Luxembourg in February?

AIn February, Ripple obtained a full EU electronic money institution license in Luxembourg.

QWhat is the market capitalization of Ripple's XRP token and its current ranking?

ARipple's XRP token has a market capitalization of $85.1 billion, making it the fifth-largest crypto asset in the world.

QWhich stablecoin does Ripple issue and what is its approximate market cap?

ARipple issues a dollar-pegged stablecoin called RLUSD, which has a market cap of about $1.6 billion, making it the 10th-largest stablecoin.

Related Reads

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

AI is reshaping the labor market's value proposition. The traditional four-year college degree is losing its appeal as a guaranteed career path, while skilled blue-collar trades like electricians, welders, and plumbers are experiencing historic demand and wage premiums. This shift is driven by dual pressures: AI's displacement of certain white-collar roles and a booming need for physical infrastructure and data center construction. Data confirms the trend. In the U.S., vocational school revenue surged, and a significant portion of recent layoffs are AI-related. Surveys show a majority of Gen Z adults plan to pursue blue-collar work, citing better job security against AI automation. Vocational education interest has exploded recently. Experts cite a psychological shift as younger generations seek tangible, AI-resistant careers and avoid high student debt. In many cases, salaries for skilled trades now match or exceed those requiring a bachelor's degree. In South Korea, semiconductor vocational high schools boast near-total employment, with graduates securing high-paying roles at companies like Samsung. The shortage is structural, exacerbated by a retiring baby boomer workforce and massive infrastructure projects. Companies like JPMorgan Chase, Meta, and Lowe's are investing heavily in training programs. However, overcoming historical stigma and a "perception gap" around trade careers remains a key challenge to closing the talent gap.

marsbit8m ago

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

marsbit8m ago

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

Qualcomm reported its Q3 FY2026 results (ending June 2026), with revenue of $9.95B, down 4% YoY but above expectations. Gross margin declined to 53.1%, pressured by rising costs across manufacturing and memory. Key business segments showed mixed performance: Handset revenue fell 19.6% YoY to $5.09B, dragged by an 11% decline in non-Apple Android shipments and weaker high-end mix. Conversely, Automotive revenue surged 61% to $1.59B, and IoT grew 9% to $1.83B. Core operating profit dropped 41% YoY due to margin compression and higher expenses. Management's Q4 FY2026 guidance projects revenue of $9.7B-$10.5B, in line with consensus, but Non-GAAP EPS guidance of $2.05-$2.25 fell short of expectations. Amidst persistent weakness in its core handset market, Qualcomm is pursuing growth in AI, focusing on Edge AI (smartphones, PCs, automotive) and Data Center AI. Its data center strategy includes four pillars: AI accelerators (e.g., AI200), commercial CPUs (Dragonfly C1000), custom silicon, and connectivity solutions. While these initiatives initially boosted its stock, concerns over AI capital expenditure sustainability have since erased those gains. The company targets $5B in data center revenue for FY2027 and $15B for FY2029. The report concludes that with the traditional handset business still under pressure, the data center opportunity is currently viewed as a longer-term option, and a more conservative valuation based on core operations may be warranted until AI contributions materialize.

marsbit13m ago

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

marsbit13m ago

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

At the 2026 YC Startup School, Jeff Dean outlined his vision for AI's next phase, shifting focus from simply scaling models to building intelligent, autonomous systems. He believes AI's progress is no longer just about creating smarter models, but about integrating them into systems capable of long-term, iterative work, automated experimentation, and continuous learning. This evolution moves the competition from "who has the bigger model" to "who can best organize intelligence." Dean suggests AI capabilities are now comparable to a junior engineer, enabling the automation of complex workflows. However, the true challenge and opportunity lie in managing these AI "workers" at scale. He emphasizes the importance of **context engineering**—structuring tools, memory, and feedback loops—over raw model power. For startups, this means building deep expertise in niche domains where general models currently fail (near 0-1% success rates), leveraging proprietary data, specialized tools, and domain-specific evaluators. A recurring theme is re-examining fundamental constraints. Dean's past work, like moving Google's search index to memory or creating the TPU, stemmed from questioning outdated assumptions about hardware and cost. He sees similar inflection points today, particularly in **specialized inference hardware** to drastically reduce latency and energy consumption for real-time Agent operation. Notably, he points out that in modern AI systems, the dominant cost is often not computation but **data movement**. Reliable, long-running Agents require robust system design, borrowing concepts from distributed computing like checkpointing, state management, and parallel exploration to handle failures and maintain progress over days or weeks. As AI automates execution, the scarcest human skills will shift to **defining clear specifications**, **judging what problems are worth solving** (taste), and designing effective feedback loops. Ultimately, Dean's framework prioritizes understanding the problem deeply, identifying the true bottlenecks, and systematically building closed-loop systems where AI can not only perform tasks but also improve AI itself.

marsbit13m ago

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

marsbit13m ago

Trading

Spot
活动图片