WEX has debuted a tool designed to help people get faster reimbursements from their flexible spending accounts (FSAs). The AI-powered tool is designed to reduce busywork by automating steps like checking receipts, pre-filling claim forms and approving eligible claims for reimbursement. By building smarter tools that automate the most frustrating parts of the process, we’re helping our partners offer a faster, more modern experience without adding extra burden to their teams. The tool includes document verification that informs consumers in real time when information is missing, as well as smart form completion that pre-fills key fields to cut down on manual errors. This new claims tool tackles the most common reasons for denials, like missing documentation or ineligible expenses and gives users a more predictable, seamless experience from start to finish
Two-thirds of BNPL users noted that they would likely increase their usage if tariffs impact prices
52% of consumers have used flexible financing options in the past, with younger consumers (66% of Gen Z and 48% of millennials) leading the charge, according to a new survey from CouponBirds. 68% BNPL users noted that they would likely increase their usage if tariffs impact prices. 27% of those surveyed said they are “very likely” to increase buy now, pay later usage if prices increase, while 41% said they are “somewhat likely.” Even among those who have never used buy now, pay later programs before, 43% report they would consider it if tariffs cause significant price increases. Increasingly, BNPL programs are being used for basic needs. Gen Z (51%) and Gen X (24%) lead all age groups in using these services to finance healthcare costs, a stark contrast to baby boomers at just 12%. Overall, 42% of BNPL users say they’ve used it to make their medical expenses more affordable. While 24% of all users report using these services for rent, mortgage, or utilities, the number jumps to 32% among Gen Z and 19% among millennials. With increased usage comes looming payments. According to the survey, over the past year, 51% of Gen Z users have missed at least one payment, significantly higher than millennials (41%), Gen X (32%) and baby boomers (18%). Among Gen Z users who paid late, 38% report multiple missed payments, while those earning under $50,000 annually are nearly twice as likely to miss payments as their higher-earning counterparts. Among all BNPL users, 66% report being “very confident” in their ability to make payments, while 27% said they are “somewhat confident” and 5% are not very confident.
Zendesk’s AI agents for email can automate over 50% of email interactions instantly with responses that reflect a brand’s tone and style
Zendesk is enhancing its Resolution Platform, an AI-first solution that integrates automation, intelligence, and human context to resolve issues faster. The company is introducing new capabilities, including AI Agents for Email, no-code automation, tailored quality controls, AI-powered Generative Search, and proactive real-time monitoring. These enhancements drive faster resolutions, scalable operations, and high-quality experiences, transforming how businesses engage with and support customers. Zendesk’s focus is on building AI-powered solutions that are simple, easy-to-use, and scalable, ensuring businesses can enhance their customer and employee experience without complexity or compromise. The company’s latest innovations in AI, analytics, and workforce management are aimed at making interactions smarter, more relevant, and reflective of India’s market dynamism. AI and Automation Breakthroughs. Intelligent, autonomous tools streamline customer and employee support and deliver tailored responses for better outcomes: Agentic AI³: Zendesk’s agentic architecture enables AI Agents to reason, adapt, and resolve issues end-to-end without manual setup or fixed flows. AI Agents for Email: Automate over 50% of email interactions instantly with responses that reflect a brand’s tone and style. Instructions for AI Agents: Set custom guidelines to keep AI responses accurate, on-brand, and compliant. Multiple Content Sources for GenAI: AI agents access external knowledge like web crawlers to answer across channels. Use Case Suggestions: AI suggests topics to improve automated resolutions with AI agents. Generative Search: Deliver instant AI-powered answers in the Help Centre, powered by generative AI. Agent Instructions in Copilot: Real-time, step-by-step guidance for agents to resolve complex tasks faster without breaking workflow. Auto Assist Enhancements in Copilot: Suggests accurate responses based on solved tickets.
Orchestra AI’s analytics platform for mid-market businesses detects and categorizes AI agent traffic across major platforms in real-time and measures how AI mentions translate to actual website traffic
Orchestra AI announced the launch of Spyglasses, the first analytics platform designed specifically for mid-market businesses to track and optimize their visibility across AI search platforms. Spyglasses detects and categorizes AI agent traffic across major platforms including ChatGPT, Claude, Perplexity, Google Gemini, and Microsoft Copilot. The platform provides businesses with crucial metrics including AI conversion rates, brand mention frequency across AI responses, and visibility share compared to competitors. Spyglasses utilizes advanced detection algorithms to identify AI agent traffic patterns while providing businesses with actionable insights about their AI search performance. The platform’s analytics dashboard reveals which content AI systems access most frequently, how often brand mentions occur in AI responses, and whether AI-driven awareness translates to website visits and conversions. The platform provides: Real-time AI Agent Detection: Identifies visits from AI systems across all major platforms; AI Conversion Tracking: Measures how AI mentions translate to actual website traffic; Competitive AI Intelligence: Shows how often competitors appear in AI responses; Answer Engine Optimization (AEO) Insights: Provides actionable recommendations for improving AI visibility; Multi-platform Integration: Works with WordPress, WebFlow, Shopify, Next.js, Ruby, Python, and more.
