A new survey from global insights firm Globescan revealed that 53% of Americans want companies to support DEI initiatives, while only 14% want them to oppose it. The survey found that even among Republican voters, 38% are in favor of corporate DEI initiatives, while 19% are opposed. On questions of sustainability, consumers are also in favor of companies taking progressive stances. 65% said corporations should support government action on protecting fresh water, and 52% said government action on climate change should be supported. Only 10% and 11%, respectively, said that companies should oppose these issues. Protecting democracy (48%), rights of the LGBTQ+ community (38%), and a woman’s right to choose (34%) are also issues where many consumers believe companies should speak out in support of. However, for the latter two, many believe companies should stay neutral or quiet, at 32% and 39% respectively. Overall, consumers say that CEOs specifically should be expected to speak out to defend social causes. 67% said that chief executives should speak out on the importance of DEI progress, while 71% say it is important for CEOs to address climate change. Across demographic groups, Americans feel strongly about this. Gen Z (78%) are most likely to want CEOs to defend DEI, while 56% of Baby Boomers and older agree. Nearly half (49%) of those surveyed report buying an environmentally-friendly product in the last month, compared to 43% in July 2024. Only 15% of those surveyed said they are “uninterested” in sustainable purchases. Price is the leading obstacle for those looking to buy sustainably, as 55% said they would have liked to buy an eco-friendly product, but didn’t because it was too expensive.
Standards for bank tokens proposed by Kinexys by JP Morgan, MIT – Ledger Insights – blockchain for enterprise
Kinexys by JP Morgan, the bank’s blockchain arm, and the Massachusetts Institute of Technology’s Digital Currency Initiative (MIT DCI) have collaborated on a paper to explore standards for bank tokens on open blockchains. The authors suggest primarily relying on existing Ethereum standards, but propose two new ones they believe are needed for interbank payments. They also suggest areas where regulations might be relaxed for blockchain-based bank payments. By open blockchains they mean permissionless blockchains and also open permissioned blockchains such as Unified Ledgers and Singapore’s Global Layer One. In the latter case, a key differentiating feature is the blockchain node operators are regulated. Part of the paper explores potential standards for bank tokens to enable interoperability for payments between banks. It maps various bank token functions against existing Ethereum token standards. However, this mapping process unveiled a couple of large gaps, particularly around payment orchestration. One example is AML and fraud analysis, which is based on large datasets, so would be processed off chain, and currently would be executed before the payment is initiated. The ERC-20 payment standard has three – payment and recipient wallet addresses and the amount. Banks need more variables. So, when a user wants to make a payment, the wallet would request the format of the payment information needed by the bank (or other entity with authority), and present the appropriate screen to the user for input. Once the user has entered the data, the bank responds to the wallet with the authorization, which is included in the on-chain transfer request. The transfer and authorization would be validated on -chain, for example, to ensure that the payment amount does not exceed the amount authorized. Stepping back, JP Morgan is keen for these standards to be “designed to be narrow in scope and componentized in a way that allows them to be easily composed with other standards,” the authors wrote.
Collibra survey reveals 86% of respondents cite protecting data privacy as a top concern with 76% citing ROI on data privacy and AI initiatives
Collibra survey found that 86% of respondents cite protecting data privacy as a top concern with 76% of respondents citing ROI on data privacy and AI initiatives across their organization. Notably, eight in 10 decision makers also said that data ownership has changed over the last year with the emergence of AI (85%). Despite concerns around data privacy and ROI, the survey indicates a strong overall momentum towards AI adoption, with 86% of organizations planning to proceed with their AI initiatives. However, this enthusiasm varies by company size. While nearly all large companies (96%) intend to forge ahead with their AI plans despite the evolving landscape, smaller (78%) and medium-sized (79%) organizations are exhibiting a more measured approach. On a positive note, the new survey also found that nearly nine in 10 decision-makers say that they have a lot or a great deal of trust in their own companies’ approach [88%] to shaping the future of AI , with three quarters [75%] agreeing that their company prioritizes AI training and upskilling, with decision-makers at large companies (1000+ employees) more likely than those at small companies (1-99 employees) to agree (87% vs. 55%).
