Rocket Companies, the Detroit-based homeownership platform, has announced the completion of its acquisition of Redfin, uniting the most-visited real estate brokerage website with America’s largest mortgage lender. “I’ve used Redfin every day for the last 20 years. It helped me find and fall in love with my first home, completely changing how I thought about real estate,” said Varun Krishna, Rocket Companies CEO. “The Redfin team is best-in-class in building a product experience focused on simplicity. It was a perfect fit for Rocket’s vision of what the homeownership experience should be.” Alongside the acquisition, the companies introduced Rocket Preferred Pricing, offering clients who finance through Rocket Mortgage and purchase with a Redfin agent or buy a Redfin-listed home a one percentage point interest rate reduction for the first year of their loan or a lender credit at closing of up to $6,000. This offer applies to qualified buyers using conventional, FHA, or VA loans. Rocket Mortgage and Redfin plan to roll out additional offerings for buyers, agents, and brokers in the coming months. Redfin has also unveiled a refreshed brand identity as “Redfin Powered by Rocket,” aligning with the unified experience. “The gulf between the American Dream of homeownership and reality has never been wider,” said Redfin CEO Glenn Kelman. “The reason Rocket and Redfin came together was to bridge that gap, so that the people who spend their days dreaming on Redfin.com can easily use Rocket financing to own their dream.”
U.S. banks’ loans to the nonbank financial sector exceeded $1.14 trillion in Q1 2025, with non-depository lending growing by an average of 26% annually since 2012 as “shadow banking” surges. “This interconnectedness between banks and nonbanks adds an extra layer of intermediation, as banks lend to mortgage companies, insurance companies, investment funds (such as mutual funds, money market funds, hedge funds and private capital funds), pension funds, broker-dealers, securitization vehicles and other financial entities, which then lend directly to end users in the economy,” noted the Fed. The growth rate of non-depository financial institutional lending has grown by 26% on average each year since 2012. Getting a bit more granular, the Financial Stability Board estimated late last year that the aggregate FinTech lending across seven jurisdictions came in at $38.5 billion. As the FSB elaborated, “FinTech lending platforms can act as auxiliaries or intermediaries. As auxiliaries, they can be in the form of a ‘marketplace platform,’ which is an online market that allows lenders to trade directly with borrowers (peer-to-peer lending and crowdfunding platforms). Fintech lending platforms can act as intermediaries when they use their balance sheets to originate the lending.” Loans to mortgage and private credit intermediaries each represent 23% of loans outstanding, and loans to business intermediaries and consumer intermediaries represent 21% and 9%, respectively, estimated the Fed. According to a report by the Congressional Research Service, “banks are increasingly lending to NBFIs (nonbank financial institutions) and, at the same time, reducing their lending to commercial and industrial borrowers.” “Increased lending from banks to NBFIs could expose banks to counterparty credit risk and spillover effects during a financial crisis…” the report added. “The size and growth of NBFI suggest that significant amount of financing is being intermediated and held outside of the banking sector. In contrast to the traditional banking model, where banks normally manage risks (e.g., credit, market, liquidity, and operational risks) on their balance sheets, the market-based NBFI financing model shifts risks toward capital markets investors and intermediaries.” As for the risks, the CRS cautioned that the “vulnerabilities affecting financial stability are present in capital markets NBFI, including in certain money-like instruments that face potential ‘runs,’ leverage levels, interconnectedness between nonbanks and banks, data and transparency issues, liquidity mismatch at certain open-end funds, and concentration risk at market intermediaries.” There’s a knock-on effect here, as some financial institutions are grappling with shadow banking stalwarts as competitors, which in turn has shifted activities away from core deposits toward long-term securities and other holdings.
Circle applies to establish national trust bank to oversee USDC reserve
Circle Internet Group has applied for a national trust charter, aiming to establish a national trust bank called First National Digital Currency Bank, N.A. If the application is approved by the Office of the Comptroller of the Currency (OCC), the bank would oversee the management of the reserve of the USDC stablecoin on behalf of Circle’s U.S. issuer. First National Digital Currency Bank, N.A. would also offer digital asset custody services to institutional customers. For Circle, the charter would also help it meet the expected requirements of the proposed stablecoin legislation, the GENIUS Act. “By applying for a national trust charter, Circle is taking proactive steps to further strengthen our USDC infrastructure,” Circle Co-founder, Chairman and CEO Jeremy Allaire said. “Further, we will align with emerging U.S. regulation for the issuance and operation of dollar-denominated payment stablecoins, which we believe can enhance the reach and resilience of the U.S. dollar, and support the development of crucial, market neutral infrastructure for the world’s leading institutions to build on.” Becoming a publicly traded company requires Circle to comply with a higher standard of regulatory oversight, including audits, disclosures and governance practices.