University of Pittsburgh’s mobile ID system allows students, faculty, and staff to download credentials via mobile wallets while offering the option to choose between digital ID and physical card
The University of Pittsburgh has launched a new mobile credentials system for students, faculty, and staff, launched on July 15, 2025. The system, facilitated by Transact, allows users to download credentials via Apple Wallet, Google Wallet, and Samsung Wallet. Users will have the option to choose between the Mobile Panther ID and the current physical contactless Panther Card. The mobile ID is part of a larger effort to modernize campus life, enhance security, and reduce plastic waste. However, mobile ID users will need to use the Pittsburgh Regional Transit app for public transit access. The system also addresses concerns about using the credential if a phone battery dies, with iPhone users able to use Express Mode with power reserve for up to five hours after a phone shuts down due to low battery, and Samsung users able to make up to 15 transactions within 24 hours. Existing students, staff, and faculty who choose the mobile credential can keep their physical Panther Card as a souvenir. Once activated, it functions everywhere the physical card does: Doors and Building Access; Laundry; Libraries; Dining Hall Turnstiles; Bookstore; All Pitt Eats locations; Student Recreation; Residence Hall Gyms; Guest Check-in; Cart Sign-Out; Parking Access.
Salt Edge’s API solution to leverage IBM Z platform to enable banks to meet evolving global open banking regulations and simplify compliance with built-in consent management without overhauling their core systems
Salt Edge, a global provider of API technology for financial services, has confirmed its Open Banking API suite is compatible with IBM LinuxONE and IBM Z platforms, allowing global banks to implement a fully managed API access layer that meets evolving open banking compliance requirements. The solution leverages IBM Z’s performance, security, and scalability to help financial institutions modernize customer-facing services without overhauling their core systems. Salt Edge’s platform supports a broad range of open banking regulatory frameworks, simplifying compliance by exposing consent-based APIs that align with local laws and market needs. When deployed on IBM LinuxONE or IBM Z, Salt Edge’s platform enables banks to: Meet global open banking regulations without incremental development; Offer API-based services to fintechs and third parties with reduced operational burden; Accelerate time-to-market with built-in consent management, developer tools, and third-party onboarding; Leverage the reliability and performance of enterprise-class infrastructure to support critical workloads.
DebitMyData’s platform combines reinforcement learning with blockchain-verified digital identity to offer real-time detection and mitigation of unauthorized AI-generated content, impersonation, and biometric spoofing at scale
DebitMyData, founded by digital sovereignty pioneer Preska Thomas, has launched its LLM Security API Suite, a next-generation platform that combines reinforcement learning with blockchain-verified digital identity. The suite offers the first plug-and-play APIs for Agentic Logos and Agentic Avatars, designed to secure AI at scale across commercial and regulatory settings. The interoperable identity infrastructure enables verification of authenticity and trust in AI outputs. The platform’s reinforcement learning core dynamically adapts to evolving AI manipulation techniques, delivering: Real-time detection and mitigation of unauthorized AI-generated content, impersonation, and biometric spoofing; Built-in global compliance with GDPR, HIPAA, AI Act, and digital sovereignty protocols, ensuring enterprise-ready, auditable privacy. Plug-and-Play Enterprise Security: Agentic Logos™: Secure your brand’s logos with a blockchain-verified fingerprint, enabling instant scanning and flagging of unauthorized usage across AI platforms—with zero technical barriers and GDPR-first privacy controls. Agentic Avatars™: Convert faces and voices into secure, self-authenticating digital signatures, verified via NFT credentials for safe identity gating in synthetic communications.
