Capital One is getting a cleaner and leaner Discover Financial Services than the one it agreed to buy 15 months ago as the Riverwoods-based company spent much of the intervening time resolving the issues that made it ripe for sale in the first place. As the firms move toward a May 18 closure date, the Discover brand — which analysts said was largely untainted in consumers’ eyes by its regulatory issues — and the company’s much-desired payment network are what’s left. “They are still getting a great brand from a customer experience perspective,” said Jordan Sternlieb, leader of the banking practice at Chicago-based consulting firm West Monroe. “I think they’re just so well known in that space. I hope that the Capital One team sees that as a valuable part of this acquisition and kind of takes the best of that forward.” Since the acquisition was announced, Discover’s stock has soared nearly 73%. Capital One’s has risen 44%, boosting the implied value of the all-stock deal to about $50.4 billion from $35 billion. After the deal closes, Capital One stockholders will own 60% of the combined company. Capital One has given no indications about its designs for Discover’s Riverwoods campus, which houses about 5,000 workers. But it has said it will remain committed to Discover’s plan for hiring 1,000 employees at its Chatham call center, a goal the company hit in October. Capital One’s executives highlighted the importance of Discover’s brand and personal attention it gives customers on a recent call with analysts discussing its first-quarter earnings. Much of the questioning revolved around Capital One’s plans for Discover. The credit card company’s payment network will allow Capital One to hold on to processing fees normally collected by rivals such as Visa and Mastercard. It also will serve as a key point for growth-spurring innovation. Despite the regulatory issues, Discover’s financial performance has improved since the Capital One takeover was announced. The company’s 2024 net income rose to $1.1 billion, or $4.25 per diluted share, compared with net income of $851 million, or $3.25 per diluted share, in 2023. And the company’s net charge-off rate, the amount of credit card debt it views as uncollectible, decreased 19 basis points to 5.47% during the same time period. Discover’s delinquency rate for credit card loans more than 30 days overdue dropped 17 basis points to 3.66%. The results show Discover’s long-standing regulatory problems are in the past, Morningstar equity analyst Michael Miller said. “When Capital One agreed to acquire Discover, Discover was facing quite a lot of regulatory uncertainty,” Miller said. “None of this ended up being too damaging for Discover. At the time we did not know what the actual end cost would be. Since that time we have gotten more clarity, specifically what the final cost of this was, and it ended up not being that substantial.” The deal, which was approved by shareholders in February, appears headed for closure later this month. Regulators from the Federal Reserve and the Office of Comptroller of the Currency gave their seal of approval April 18, and while Department of Justice staff were divided about whether the DOJ should challenge the tie-up, new antitrust division chief Gail Slater determined there was not enough evidence to try and block it.
Talent development, right data infrastructure, industry-specific strategic bets, responsible AI governance and agentic architecture are key for scaling enterprise AI initiatives
A new study from Accenture provides a data-driven analysis of how leading companies are successfully implementing AI across their enterprises and reveals a significant gap between AI aspirations and execution. Here are five key takeaways for enterprise IT leaders from Accenture’s research.
Talent maturity outweighs investment as the key scaling factor. Accenture’s research reveals that talent development is actually the most critical differentiator for successful AI implementation. “We found the top achievement factor wasn’t investment but rather talent maturity,” Senthil Ramani, data and AI lead at Accenture, told. The report shows front-runners differentiate themselves through people-centered strategies. They focus four times more on cultural adaptation than other companies, emphasize talent alignment three times more and implement structured training programs at twice the rate of competitors. IT leader action item: Develop a comprehensive talent strategy that addresses both technical skills and cultural adaptation. Establish a centralized AI center of excellence – the report shows 57% of front-runners use this model compared to just 16% of fast-followers.
Data infrastructure makes or breaks AI scaling efforts. “The biggest challenge for most companies trying to scale AI is the development of the right data infrastructure,” Ramani said. “97% of front-runners have developed three or more new data and AI capabilities for gen AI, compared to just 5% of companies that are experimenting with AI.” These essential capabilities include advanced data management techniques like retrieval-augmented generation (RAG) (used by 17% of front-runners vs. 1% of fast-followers) and knowledge graphs (26% vs. 3%), as well as diverse data utilization across zero-party, second-party, third-party and synthetic sources. IT leader action item: Conduct a comprehensive data readiness assessment explicitly focused on AI implementation requirements. Prioritize building capabilities to handle unstructured data alongside structured data and develop a strategy for integrating tacit organizational knowledge.