Shift from SEO to Generative Engine Optimization (GEO), requiring focus on clear, helpful content with proper technical structure, adding clear labels instead of meta-tags and tracking ‘page mentions’ not just clicks
Search Engine Optimization (SEO) has been the cornerstone of digital visibility for decades. Now, Generative Engine Optimization (GEO) is emerging as its essential companion. The new GEO approach is about writing content that answers real questions thoroughly. This way, AI systems can quote your expertise. With SEO, we added meta-tags that humans never notice. GEO, in terms of metadata, requires adding clear labels that tell AI exactly what each page is about. SEO success meant counting clicks from search results. GEO success means tracking how often an AI tool mentions your page or links back to your content. Digital agencies are now offering “AI Readiness” audits and GEO services to help businesses adapt. The search landscape is evolving from “find information” to “get answers.” Generative Engine Optimization simply means ensuring your business is part of those answers. By focusing on clear, helpful content with proper technical structure, you can maintain visibility regardless of how people search—whether they’re typing in a search box, asking a voice assistant, or chatting with an AI. For most businesses, the principles aren’t actually new: create valuable content that genuinely helps your audience. What’s changing is how that content gets discovered and consumed. Companies that adapt quickly will maintain their connection to customers, while those that ignore this shift are at risk of becoming increasingly difficult to find. Here are some best practices for GEO: Answer the obvious questions first; Use plain headings and short paragraphs; Add behind-the-scenes labels once; Let reputable AI bots in; Earn mentions on trustworthy sites; Keep pages fresh; Track “mention share,” not just clicks.
Senator Warren urges Fed to reconsider Capital One deal for Discover as it would inflict “serious harm” on consumers and the banking system
The top Democrats on congressional banking committees called on the Federal Reserve to reconsider its decision to approve Capital One Financial Corp.’s purchase of Discover Financial Services, saying it would inflict “serious harm” on consumers and the banking system. The decision sounds like the Fed “had predetermined it was going to approve the transaction and either ignored relevant facts or explained them away with baseless assertions copied and pasted from Capital One’s application,” Senator Elizabeth Warren and Representative Maxine Waters said in a letter sent to the Fed. “Treating the transaction as a traditional bank merger was deeply misguided,” the lawmakers wrote. “These are not two traditional banks — they are card giants.” Warren and Waters emphasized the Fed’s review failed to appropriately assess the competitive effects on the credit-card market and didn’t take into account the views of the Consumer Financial Protection Bureau and the Federal Deposit Insurance Corp. The Fed said in an order last month that it consulted with other regulatory agencies including the FDIC and CFPB. Capital One said the deal’s approval follows “an exhaustive, fact-based 14-month examination where legal and regulatory experts examined the deal’s competitive impact, financial stability considerations, community needs, and all other relevant factors.”