Darktrace acquires Mira Security to boost encrypted traffic visibility- with policy control and compliance capabilities that allow administrators to decrypt traffic based on predefined rules
Machine learning cybersecurity firm Darktrace PLC has acquired network traffic visibility solutions company Mira Security Inc. for an undisclosed price. Mira Security specializes in encrypted traffic orchestration with solutions that allow organizations to detect, decrypt and analyze encrypted network traffic at scale. The company’s offerings are purpose-built to provide full traffic visibility without compromising privacy, performance, or compliance mandates. Mira Security’s main offering, its Encrypted Traffic Orchestration platform, includes support for both physical appliances and virtual deployments. ETO can intercept SSL/TLS and SSH traffic across any port, decrypting it for analysis and re-encrypting it before forwarding, without the need for complex re-architecting or performance degradation. Mira also offers granular policy control and compliance capabilities that allow administrators to decrypt traffic based on predefined rules while enforcing blocking of outdated or insecure encryption protocols and managing what data is visible to different tools to ensure sensitive information remains protected. The platform additionally supports full visibility into TLS 1.3 traffic, a major challenge for many existing cybersecurity tools due to the protocol’s stricter encryption practices. The combination of Darktrace and Mira Security is said by Darktrace to close the encrypted data blind spot without impacting network performance or requiring complex re-architecting. The closer integration of Mira Security’s in-line decryption capabilities with Darktrace’s existing analysis and understanding of encrypted traffic will also provide organizations with more in-depth visibility across on-premises, cloud and hybrid environments.
Apple’s AI models are trained to refuse requests when necessary and to adapt their tone depending on where the user lives.
A recent machine learning update from Apple reveals how iOS 26 brings faster, safer AI that was trained without your texts, your photos, or your permission. Apple’s training pipeline starts with Applebot, the company’s web crawler. It collects data from sites that allow it, pulling in pages from across the internet in multiple languages. But it’s not scraping everything it finds. Applebot prioritizes clean, structured web pages and uses signals like language detection and topic analysis to filter out junk. It also handles complex websites by simulating full-page loading and running JavaScript. That allows it to gather content from modern pages that rely on interactive design. The goal is to collect useful, high-quality material without ever touching your private information. Instead of gathering more data at any cost, the company is focused on building smarter datasets from cleaner, publicly available sources. Once the data is collected, Apple trains the models in stages. It starts with supervised examples that show the model how to respond in different situations. Then it uses reinforcement learning, with real people rating model responses, to fine-tune the results. Apple also built a safety system that identifies categories like hate speech, misinformation, and stereotypes. The models are trained to refuse requests when necessary and to adapt their tone depending on where the user lives. Features powered by Apple Intelligence now respond faster, support more languages, and stay on track when given complex prompts. The Writing Tools can follow specific instructions without drifting off-topic. The image parser can turn a photo of a flyer into a calendar event, even if the design is cluttered. And all of that happens without Apple seeing what you type or share. If the model needs help from the cloud, Private Cloud Compute handles the request in encrypted memory, on servers Apple cannot access. For users, the big shift is that Apple Intelligence feels more useful without giving up control. For developers, the new Foundation Models framework offers structured outputs, safer tool integration, and Swift-native design. Developers can now use its on-device foundation model through the new Foundation Models framework. That gives third-party apps direct access to the same model that powers Apple Intelligence across iOS 26. Apple isn’t just matching competitors in model size. Its 3 billion-parameter model is optimized for Apple Silicon using 2-bit quantization and KV-cache sharing. That gives it a performance and efficiency edge without relying on the cloud. Developers get faster results, lower costs, and tighter user privacy. Instead of relying on external APIs or background network calls, apps can now integrate powerful AI locally and privately.
ChatGPT’s new ‘router’ function to automatically select the best OpenAI model to respond to the user’s input on the fly, depending on the specific input’s content by switching between reasoning, non-reasoning, and tool-using models
Reports emerged over the last few days on X from AI influencers, including OpenAI’s own researcher “Roon (@tszzl on X)” (speculated to be technical team member Tarun Gogineni) — of a new “router” function that will automatically select the best OpenAI model to respond to the user’s input on the fly, depending on the specific input’s content. Similarly, Yuchen Jin, Co-founder & CTO of AI inference cloud provider Hyperbolic Labs, wrote in an X post, “Heard GPT-5 is imminent, from a little bird. It’s not one model, but multiple models. It has a router that switches between reasoning, non-reasoning, and tool-using models. That’s why Sam said they’d “fix model naming”: prompts will just auto-route to the right model. GPT-6 is in training.” While a presumably far more advanced GPT-5 model would (and will) be huge news if and when released, the router may make life much easier and more intelligent for the average ChatGPT subscriber. It would also follow on the heels of other third-party products such as the web-based Token Monster chatbot, which automatically select and combine responses from multiple third-party LLMs to respond to user queries. Hopefully any hypothetical OpenAI router seamlessly helps direct them to the right model product for their needs, when they need it.