Kraken and Bybit listing their tokenized U.S. stocks just two hours apart indicates growing momentum behind tokenized finance and the broader ambition to decentralize access to traditional markets
In a sign of growing momentum behind tokenized finance, two major crypto exchanges, Kraken and Bybit, unveiled their listings of tokenized U.S. stocks just two hours apart. Kraken is launching 60 tokenized equities under the xStocks brand, powered by Swiss issuer Backed. The offering includes prominent names like Apple, Tesla, and ETFs such as SPY. Two hours later, Bybit, currently the second-largest exchange by crypto trading volume, announced the same product integration on its Spot platform. Kraken’s launch signals a broader ambition to decentralize access to traditional markets. Its xStocks are built on the Solana blockchain and allow users not only to trade them on the exchange but also to withdraw them to self-custody wallets. From there, users can deploy them as collateral across decentralized finance protocols, something conventional stocks can’t match. The exchange plans to expand access to xStocks across more than 185 countries in the coming weeks, with support for additional blockchains to follow. Bybit’s listing supports Ethereum (ERC-20) and Solana (SPL) versions of xStocks, and includes the same basket of high-demand equities. Emily Bao, Bybit’s Head of Spot, said the exchange aims to provide users with more control and choice while remaining within the crypto ecosystem. xStocks offer features such as traditional equities can’t, fractional ownership, on-chain mobility, and round-the-clock trading. By listing them nearly simultaneously, Kraken and Bybit are positioning themselves at the frontier of financial infrastructure. Meanwhile, Robinhood also announced the launch of tokenized versions of U.S.-listed stocks and ETFs, besides a blockchain network.
OpenLedger enables deploying thousands of fine-tuned models using a single GPU without preloading them, by dynamically merging and infering on demand using quantization, flash attention, and tensor parallelism to offer 90% savings in deployment costs
OpenLedger has launched OpenLoRA, a new open protocol that enables developers to deploy thousands of LoRA fine-tuned models using a single GPU, saving up to 90% of deployment costs. Built on cutting-edge research and an open-source foundation, OpenLoRA allows developers to serve thousands of LoRA models on one GPU without preloading them, dynamically merging and infering on demand using quantization, flash attention, and tensor parallelism. This means builders can now scale AI deployment without bloating compute bills. Deployed as a SaaS platform, OpenLoRA makes it radically easier for startups and enterprises alike to launch AI products across verticals, from marketing, legal, education, crypto, customer service, and beyond, without having to replicate the entire model architecture for each use case. It’s a paradigm shift in how fine-tuned intelligence can be deployed at scale. Ram, Core Contributor at OpenLedger said, “With OpenLoRA, we’re redefining the economics of AI deployment, offering the first protocol where developers can serve massive fleets of fine-tuned models with minimal cost and maximum performance.”
Success of Pix and UPI is paving way for a three-stage framework for state-led fast payment systems that involves weighting pre-requisites, implementation and scaling and establishing engagement mechanisms and regulatory adjustments
Pix and Unified Payments Interface (UPI), Brazil and India’s respective instant payment systems, provide two key lessons for governments interested in implementing new fast or immediate payment systems. First, the significant effect that government-led instant payment systems can have on citizens and the financial market transforms financial inclusion and market structures. Second, decisions made during the early stages of the process, such as system pricing and ownership structure, shape the power dynamics between local and international players, as well as incumbent and new entrants. These lessons are shaping an emerging framework governments can use to evaluate their need for central bank-led immediate payment systems, their potential structure, organizational features, and trade-offs involved in implementing a similar approach. The framework is composed of a three-step approach, including prerequisite weighting (i.e., “do we need this system”), the preparations needed to hit the ground running, and the process of setting up new immediate payment systems.
Researchers from MIT, McGill University, ETH Zurich, Johns Hopkins University, Yale and the Mila-Quebec Artificial Intelligence Institute have developed a new method for ensuring that AI-generated codes are more accurate and useful. In the paper, the researchers used Sequential Monte Carlo (SMC) to “tackle a number of challenging semantic parsing problems, guiding generation with incremental static and dynamic analysis.” Sequential Monte Carlo refers to a family of algorithms that help figure out solutions to filtering problems. This method spans various programming languages and instructs the LLM to adhere to the rules of each language. The group found that by adapting new sampling methods, AI models can be guided to follow programming language rules and even enhance the performance of small language models (SLMs), which are typically used for code generation, surpassing that of large language models. João Loula, co-lead writer of the paper, said that the method “could improve programming assistants, AI-powered data analysis and scientific discovery tools.” It can also cut compute costs and be more efficient than reranking methods. Key features of adapting SMC sampling to model generation include proposal distribution where the token-by-token sampling is guided by cheap constraints, important weights that correct for biases and resampling which reallocates compute effort towards partial generations.
