The Treasury Department has debuted rule changes designed to help banks maintain compliance. The department’s Financial Crimes Enforcement Network (FinCEN). The department’s Financial Crimes Enforcement Network (FinCEN) issued an order Friday (June 27) that allows banks to collect tax identification number (TIN) information from a third party rather than from the financial institution’s customer. “We recognize that the way customers interact with banks and receive financial services has changed significantly since 2001, when the initial requirement was enacted into law under the USA PATRIOT Act,” FinCEN Director Andrea Gacki said. “This order reduces burden by providing banks with greater flexibility in determining how to fulfill their existing regulatory obligations without presenting a heightened risk of money laundering, terrorist financing, or other illicit finance activity.” FinCEN issued the order in coordination with the Office of the Comptroller of the Currency (OCC), the Federal Deposit Insurance Corp. (FDIC), and the National Credit Union Administration (NCUA). The order lets banks that are under these agencies jurisdiction use an alternative collection method to obtain TIN information from a third-party rather than from the customer, so long as the bank otherwise adheres to the Customer Identification Program (CIP) Rule. That rule, requires written procedures that allow a bank to obtain TIN information prior to opening an account and “are based on the bank’s assessment of the relevant risks.”
58% of US banks now use both RTP network and FedNow embracing a multi-rail strategy for real-time payments, no longer viewing adoption as a choice between networks
PYMNTS report reveals a shift in the U.S. financial landscape: banks are embracing a multi-rail strategy for real-time payments, no longer viewing adoption as a choice between The Clearing House’s private RTP® network and the Federal Reserve’s public FedNow® Service. This strategic pivot is driven by the imperative to enhance speed, flexibility, and customer satisfaction. By leveraging both networks, financial institutions (FIs) can tap into their respective strengths, ensuring reliable, seamless service regardless of back-end disruptions, thereby boosting trust and minimizing operational risks. This multi-rail approach enhances flexibility for FIs, enabling them to support diverse transaction needs and expand their real-time payment reach across a broader range of customers. This shift reflects rising consumer expectations for seamless, always-on payment experiences and the critical need for resilience in payment infrastructure. Fifty-eight percent of U.S. financial institutions that enable instant payments do so through both the RTP network and the FedNow Service, indicating that a multi-rail approach has become the norm rather than the exception. This marks a change from earlier views, where choosing between the two rails was seen as a barrier to adoption. Consumer preference for speed is overwhelmingly clear, with 90% of individuals stating they would prefer to receive disbursements instantly if given the choice. Moreover, 94% of consumers who chose instant payments reported high satisfaction, significantly higher than the 80% satisfaction reported by those who did not use instant payments. The distinct capabilities of the two networks are being leveraged by FIs: The RTP network supports transactions up to $10 million, while the FedNow Service’s cap will rise from $500,000 to $1 million this summer. This difference in transaction limits, alongside the RTP network’s longer operational history and higher daily payment volume (1.2 million vs. FedNow’s 14,500), illustrates how combining them allows FIs to meet a wider array of customer needs.
The Discover network could enable Capital One to make more money from debit card payments than competitors that are not both a card issuer and network; using the incremental interchange revenue
Capital One Financial has reportedly entered a “new era” after completing its acquisition of Discover Financial Services. With the acquisition, Capital One grew in size and added a debit and credit card network, which could “supercharge” its banking and card businesses. The Discover network could enable Capital One to make more money from debit card payments than competitors that are not both a card issuer and network; use the incremental interchange revenue to boost its bottom line or fund debit card rewards to attract new customers; and fund more investment and enhance rewards and deals to keep expanding its credit business. When Capital One announced in February 2024 that it planned to acquire Discover for $35.3 billion, it said the transaction would create a global payments platform with 70 million merchant acceptance points in more than 200 countries and territories. “Our acquisition of Discover is a singular opportunity to bring together two very successful companies with complementary capabilities and franchises, and to build a payments network that can compete with the largest payments networks and payments companies,” Richard Fairbank, founder, chairman and CEO of Capital One, said. “Through this combination, we’re creating a company that is exceptionally well-positioned to create significant value for consumers, small businesses, merchants and shareholders as technology continues to transform the payments and banking marketplace.”
