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Steve Steinour, chairman, president, and CEO said, “Our second quarter results reflect the ongoing successful execution of our organic growth strategy. We are acquiring new customers, deepening relationships, and expanding both net interest income and fee-based revenue through the strength of our product suite and capabilities. Our sustained growth reflects focused execution across both our core businesses and new growth initiatives. We are leveraging our scale as we further expand our well-diversified loan portfolio and continue to deepen client relationships. We have seen both loans and deposits growth of approximately $10 billion over the last year. Our commercial specialty banking teams are delivering solid results, as we broaden capabilities and extend our national reach. The Huntington brand is gaining traction and attracting clients in our newer markets—North and South Carolina and Texas, where the combination with Veritex further supports our long-term growth ambitions. Credit continues to perform well, demonstrated by improved net-charge offs and stable levels of criticized and non-performing assets. This is evidence of our disciplined credit risk management and client selection. We remain confident in our ability to execute our strategy and sustain strong growth, while maintaining our disciplined approach to risk management. We have never been better positioned.”

• Executing Core Strategies: Sustained new customer acquisition momentum with consumer primary bank relationship (PBR); growth of 4% and business PBR of 6% YoY; Drove 11% YoY growth in Key Strategic Fee3 areas; Continuing rollout of full franchise expansion in North and South Carolina; Added new middle market team focused on commercial opportunities in Florida; and Announced acquisition of Veritex in Texas

 

Loans and Leases I Balanced and Diversified Growth: Highlights

• Average loan growth of 1.8% QoQ, or 7.1% annualized
• 40% of 20 growth from new initiatives
• Organic expansion in Texas and North and South Carolina
• Growth in Specialty and Commercial verticals including
• Financial Institutions Group and Funds Finance
• Core growth driven by Regional Banking and Middle Market
• Auto continued to benefit from sustained new
• origination levels
• New CRE originations improving and run-off decelerating

 

Strategic Fee Revenue Focus Areas I Payments

Drivers:

• Payment revenue growth of +7% YoY led by +18% growth in commercial payments
• Merchant services revenue growth of +96% YoY and new account growth of +52% YoY benefitted from new operating model
• TM revenue +10% YoY revenue growth

Highlights:

• 2nd highest growth rate in commercial card spend in FY241
• Debit card spend growth exceeded industry median by
• 60% in FY242
• Strong customer acquisition and enhanced relationships
• supported by Best Bank- Trust award

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Artificial intelligence models that spend more time “thinking” through problems don’t always perform better — and in some cases, they get significantly worse, according to new research from Anthropic that challenges a core assumption driving the AI industry’s latest scaling efforts. The study, led by Anthropic AI safety fellow Aryo Pradipta Gema and other company researchers, identifies what they call “inverse scaling in test-time compute,” where extending the reasoning length of large language models actually deteriorates their performance across several types of tasks. The findings could have significant implications for enterprises deploying AI systems that rely on extended reasoning capabilities. The study reveals distinct failure patterns across major AI systems. Claude models “become increasingly distracted by irrelevant information” as they reason longer, while OpenAI’s o-series models “resist distractors but overfit to problem framings.” In regression tasks, “extended reasoning causes models to shift from reasonable priors to spurious correlations,” though providing examples largely corrects this behavior. Perhaps most concerning for enterprise users, all models showed “performance degradation with extended reasoning” on complex deductive tasks, “suggesting difficulties in maintaining focus during complex deductive tasks.” Major AI companies have invested heavily in “test-time compute” — allowing models more processing time to work through complex problems — as a key strategy for enhancing capabilities.  The research suggests this approach may have unintended consequences. “While test-time compute scaling remains promising for improving model capabilities, it may inadvertently reinforce problematic reasoning patterns,” the authors conclude. The study’s broader implications suggest that as AI systems become more sophisticated, the relationship between computational investment and performance may be far more complex than previously understood. 