Citi Restarts subscription line financing, lending to buyout funds; help banks build relationships with asset managers, who may hire their lenders in the future
Citigroup Inc. is ramping up lending to private equity and private credit groups, working to catch up with peers like JPMorgan Chase & Co. and Goldman Sachs Group Inc. after the bank spent years on the sidelines. The bank told investors it wants to get back into a lending business it retreated from several years ago. Citigroup in the past year returned to offering loans backed by the cash that investors pledge to funds, according to people familiar with the situation, granted anonymity to discuss private matters. As the bank pulled back on this kind of funding, known as subscription line financing, rivals moved to pick up more business. Goldman Sachs, JPMorgan and PNC Financial Services Group scooped up large amounts of the debt from First Republic Bank and Signature Bank, which were big providers of the revolving loans before they failed or were rescued in 2023. Citigroup’s return comes as CEO Jane Fraser pushes to overhaul the bank and boost profits by generating more fee-based revenue and forging ties with alternative asset managers. Last year, the lender hired Vis Raghavan, a rainmaker from rival JPMorgan, to run its global banking business. Subscription lines don’t generate high margins but they do help banks build relationships with asset managers, who may hire their lenders in the future to advise on acquisitions and underwrite junk bond sales. The lines have become extremely popular among fund managers, used by nearly 85% of buyout funds last year, up from just a quarter a decade ago, according to data from MSCI. Altogether, the sublines business is estimated to be roughly $900 billion globally, law firm Dechert LLP wrote last year. The financing is helpful when dealmaking picks up, but it also provides liquidity during a slowdown, which asset managers have faced for years as transactions dried up and some of their bets haven’t paid off. The threat of tighter standards under the previous White House led some large banks to exit capital-intensive lines of business. Regulators last year said they were going to ease rules known as Basel III Endgame, potentially freeing up space for banks to offer more financing. Fraser wants to lift Citigroup’s return on tangible common equity — a key measure of profitability — to 10% to 11% by the end of next year, bringing it more in line with its peers. Last quarter, that metric came in at 9.1%. When private equity firms raise funds, their investors agree to provide cash to fund leveraged buyouts over time. But to access that money, managers have to make a “capital call.” Subscription lines are backed by the promises to meet those calls. Because investors have rarely defaulted on capital calls, subscription lines are seen as safe. Many banks have packaged them into securities, freeing up their balance sheets to make new loans.
Bank of America adopts a four-layer framework for AI- – rules-based automation, analytical models, language classification and GenAI
Banks have long used traditional AI and machine learning techniques for various functions, such as customer service bots and decision algorithms that provide a faster-than-human response to market swings. But modern generative AI is different from prior AI/ML methods, and it has its own strengths and weaknesses. Hari Gopalkrishnan, Bank of America’s chief information officer and head of retail, preferred, small business, and wealth technology, said generative AI is a new tool that offers new capabilities, rather than a replacement for prior AI efforts. “We have a four-layer framework that we think about with regards to AI,” Gopalkrishnan told. The first layer is rules-based automation that takes actions based on specific conditions, like collecting and preserving data about a declined credit card transaction when one occurs. The second is analytical models, such as those used for fraud detection. The third layer is language classification, which Bank of America used to build Erica, a virtual financial assistant, in 2016. “Our journey of Erica started off with understanding language for the purposes of classification,” Gopalkrishnan said. But the company isn’t generating anything with Erica, he added: “We’re classifying customer questions into buckets of intents and using those intents to take customers to the right part of the app or website to help them serve themselves.” The fourth layer, of course, is generative AI. Given the history, it’d be reasonable to think banks would turn generative-AI tools into new chatbots that more or less serve as better versions of Bank of America’s Erica, or as autonomous financial advisors. But the most immediate changes instead came to internal processes and tools. Bank of America is pursuing similar applications, including a call center tool that saves customer associates’ time by transcribing customer conversations in real time, classifying the customer’s needs, and generating a summary for the agent. The decision to deploy generative AI internally first, rather than externally, was in part due to generative AI’s most notable weakness: hallucinations. Banks are wary of consumer-facing AI chatbots that could make similar errors about bank products and policies. Deploying generative AI internally lessens the concern. It’s not used to autonomously serve a bank’s customers and clients but to assist bank employees, who have the option to accept or reject its advice or assistance. Bank of America provides AI tools that can help relationship bankers prep.
