Visa announced the general availability of the Visa Commercial Solutions (VCS) Hub, a breakthrough platform that redefines the future of commercial payments for issuers and fintechs worldwide. The VCS Hub represents a transformational leap forward, engineered to deliver a smarter, more seamless experience for all users. As expansion continues, the VCS Hub will also incorporate next-generation AI capabilities, ultimately offering issuers the ability to unlock a unified, intelligent platform that turns complexity into simplicity. Following a successful pilot, the VCS Hub is now available broadly, enabling issuers and fintechs to deliver powerful commercial payment and embedded finance experiences, turbocharged by automation and seamless integration. For existing users, the platform offers an end-to-end payables solution, enabling full invoice and supplier payments, while also supporting flexible ad hoc payments to efficiently manage business needs. For embedded payments, seamless integration into accounting solutions is a core capability, making it easier and more secure for organizations to manage payments and focus on other essential business priorities. The VCS Hub will continue to expand and be enhanced with additional commercial payment solutions and capabilities. GenAI will be at the core of that, transforming how business gets done. Key enhancements include: AI-Powered Payables: Automate accounts payable with GenAI-driven workflows that anticipate business needs, optimize cash flow and reduce manual bottlenecks. Embedded Payments: Integrate payment capabilities into business applications—accounting, ERP or custom workflows—using Visa’s open APIs and intelligent orchestration. Reporting and Insights: Harness advanced analytics and GenAI to surface actionable insights, predict trends and empower smarter business decisions in real time. Personalized Experiences: User experiences can be tailored by AI, delivering recommendations, alerts and next steps that drive growth and efficiency.
GoDaddy creates trusted identity naming system for AI agents- issues a report on each enrolled agent: verifying its identity, confirming good standing and specifying its location
GoDaddy is launching a trusted identity naming system for AI agents. This system is based on proven technologies and protocols, making it easy to find and trust agents that are legitimate. It builds on the company’s decades of leadership and experience in helping keep the internet safe with domain names, the Domain Name System (DNS) and Secure Sockets Layer (SSL) certificates. The service is engineered to work across protocols via a modular adapter layer and draws on concepts documented in an Internet Engineering Task Force (IETF) draft for an Agent Name Service (ANS). The system issues a report on each enrolled agent: verifying its identity, confirming good standing and specifying its location. Discovery through domains and DNS: Human-readable names map to agent endpoints and metadata. Verifiable identity with X.509: Agent operators enroll, renew and manage certificates issued by trusted authorities, enabling cryptographic verification using the public key infrastructure (PKI). Protocol-agnostic adapters: An open layer translates agent records into the formats used by popular agent frameworks, preventing lock-in. Lifecycle management: Registration, renewal and revocation flows provide security and governance for production deployments. The design draws on work from the IETF on an agent name service, a universal, DNS-inspired directory, that pairs discovery with PKI-backed verification and a protocol-agnostic adapter layer. GoDaddy is driving collaboration with standards bodies and industry partners to ensure broad participation and seamless interoperability.
Google Messages is testing RCS’ new MLS encryption which makes E2E encryption possible across different RCS clients and providers
Google Messages is beginning to test the new Messaging Layer Security (MLS) protocol. Universal Profile 3.0 adds support for MLS, which makes E2E encryption possible across different RCS clients and providers. Google first announced its support for this interoperable protocol in 2023. The GSMA and Apple announced official adoption this March. Google Messages is now beginning to test MLS encryption for RCS. It starts with a new message “Details” (long-press on the chat/text) screen that’s fullscreen compared to the current approach. You get a preview of the message at the top, with Google also showing a “Status” section for “Sent” and Delivered” that explains the new checkmarks. We see Google using the latest single circle design that has yet to become widely available. There’s also a “From” section, while the bottom portion provides more technical details including Type, Priority, Message id and Encryption Protocol. This new design is not widely rolled out in the beta channel. It’s unclear if that’s also the case for MLS as the old UI makes no indication, while Apple has yet to specify when support is coming.
Embedding agentic AI into dispute workflows and fraud controls can help banks unlock RTP’s full potential by adding a predictive, self-learning layer that can autonomously detect, decide, and act
To thrive in a real-time payment’s world, banks must embed AI and cyber resilience into dispute workflows, fraud controls and compliance operations. To unlock the full potential of real-time payments (RTP), financial institutions worldwide are adopting intelligent, AI-led solutions to manage fraud, reduce errors, and enhance operational efficiency. In the US, AI is streamlining dispute resolution, improving accuracy and reducing turnaround times. The UK’s Faster Payments Service (FPS) is setting global standards for secure, real-time operations with integrated compliance controls. Meanwhile, banks in India, Brazil, and across the EU are exploring AI for real-time risk monitoring and dispute handling. The emergence of Agentic AI adds a predictive, self-learning layer that can autonomously detect, decide, and act transforming RTP ecosystems globally. Agentic AI brings autonomous decision-making, enabling systems to detect, act, and learn driving smarter fraud prevention and faster, context-aware dispute resolutions. To unlock RTP’s full potential, banks must center their dispute strategy around AI backed by regulatory alignment, robust assurance mechanisms, and proactive consumer education to ensure secure, compliant, and future-ready operations dispute management in the real-time era.