Crowdsourced AI benchmarks should be dynamic rather than static datasets, and tailored specifically to distinct use casesAI benchmarks
Over the past few years, labs including OpenAI, Google, and Meta have turned to platforms that recruit users to help evaluate upcoming models’ capabilities. When a model scores favorably, the lab behind it will often tout that score as evidence of a meaningful improvement. It’s a flawed approach, however, according to Emily Bender, a University of Washington linguistics professor and co-author of the book “The AI Con.” Bender takes particular issue with Chatbot Arena, which tasks volunteers with prompting two anonymous models and selecting the response they prefer. To be valid, a benchmark needs to measure something specific, and it needs to have construct validity — that is, there has to be evidence that the construct of interest is well-defined and that the measurements actually relate to the construct,” Bender said. “Chatbot Arena hasn’t shown that voting for one output over another actually correlates with preferences, however they may be defined.” Asmelash Teka Hadgu, the co-founder of AI firm Lesan and a fellow at the Distributed AI Research Institute, said that he thinks benchmarks like Chatbot Arena are being “co-opted” by AI labs to “promote exaggerated claims.” Benchmarks should be dynamic rather than static datasets,” Hadgu said, “distributed across multiple independent entities, such as organizations or universities, and tailored specifically to distinct use cases, like education, healthcare, and other fields done by practicing professionals who use these [models] for work.” Wei-Lin Chiang, an AI doctoral student at UC Berkeley and one of the founders of LMArena, which maintains Chatbot Arena said that incidents such as the Maverick benchmark discrepancy aren’t the result of a flaw in Chatbot Arena’s design, but rather labs misinterpreting its policy.
Capital One 1Q25: Credit card purchase volume is up 5%, auto loan originations are up 22%
Capital One Financial Corporation announced net income for the first quarter of 2025 of $1.4 billion, or $3.45 per diluted common share, compared with net income of $1.1 billion, or $2.67 per diluted common share in the fourth quarter of 2024, and with net income of $1.3 billion, or $3.13 per diluted common share in the first quarter of 2024. Adjusted net income(1) for the first quarter of 2025 was $4.06 per diluted common share. “Last week, we received regulatory approval for our acquisition of Discover and we’re fully mobilized to complete the transaction on May 18th,” said Richard D. Fairbank, Founder, Chairman, and Chief Executive Officer. “The combination of Capital One and Discover will create a leading consumer banking and payments platform with unique capabilities, modern technology, and powerful brands. It leverages Capital One’s technology transformation and digital capabilities across a significantly larger customer franchise. And it offers the potential to enhance competition and create significant value for merchants and customers.
Credit Card
- Ending loans held for investment up $6.6 billion, or 4%, year-over-year; average loans held for investment up $6.8 billion, or 5%, year-over-year
- Purchase volume up 5% year-over-year
- Revenue up $417 million, or 6%, year over-year
Consumer Banking
- Ending loans held for investment up $3.8 billion or 5% year-over-year; average loans held for investment up $3.4 billion, or 5%, year-over-year
- Ending deposits up $24.1 billion, or 8%, year-over-year
- Auto loan originations up $1.7 billion, or 22%, year-over-year
A huge portion of the integration will involve Capital One bringing Discover up to speed on the technology infrastructure it has spent years modernizing; however, running a payment network is new to Capital
Days after winning regulatory approval for its blockbuster acquisition of Discover Financial Services, Capital One Financial said that its expectations for what the integration will cost haven’t changed. The $35 billion transaction has been and will continue to be costly, but Capital One Chairman and CEO Richard Fairbank said that the $1.5 billion estimate for integration expenses during 2027 remain intact — except shifted out by about six months to account for the deal’s longer-than-expected regulatory review. Fairbank told that he thinks this transaction is different from other acquisitions, where the goal is “to take two companies, squash them together and rip out the costs.” “I think that Discover brings us a growth platform, both on the network side and with respect to their card franchise, that allows us to preserve the best of what they do, leverage a lot of Capital One’s capabilities that we bring and build something really special,” Fairbank said. Upon the closing of the Discover merger, the combined company will have $660 billion of assets. Capital One will own a massive chunk — estimated to be between one-fourth and one-third — of the subprime card market. And it will operate Discover’s payment network, instead of having to use Visa’s or Mastercard’s — an element of the transaction that Fairbank has called “the holy grail.” But the road to getting there isn’t completely nailed down. A huge portion of the integration will involve Capital One bringing Discover up to speed on the technology infrastructure it has spent years modernizing. Meanwhile, Discover will take Capital One “back to the world of data centers,” Fairbank said. He added that his bank has experience ramping up the tech stack of a credit card company. However, running a payment network is new to Capital One, and “very complex and very high stakes,” Fairbank said. Another key facet of the merger is Capital One’s effort to increase acceptance of Discover’s payment network internationally. Fairbank characterized this spending as a long-haul type of investment, measured in “a whole bunch of years.” Kyle Sanders, an analyst at Edward Jones, thinks it will take several years for the merger’s benefits to manifest themselves, and that near-term integration challenges “will present obstacles.” Last week, the deal earned the approval of the Federal Reserve and Office of the Comptroller of the Currency, but regulators ordered Discover to pay more than $1 billion in fines and restitution in connection with the company’s earlier overcharging of merchants. When asked about recent regulatory developments, Fairbank said that Capital One knew risk management would be “a big investment,” but the company hasn’t changed its outlook on how much those efforts will cost.