Strategic bets deliver superior returns to broad implementation. While many organizations attempt to implement AI across multiple functions simultaneously, Accenture’s research shows that focused strategic bets yield significantly better results. “In the report, we referred to ‘strategic bets,’ or significant, long-term investments in gen AI focusing on the core of a company’s value chain and offering a very large payoff. This strategic focus is essential for maximizing the potential of AI and ensuring that investments deliver sustained business value.” This focused approach pays dividends. Companies that have scaled at least one strategic bet are nearly three times more likely to have their ROI from gen AI surpass forecasts compared to those that haven’t. IT leader action item: Identify 3-4 industry-specific strategic AI investments that directly impact your core value chain rather than pursuing broad implementation.
Responsible AI creates value beyond risk mitigation. Most organizations view responsible AI primarily as a compliance exercise, but Accenture’s research reveals that mature responsible AI practices directly contribute to business performance. “ROI can be measured in terms of short-term efficiencies, such as improvements in workflows, but it really should be measured against longer-term business transformation.” The report emphasizes that responsible AI includes not just risk mitigation but also strengthens customer trust, improves product quality and bolsters talent acquisition – directly contributing to financial performance. IT leader action item: Develop comprehensive responsible AI governance that goes beyond compliance checkboxes. Implement proactive monitoring systems that continually assess AI risks and impacts. Consider building responsible AI principles directly into your development processes rather than applying them retroactively.
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Goldman Sachs to roll out AI Assistant to all its workforce to serve as backbone of other such tools- Banker Copilot, Legend AI Query, Legend Copilot and Translate AI
Goldman Sachs has been building out its generative AI toolkit. The firm aims to release one of its tools, an AI assistant, to most staff this year. Here’s a look at five such tools — the promise of what they can do, plus who’s using them and how.
- GS AI Assistant. What it does: Goldman Sachs’ in-house version of ChatGPT. Think of the GS AI Assistant as a sidekick for Goldman employees. It uses a chat interface, similar to that of ChatGPT, but can pull its responses from the bank’s confidential data repository. Right now, it’s available to about 10,000 workers; the firm is intending to get it in the hands of the rest of the bank’s workforce of over 46,000 by the end of the year. It can perform a variety of functions, helping executives draft presentations and plan off-site meetings, or serving as a “personal tutor” for quant strategists.
- Banker Copilot. What it does: Streamlines some aspects of investment bankers’ jobs. Members of Goldman’s investment banking division are also set to get an AI boost with the bank’s so-called banker copilot, which makes access to high-level, protected data about matters like deal-making available to eligible users. Only a small group numbering in the dozens has access to it right now since it’s in the early stages of development. But the promise of what the AI assistant could represent for the banking business is hard to deny. Solomon himself has acknowledged the potential for AI to automate large chunks of tedious processes, like drafting S-1 regulatory disclosures for initial public offerings, for instance.
- Legend AI Query: What it does: A search tool that uses AI to navigate the bank’s vast repository of data. Goldman uses Legend, an open-source data management and governance platform. Accessing Goldman’s vast vault of knowledge used to require users to know what they were looking for — as well as where they were looking — ahead of time, using a tool called “Legend Query.” Think of this process as being like perusing dozens of stacks in a library, but without a librarian to help. Enter Legend AI Query, a query tool that saves time by tapping artificial intelligence to serve as that librarian.
- Legend Copilot. What it does: A fast-tracked way to upload data onto Legend, and keep the system organized. Legend Copilot, which launched in October, is a tool primarily designed for use by data engineers to maintain Legend’s infrastructure, and keep its information streams organized for others to access. Legend draws on data that originates in other databases, but still needs to be routed into the centralized Legend system.
- Translate AI: What it does: In-house language translation to and from English. As a global bank, Goldman Sachs has clients worldwide. Sometimes, those clients have a preferred language that’s not English. To reach clients in their non-English speaking language, the bank would historically outsource some of this translation work, but turnaround times could stretch into days.