Google’s new app lets users not only virtually “try on” outfits but also see themselves in motion while wearing them in AI-generated videos
Google launched an experimental app that lets users not only virtually “try on” outfits but also see themselves in motion while wearing them. The new Doppl app from Google Labs builds on the capabilities of the AI Mode virtual try-on feature launched in May by Google Shopping, adding the ability to turn static images into artificial intelligence-generated videos. The dynamic visuals give users “an even better sense for how an outfit might feel.” Users can generate these images and videos by uploading a full-body photo of themselves as well as photos or screenshots of the items they would like to try on. “With Doppl, you can try out any look, so if you see an outfit you like from a friend, at a local thrift shop, or featured on social media, you can upload a photo of it into Doppl and imagine how it might look on you,” Google’s post said. “You can also save or share your best looks with friends or followers.”
Treasure Data has released its MCP Server that allows AI assistants like Claude, GitHub Copilot Chat, and Windsurf to interact directly with intelligent Customer Data Platform (CDP )cc
Treasure Data, the Intelligent Customer Data Platform (CDP) built for enterprise scale and powered by AI, has released its MCP Server, a new open-source connector that allows AI assistants like Claude, GitHub Copilot Chat, and Windsurf to interact directly with your Treasure Data environment. Powered by the open Model Context Protocol (MCP), this solution gives data teams a new superpower: the ability to explore and analyze customer data in an easy and effective way, using plain language and a conversation window. With the Treasure Data MCP Server, teams can query parent segments and segments, explore tables, and analyze data using natural language, making data insights more accessible than ever. The MCP Server acts as a local bridge between your LLM-enabled tools and the Treasure Data platform. Once configured, it allows AI agents to securely interact with your CDP through structured tool calls. Instead of spending an hour writing multi-step SQL and debugging joins, the AI does it for you, writing, refining, and executing the query directly within Treasure Data. The MCP Server handles the permissions, safely limits results, and ensures your API keys and environment variables are managed securely. For most enterprises, the biggest barrier to using AI effectively isn’t the model, it’s the data. If an LLM can’t access high-quality, governed data, it can’t generate useful insights. The Treasure Data MCP Server removes that barrier. The AI accesses the CDP directly, securely and intelligently, so teams can finally start having productive conversations with their customer data.
Walmart’s AI architecture rejects horizontal platforms for targeted stakeholder solutions, each group receives purpose-built tools that address specific operational frictions
Walmart continues to make strides in cracking the code on deploying agentic AI at enterprise scale. One of the retailer’s primary objectives is to consistently maintain and strengthen customer confidence among its 255 million weekly shoppers. Walmart’s AI architecture rejects horizontal platforms for targeted stakeholder solutions. Each group receives purpose-built tools that address specific operational frictions. Customers engage Sparky for natural language shopping. Field associates get inventory and workflow optimization tools. Merchants access decision-support systems for category management. Sellers receive business integration capabilities. The segmentation acknowledges the fundamental need of each team in Walmart to have purpose-built tools for their specific jobs. Store associates managing inventory need different tools from merchants analyzing regional trends. Generic platforms fail because they ignore operational reality. Walmart’s specificity drives adoption through relevance, not mandate. Walmart’s Trend to Product system quantifies the operational value of AI. The platform synthesizes social media signals, customer behavior and regional patterns to slash product development from months to weeks. The system creates products in response to real-time demand rather than historical data. The months-to-weeks compression transforms Walmart’s retail economics. Inventory turns accelerate. Markdown exposure shrinks. Capital efficiency multiplies. The company maintains price leadership while matching any competitor’s speed-to-market capabilities. Every high-velocity category can benefit from using AI to shrink time-to-market and deliver quantifiable gains. Walmart’s approach to agent orchestration draws directly from its hard-won experience with distributed systems. The company uses Model Context Protocol (MCP) to standardize how agents interact with existing services. Walmart leverages decades of employee knowledge, making it a core component of its growing AI capabilities. The company systematically captures category expertise from thousands of merchants, creating a competitive advantage no digital-first retailer can match. The strategic advantage compounds. Walmart’s 2.2 million associates represent proprietary intelligence that algorithms cannot synthesize independently. Their framework applies across industries. Financial services organizations balancing customer needs with regulatory requirements, healthcare systems coordinating patient care across providers, manufacturers managing complex supply chains are all facing similar multi-stakeholder challenges. Walmart’s approach provides a tested methodology for addressing this complexity.