AmEx’s integration with Navan to enable business users to instantly create unique virtual cards for travel bookings with built-in spending policies while offering automated reconciliation and real-time expense management
Navan announced a new integration with American Express that enables American Express U.S. Business and Corporate Card Members to instantly create unique virtual Cards for travel booked on the Navan Travel platform via Navan Connect. Navan Connect’s “Bring Your Own Card” functionality enables businesses to enjoy the benefits of the travel and expense solution employees love while keeping the benefits of the company’s existing bank and corporate card partner. To support and foster this integration, Navan is participating in the American Express Sync Commercial Partner Program. Combined with the end-to-end Navan T&E solution, the Navan-American Express Sync integration offers: Improved reconciliation. Speed up month-end close with automated reconciliation, all while earning the rewards of your American Express Card. Proactive spending policies. Create unique virtual Cards with built-in spending policies that make managing travel spend simple for finance teams. Real-time expense management. Companies have full visibility into every virtual Card expense the instant it happens with pending and cleared transactions that automatically appear in the Navan Expense dashboard to enable finance leaders to uncover savings opportunities — while keeping budgets and forecasts up-to-date. With Navan there are even more reasons to love your Card. American Express Card Members can earn the rewards of their eligible American Express Card when they use on-demand virtual Cards for travel payments.
A hacker was able to infiltrate a plugin for an Amazon generative AI assistant after obtaining stolen credentials and making unauthorized changes, including secretly instructing it to delete files
Coders who use artificial intelligence to help them write software are facing a growing problem, and Amazon.com Inc. is the latest company to fall victim. A hacker was recently able to infiltrate a plugin for an Amazon generative AI assistant1 after obtaining stolen credentials and making unauthorized changes, including secretly instructing it to delete files from the computers it was used on. The incident points to a gaping hole in the security practices of AI coding tools that has gone largely unnoticed in the race to capitalize on the technology. The hacker effectively showed how easy it could be to manipulate artificial intelligence tools — through a public repository like Github — with the the right prompt. Amazon ended up shipping a tampered version of the plugin to its users, and any company that used it risked having their files deleted. Fortunately for Amazon, the hacker deliberately kept the risk for end users low in order to highlight the vulnerability, and the company said it “quickly mitigated” the problem. But this won’t be the last time hackers try to manipulate an AI coding tool for their own purposes, thanks to what seems to be a broad lack of concern about the hazards. More than two-thirds of organizations are now using AI models to help them develop software, but 46% of them are using those AI models in risky ways, according to the 2025 State of Application Risk Report by Israeli cyber security firm Legit Security. “Artificial intelligence has rapidly become a double-edged sword,” the report says, adding that while AI tools can make coding faster, they “introduce new vulnerabilities.” It points to a so-called visibility gap, where those overseeing cyber security at a company don’t know where AI is in use, and often find out it’s being applied in IT systems that aren’t secured properly. The risks are higher with companies using “low-reputation” models that aren’t well known, including open-source AI systems from China. Dive into the shadow world of hackers and cyber-espionage. The flaw was discovered by the Swedish startup’s competitor, Replit; Lovable responded on Twitter by saying, “We’re not yet where we want to be in terms of security.” One temporary fix is — believe it or not — for coders to simply tell AI models to prioritize security in the code they generate. Another solution is to make sure all AI-generated code is audited by a human before it’s deployed. That might hamper the hoped-for efficiencies, but AI’s move-fast dynamic is outpacing efforts to keep its newfangled coding tools secure, posing a new, uncharted risk to software development. The vibe coding revolution has promised a future where anyone can build software, but it comes with a host of potential security problems too.