Capital One closes Discover acquisition with stipulations to address Discover’s outstanding enforcement actions; will reportedly pay $425 million to settle a lawsuit accusing it of cheating savings account depositors
Armed with Discover’s payments network, which competes with those of Visa Inc. and Mastercard Inc., Capital One is poised to capture an even greater share of spending on credit and debit cards that Americans so heavily rely upon. “We are well-positioned to continue our quest to change banking for good for millions of customers,” Capital One Chief Executive Officer Richard Fairbank said. The acquisition wasn’t assured, given the last presidential administration’s skepticism of mergers — and especially those involving finance firms. Bank dealmaking activity was stunted during Joe Biden’s presidency, and some Congressional Democrats opposed the Capital One takeover of Discover, saying it may harm consumers and put the stability of the US financial system at risk. With Donald Trump now in the Oval Office, the Federal Reserve and the Office of the Comptroller of the Currency approved the deal last month after the US Department of Justice decided not to challenge it. But the approval came with stipulations: the OCC mandated that Capital One outline the corrective actions it planned to take to address Discover’s outstanding enforcement actions. In 2023, the firm disclosed that, starting in 2007, it had been charging merchants more than it should have to accept payments on certain credit cards. In connection with the acquisition, Capital One is expanding its board of directors to 15 members from 12. Capital One and Discover customer accounts and banking relationships remain unchanged for now, and information in advance of any forthcoming changes will be provided, according to the statement.
Nvidia’s blueprint for AI factory digital twins allows developers to design, simulate and optimize entire AI factories in physically accurate virtual environments by aggregating detailed 3D and simulation data representing all aspects of the data center
Nvidia announced a significant expansion of the Nvidia Omniverse Blueprint for AI factory digital twins, now available as a preview. The expanded blueprint will equip engineering teams to design, simulate and optimize entire AI factories in physically accurate virtual environments, enabling early issue detection and the development of smarter, more reliable facilities. Built on reference architectures for Nvidia GB200 NVL72-powered AI factories, the blueprint taps into Universal Scene Description (OpenUSD) asset libraries. This allows developers to aggregate detailed 3D and simulation data representing all aspects of the data center into a single, unified model, enabling them to design and simulate advanced AI infrastructure optimized for efficiency, throughput and resiliency. Siemens is building 3D models according to the blueprint and engaging with the simulation-ready, or SimReady, standardization effort, while Delta Electronics is adding models of its equipment. Because these are built with OpenUSD, users get accurate simulations of their facility equipment. Jacobs is helping test and optimize the end-to-end blueprint workflow. Connections to the Cadence Reality Digital Twin Platform and ETAP provide thermal and power simulation, enabling engineering teams to test and optimize power, cooling and networking long before construction begins. These contributions help Nvidia and its partners reshape how AI infrastructure is built to achieve smarter designs, avoid downtime and get the most out of AI factories. The OpenUSD-based models within the blueprint are inherently SimReady, designed from the ground up to be physics-based. This is especially valuable for developing and testing physical AI and agentic AI within these AI factories, enabling rapid and large-scale industrial AI simulations of power and cooling systems, building automation and overall IT operations. A key enhancement to this blueprint is the SimReady standardization workflow.
Open community platform for AI reliability and evaluation allows testing AI models with diverse, real-world prompts across a range of use cases; sees over 400 model evaluations, with over 3 millions votes cast on its platforms
LMArena, the open community platform for evaluating the best AI models, has secured $100 million in seed funding led by a16z and UC Investments (University of California) with participation from Lightspeed, Laude Ventures, Felicis, Kleiner Perkins and The House Fund. In a space moving at breakneck speed, LMArena is building something foundational: a neutral, reproducible, community-driven layer of infrastructure that allows researchers, developers, and users to understand how models actually perform in the real world. Over four hundred model evaluations have already been made on the platform, with over 3 millions votes cast, helping shape both proprietary and open-source models across the industry, including those from Google, OpenAI, Meta, and xAI. The new LMArena includes: a rebuilt UI, mobile-first design, lower latency, and new features like saved chat history and endless chat. The legacy site will remain live for a while, but all future innovation is happening on lmarena.ai. Backers say what makes LMArena different is not just the product, but the principles behind it. Evaluation is open, the leaderboard mechanics are published, and all models are tested with diverse, real-world prompts. This approach makes it possible to explore in-depth how AI performs across a range of use cases.