Wirex launches institutional-grade stablecoin payments on Fireblocks digital asset platform- issuing fully stablecoin-backed Visa debit cards, opening stablecoin checking accounts, and managing high-volume treasury and payments
Wirex Pay Chain is now officially supported on Fireblocks, the leading digital asset and payments infrastructure platform. This integration enables Fireblocks’ institutional clients to easily access Wirex Pay’s self-custodial stablecoin payment infrastructure, offering a secure and scalable gateway to stablecoin innovation. Through this support, Fireblocks customers can now issue fully stablecoin-backed Visa debit cards, open stablecoin checking accounts, and manage high-volume treasury and payments — all while retaining complete control over their assets. Wirex Pay redefines enterprise-grade finance with a focus on control, flexibility, and regulatory alignment: Retail App – Stablecoin Checking Accounts: Open stablecoin-backed current accounts with a Visa debit card, enabling instant global payments and yield on balances. Business Banking – Corporate Stablecoin Accounts: Manage fiat and stablecoins with built-in treasury, corporate cards, and real-time settlement—all fully self-custodial. Stablecoin BaaS – Stablecoin Infrastructure APIsStablecoin Infrastructure APIs: Embed stablecoin accounts and card issuing into any fintech or wallet product using modular APIs and smart contracts. Pavel Matveev, Co-Founder of Wirex said, “This unlocks a powerful new chapter in institutional stablecoin adoption — bringing together security and programmable payments infrastructure to Fireblocks’ digital asset network. Now, institutions can launch stablecoin-backed card programs and checking accounts at speed, with full control and built-in compliance.”
Universities athletic departments enter partnerships with PayPal to enable institutional payments for student-athletes in new revenue sharing model
PayPal announced multi-year agreements with the Big Ten and Big 12 Conferences that will modernize the distribution of institutional payments from universities to student-athletes in a new revenue-sharing model. The new institutional payments initiative enables athletic departments to seamlessly dispense payments through PayPal, ensuring a secure, efficient, and transparent way to distribute funds to payees. With the funds in their wallets, students will have the option to access all the benefits of PayPal’s commerce ecosystem, from seamlessly buying tickets to a sporting event or purchasing their books for the year at the university bookstore. The recent court decision, which allows colleges and universities to share revenue directly with student-athletes, stands to revolutionize college sports. This partnership helps make that real by distributing those funds to student-athletes in a fast, simple, and secure way. Venmo is doubling down in its focus on younger consumers by expanding its position as a cornerstone of campus life and commerce. Venmo will be the presenting partner of the first-ever Big Ten Rivalry Series, spanning football, men’s and women’s basketball, embedding the brand in some of the most iconic matchups in college sports. With the Big 12, Venmo will serve as the official partner of the Big 12 Conference across Big 12 football, basketball, and Olympic sports championships for both men and women. Venmo will also be seen across all 16 institutions’ athletic events. Venmo is accelerating the expansion of its commerce capabilities, introducing even more ways to use a Venmo balance beyond peer-to-peer, from everyday purchases on campus to earning rewards in-store and online 1 with the Venmo Debit Mastercard. Venmo will be working with the Big Ten and Big 12 to enable acceptance for real-world campus spending, including at bookstores, for ticketing, concessions, and merchandise, giving students more flexibility to shop and pay with the app they already use every day. Students who use the Venmo Debit Card can for a limited time unlock up to 15% cash back at select national brands with added features like tap-to-pay when added to a mobile wallet, automatic transfers to top up your balance, and the ability to shop internationally anywhere Mastercard is accepted with no foreign transaction fees.