Walmart’s second freestanding, 3D-printed store in Huntsville with 16-foot concrete walls to serve as the online grocery pick-up and delivery location
Walmart has partnered with 3D concrete printing company Alquist 3D and general contractor FMGI to complete construction of its second freestanding, 3D-printed store addition. Working with the two firms, Walmart printed the 16-foot concrete walls of the structure, which will serve as an extension of the grocery pickup area in Walmart’s supercenter in Huntsville, Ala. Set to open the week of May 5, the completed Huntsville addition will serve as the retailer’s online grocery pick-up and delivery location as part of an overall store remodel. Other companies working on Walmart’s Huntsville commercial 3D printing project included Sika USA, which supplied customized concrete mixes formulated to address varying environmental conditions. In addition, Alquist’s robotics partner RJC Technology, which furnished robotic systems designed to achieve high-precision printing with reduced labor requirements. “In a commercial construction world that pays so much attention to project timelines and costs, our work with Walmart shows that 3D printing isn’t just a novelty – it’s an innovation ready to scale for retail and other industries,” said Patrick Callahan, CEO of Alquist 3D. “This second project clearly demonstrates how retail expansions can be faster, more cost-effective and less wasteful, paving the way for broader adoption for large-scale commercial builds.”
Morgan Stanley is concentrating on making its AI tools easy to understand, thinking through the associated UX to make them intuitive to use
Koren Picariello, a Morgan Stanley managing director and its head of wealth management generative AI, said Morgan Stanley took a similar path. Throughout the 2010s, the company used machine learning for several purposes, like seeking investment opportunities that meet the needs and preferences of specific clients. Many of these techniques are still used. Morgan Stanley’s first major generative-AI tool, Morgan Stanley Assistant, was launched in September 2023 for employees such as financial advisors and support staff who help clients manage their money. Powered by OpenAI’s GPT-4, it was designed to give responses grounded in the company’s library of over 100,000 research reports and documents. The second tool, Morgan Stanley Debrief, was launched in June. It helps financial advisors create, review, and summarize notes from meetings with clients. “It’s kind of like having the most informed person at Morgan Stanley sitting next to you,” Picariello said. “Because any question you have, whether it was operational in nature or research in nature, what we’ve asked the model to do is source an answer to the user based on our internal content.” Picariello said Morgan Stanley takes a similar approach to using generative AI while maintaining accuracy. The company’s AI-generated meeting summaries could be automatically shared with clients, but they’re not. Instead, financial advisors review them before they’re sent. Meanwhile, Morgan Stanley is concentrating on making the company’s AI tools easy to understand. “We’ve spent a lot of time thinking through the UX associated with these tools, to make them intuitive to use, and taking users through the process and cycle of working with generative AI,” Picariello said. “Much of the training is built into the workflow and the user experience.” For example, Morgan Stanley’s tools can advise employees on how to reframe or change a prompt to yield a better response.
Payment processors looking at platformization to offer an end-to-end product stack adjacent to payments such as advanced fraud prevention, network tokens, real-time account updates, and acceptance rate enhancement tools
“We’re seeing a shift where businesses are now looking for a payment processor that is more inclusive of a product stack, so a one-stop shop for everything,” Justin Downey, vice president of product at Maverick Payments, said. “Payment processors are looking for services that are adjacent to payments. That could be advanced fraud prevention, network tokens, real-time account updater, other tools that can increase the acceptance rate while reducing fraud,” Downey said. He highlighted the quest for a “frictionless checkout experience” — the new gold standard for merchants and consumers alike, as “something that truly makes it easy for customers to submit payments,” he added. The future Downey envisions, and the picture of the present he has painted, is neither purely competitive nor fully collaborative. It’s both. Processors will need to be architects — building unique, defensible intellectual property at their core — as well as curators, integrating complementary services to offer breadth and agility. The platformization trend means processors are stretching beyond payments into tangentially related domains — sometimes encroaching on territory once exclusive to FinTechs or even banks. “Payment processors are expanding into areas that are close to payments, but not exactly payments, like financial services, alternative payment methods, embedded finance,” Downey said. “Processors are in this unique position where, generally, they have a very strong distribution network, and they’re expanding into new product offerings that they can offer to their businesses, all as a one-stop shop. That’s a win-win for everybody,” he added.