New ‘persona vectors’ from Anthropic helps to identify, monitor and control character traits in LLMs before the models can develop undesirable personalities (e.g., becoming malicious, excessively agreeable, or prone to making things up)
A new study from the Anthropic Fellows Program reveals a technique to identify, monitor and control character traits in large language models (LLMs). The findings show that models can develop undesirable personalities (e.g., becoming malicious, excessively agreeable, or prone to making things up) either in response to user prompts or as an unintended consequence of training. The researchers introduce “persona vectors,” which are directions in a model’s internal activation space that correspond to specific personality traits, providing a toolkit for developers to manage the behavior of their AI assistants better. In a series of experiments with open models, such as Qwen 2.5-7B-Instruct and Llama-3.1-8B-Instruct, the researchers demonstrated several practical applications for persona vectors. A key application for enterprises is using persona vectors to screen data before fine-tuning. The researchers developed a metric called “projection difference,” which measures how much a given training dataset will push the model’s persona toward a particular trait. This metric is highly predictive of how the model’s behavior will shift after training, allowing developers to flag and filter problematic datasets before using them in training. For companies that fine-tune open-source models on proprietary or third-party data (including data generated by other models), persona vectors provide a direct way to monitor and mitigate the risk of inheriting hidden, undesirable traits. The ability to screen data proactively is a powerful tool for developers, enabling the identification of problematic samples that may not be immediately apparent as harmful. The research found that this technique can find issues that other methods miss, noting, “This suggests that the method surfaces problematic samples that may evade LLM-based detection.”
Study says while paying down debt remains a top priority for many households, a significant number are failing to take concrete steps that could ease financial strain and accelerate repayment.
A study from consumer finance platform Happy Money has revealed a striking disconnect in how Americans think about debt versus how they manage it. While paying down debt remains a top priority for many households, a significant number are failing to take concrete steps that could ease financial strain and accelerate repayment. More than one-third of respondents ranked debt repayment among their leading financial goals, yet one in five admitted they had taken no action in the past six months to address it. Only a small fraction had consolidated or refinanced debt strategies that could reduce interest costs and shorten payoff timelines. Meanwhile, with average credit card APRs now exceeding 20%, the cost of carrying a balance is more punishing than ever, and over a third of cardholders continue to roll debt from month to month. The consequences of this inaction reach beyond personal finances. The report highlights a direct link between debt-related concerns and well-being: more than 40% of those worried about credit card payments reported an impact on their mental health, and a third said it disrupted their sleep. Middle-aged consumers appear to be feeling the pinch most acutely, with nearly half in the 35–54 age bracket carrying a balance every month. Happy Money CEO Matt Potere believes the solution lies in making responsible borrowing more accessible and better understood. He argues that creditworthy borrowers often overlook the benefits of fixed-rate personal loans, which can replace high-interest revolving credit with predictable monthly payments. This, he says, can help consumers pay off multiple cards faster, save money on interest, and even improve their credit scores. For the FinTech sector, the findings present a clear opportunity. As traditional lenders and digital challengers alike search for ways to attract and retain customers, offering transparent, affordable, and wellness-focused credit solutions could prove a powerful differentiator.
Airbnb launches “Reserve Now, Pay Later”—allowing US guests to book stays upfront, delaying payment until just before the free cancellation period ends
Airbnb has launched a new feature called “Reserve Now, Pay Later” that lets users in the U.S. reserve a property without paying up front, potentially allowing people to cancel their bookings with less hassle if their plans change. The feature is applicable to properties that have a “flexible” or “moderate” cancellation policy. Flexible policies let users cancel their reservation up to 24 hours before they check in, while moderate policies allow for no-fee cancellations until five days before check-in. Users will need to pay the full amount for their booking before the listing’s free cancellation period ends. Airbnb will send users a reminder to pay before that date. Citing a survey it conducted with Focaldata, Airbnb said 55% of those surveyed preferred a flexible payment option while booking a stay, with 42% saying they missed out on properties while trying to figure out payment logistics with other travelers.
Zil Money transforms spending control with AI-powered virtual card features- to automate receipt categorization, analyze transaction data, and generate actionable reports within seconds
Zil Money, a leading fintech solution, has introduced two innovative features in its Virtual Card suite: AI-powered receipt parsing and automated spending analysis reports. These features are designed to provide businesses with real-time insights and complete control over their expenses. Businesses can now automatically track spending, with detailed breakdowns by category, merchant, and more, offering unparalleled visibility into financial transactions. Following the introduction of its Virtual Card, Zil Money has empowered users to create an unlimited number of cards instantly, set customized spending limits, and easily manage expenses. Businesses have reported significant improvements in efficiency, particularly in areas like vendor payments, employee reimbursements, and subscription management. These features automate receipt categorization, analyze transaction data, and generate actionable reports within seconds. With real-time analysis, businesses can instantly verify transactions, categorize purchases, and gain valuable insights, streamlining expense management. This innovation reinforces Zil Money’s commitment to delivering cutting-edge tools that empower smarter financial decisions.