Audos offers a methodology for identifying promising everyday entrepreneurs and matching their unique expertise with viable AI business opportunities aligning personal strengths with business opportunity
Audos announced $11.5 million in combined Pre-Seed and Seed funding. Audos helps solo entrepreneurs quickly turn their expertise into business opportunities through a unique combination of AI tools, human support, and capital. Audos’ Program focuses on four key pillars: The Million-Dollar Business Model: A codified methodology from successful entrepreneurs to identify promising everyday entrepreneurs and match their unique expertise with viable AI business opportunities using a proprietary framework that aligns personal strengths with business opportunity. The result is a focus on founder-market fit, profitability, sustainability, and solving real problems from day one. Rapid Market Validation Through Doing: Helping entrepreneurs offer real services immediately instead of just testing concepts, handling technical implementation and customer acquisition so entrepreneurs can focus on relationships. The Audos Program: An all-in-one system combining human expertise with AI tools. Based on success patterns from the founders’ dozens of successful start-ups, the program helps entrepreneurs discover their business concept, identify and target customers, and create AI applications that operate 24/7 across multiple channels. The program’s agentic capabilities unlock a business’s potential through features including automated customer service, actionable insights, autopilot functions, and strategic iterative updates based on growth opportunities and customer data. Aligned Capital & Support: A model where Audos succeeds only when entrepreneurs succeed, providing flexible financing capital, support, and shared upside within a community of founders. The approach provides day-one resources with no personal risk through a revenue-sharing system, allowing entrepreneurs to grow at their own pace.
Kumo’s ‘relational foundation model’ingests raw database tables and lets the network discover the most predictive signals on its own without the need for manual effort to deliver “zero shot” capabilities on structured data
Stanford professor and Kumo AI co-founder Jure Leskovec argues that his company’s tool, a relational foundation model (RFM), is a new kind of pre-trained AI that brings the “zero-shot” capabilities of large language models (LLMs) to structured databases. Kumo’s approach, “relational deep learning,” sidesteps the manual process with two key insights. First, it automatically represents any relational database as a single, interconnected graph. For example, if the database has a “users” table to record customer information and an “orders” table to record customer purchases, every row in the users table becomes a user node, every row in an orders table becomes an order node, and so on. These nodes are then automatically connected using the database’s existing relationships, such as foreign keys, creating a rich map of the entire dataset with no manual effort. Second, Kumo generalized the transformer architecture, the engine behind LLMs, to learn directly from this graph representation. Transformers excel at understanding sequences of tokens by using an “attention mechanism” to weigh the importance of different tokens in relation to each other. Kumo’s RFM applies this same attention mechanism to the graph, allowing it to learn complex patterns and relationships across multiple tables simultaneously. Leskovec compares this leap to the evolution of computer vision. RFM ingests raw database tables and lets the network discover the most predictive signals on its own without the need for manual effort. The result is a pre-trained foundation model that can perform predictive tasks on a new database instantly, what’s known as “zero-shot.” The RFM can serve as a predictive engine for these agents. Kumo’s work points to a future where enterprise AI is split into two complementary domains: LLMs for handling retrospective knowledge in unstructured text, and RFMs for predictive forecasting on structured data. By eliminating the feature engineering bottleneck, the RFM promises to put powerful ML tools into the hands of more enterprises, drastically reducing the time and cost to get from data to decision.