J.D. Power’s survey reveals 41% customers cite family and friends using a different P2P transfer account as the most likely reason to switch P2P brands for both sending and receiving money
According to new J.D. Power data, network effects, security and ease of use play a large role in determining which “additional” brands consumers are using. Customers say the most likely reason to switch P2P brands for both sending and receiving money is family and friends using a different P2P transfer account (41%). Security concerns (27% for sending money, 25% for receiving) were also among the top reasons. how banks integrate Zelle into their mobile and electronic platforms has a large effect on satisfaction. Zelle integration is largely customizable, so how and where Zelle’s features appear in each bank’s tool vary. Capital One’s P2P customer experience, for example, is enhanced by strong discoverability from the home screen, a pay/move screen featuring a Zelle-centric money movement experience, and a final send screen that displays the recipient’s information to reconfirm money is being sent to the right person. While P2P users are steadfastly loyal to their primary brand, competing providers have a real opportunity to expand their customer base by turning existing users into advocates. Many users are receptive to opening secondary accounts to ensure they can send money across their entire social network. This means an incumbent—or even a new disruptor—doesn’t need to break brand loyalty to make meaningful gains. Sometimes, all it takes is one friend or family member requesting a transfer via another service, and suddenly, that competitor has gained a new user. As brands build out their platforms, it is incumbent on them to understand what differentiates the top performers.
VC Lightspeed changes its regulatory status to a RIA, to enable investing more capital into assets beyond direct startup equity including public and secondary shares, as well as cryptocurrencies
Lightspeed Venture Partners, has changed its regulatory status to broaden its range of investments — following similar moves by Sequoia Capital, Andreessen Horowitz and General Catalyst as they shift away from the traditional VC playbook. Lightspeed has completed the process of becoming a registered investment advisor (RIA), according to a US SEC filing. The move is the culmination of a lengthy regulatory process and gives the firm freedom to invest more capital into assets beyond direct startup equity. It’s also a signal that most of the country’s biggest VCs now have ambitions to expand beyond only investing in startups. Lightspeed is one of the last major venture firms to change its regulatory status, as VCs seek to invest in a wider array of assets, including public and secondary shares, as well as cryptocurrencies. Without the RIA designation, VC firms may only allocate up to 20% of their capital to holdings outside traditional startup equity. Lightspeed, which manages $31 billion in assets, is expected to launch new funds totaling $7 billion and has been expanding its investments in areas such as secondary deals and artificial intelligence.
Vanguard unveils generative AI client summaries for financial advisors
Vanguard launched its first client-facing GenAI capability that equips financial advisors with efficient and personalized content for client communications. Vanguard’s Client-Ready Article Summaries produce customizable synopses of its top-read market perspectives tailored by financial acumen, investing life stage, and tone. It also generates the necessary disclosures to accompany the article summaries, creating an efficient and seamless information sharing experience for advisors. Sid Ratna, Head of Digital and Analytics for Vanguard Financial Advisor Services said “The best advisors can get even better with AI in their client toolkit, and Vanguard’s Client-Ready Article Summaries help advisors drive personalized and actionable conversations that enhance client relationships over the long-term.” Vanguard Financial Advisor Services provides investment services, portfolio analytics and consulting, and research to over 50,000 advisory firms comprising 150,000 advisors.1 Supporting advisors so they can best service their clients is integral to Vanguard’s mission of giving investors the best chance for investment success. In addition to rolling out the Client-Ready Article Summaries, Vanguard continues to experiment with advanced technologies, including spatial and quantum computing and blockchain, to improve investment outcomes, expand investor access, and deliver personalized experiences.
Blackstone, Vanguard, Wellington to launch private markets fund; investors will be able to make quarterly withdrawals capped between 5% and a quarter of the fund’s net-asset value
Blackstone, Vanguard, and Wellington Management are launching an interval fund that will invest in public equities, bonds and private markets, as part of their effort to expand private-market offerings to retail clients. The firms have laid out plans for an interval fund, through which investors will be able to make quarterly withdrawals capped between 5% and a quarter of the fund’s net-asset value. The trio partnered up earlier this year, amid a rising trend of ventures between firms that typically manage public stocks and bonds and those that invest in alternatives. Wellington will manage the interval fund — the first from the partnership — and draw its investments from all three firms. It will allocate up to 60% in public equities, up to 30% in fixed income and as much as 40% in private markets investments, according to the filing, which did not disclose what fees clients will be charged. Traditional asset managers have been seeking ways to move beyond stock and bond funds into higher-margin businesses, including private equity and private credit. At the same time, alternative asset firms have been searching for ways to tap a bigger slice of the retail market. Many have resorted to interval funds which can offer exposure to assets ranging from real estate to direct lending for buyouts.