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Chee Wee Ang, the chief AI officer at Singapore’s Home Team Science and Tech Agency, a government agency that develops tech capabilities for national security, said AI has helped improve processes significantly. “Some of the information extraction … we see like 200% [improvement]. So that’s a significant improvement in terms of ROI,” Ang said. Yet Ang also pointed out that beyond improving productivity, AI advancements are allowing Singapore’s Home Team to do things that it couldn’t do before, like responding to new kinds of crime or emergency situations. Singapore’s Home Team has 10 departments, including the police force, emergency services, and immigration authorities. Ramine Tinati, the lead at Accenture’s APAC Center for Advanced AI said, “If you give employees a tool to do things faster, they do it faster. But are they more productive? Probably not, because they do it faster and then go for coffee breaks,” Tinati explained. Instead, “if you reinvent the work, then suddenly those coffee breaks don’t become meaningful anymore, because you’re doing something else,” Tinati said, adding that some companies in Asia may be slower to adopt AI because “they don’t think about reinventing the work.”  May Yap, chief information officer at manufacturing solutions provider Jabil, said that her company had been using automation and AI to augment their so-called Golden Eye, the army of workers inspecting phones for scratches and blemishes. Golden Eye workers spend eight hours a day on inspections, and working that long means that “errors will creep in,” Yap said. AI helped to augment the inspection process to account for possible mistakes from human workers. 

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While DeFi lending powered by smart contracts offers compelling advantages—such as transparency, automation, and accessibility—it is unlikely to completely replace traditional loan processing in the near future. The primary reasons are: Regulatory Compliance: Governments will likely introduce regulations that could either hinder or integrate DeFi into traditional finance rather than replace it entirely. Risk Management: Traditional banks offer structured risk management, insurance, and customer support that DeFi currently lacks. Broader Financial Needs: Many consumers and businesses rely on unsecured loans, mortgages, and credit lines that DeFi, with its high collateral requirements, cannot fully accommodate. Instead of replacing traditional finance, a more probable future involves hybrid models where banks integrate smart contracts for efficiency while maintaining regulatory oversight and risk mitigation. Some financial institutions are already exploring blockchain-based lending solutions to streamline operations while preserving customer protections. Smart contracts in DeFi are disrupting traditional loan processing by eliminating intermediaries, reducing costs, and enhancing transparency. While DeFi lending will continue to grow, it is more likely to complement rather than completely replace traditional financial institutions. A future where DeFi and traditional finance coexist—leveraging smart contracts for efficiency while ensuring regulatory protection—seems to be the most viable path forward.

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The financial industry is rapidly transforming as blockchain technology enables real-time, global, and 24/7 markets. Major firms like BlackRock, Fidelity, and Franklin Templeton are investing in public blockchains such as Ethereum, Solana, and Avalanche to tokenize real-world assets—starting with stocks, bonds, and money market funds, and soon potentially expanding to real estate, art, and carbon credits. Unlike traditional systems, blockchain allows for instant settlement, composability, and continuous operation, eliminating time-based trading constraints and cross-border barriers. Legacy infrastructure like session-based markets and end-of-day fund valuation is being replaced by systems that enable second-by-second tracking of yield and daily payouts—even on weekends. However, not all tokenized assets offer true blockchain functionality; some merely provide digital receipts while relying on legacy systems. The real breakthrough comes when ownership and yield distribution are fully on-chain and managed via compliant digital wallets, empowering investors with peer-to-peer transfers, self-custody, and seamless access to a broad range of assets. This shift toward wallet-based finance promises to free up capital, reduce operational risk, and fundamentally reshape how global markets function. Institutions that fail to embrace this evolution risk falling behind.

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Agentic cloud infrastructure startup E2B has raised $21 million in early-stage funding to build out an entirely new, open-source infrastructure for running artificial intelligence agents securely in the cloud. E2B’s vision is to provide businesses with a dedicated, open-source cloud infrastructure stack for AI agents. It believes that the best place for AI agents to be hosted is in secure sandboxed environments in the cloud, where it will provide them with safe computer and browser-use features to automate various complex business tasks. Co-founder and Chief Executive Vasek Mlejnsky explained that enterprises have massive expectations for AI agents due to all the hype around them, but he believes that they won’t easily scale if they’re built on legacy infrastructure that’s not designed to be used by them. “E2B solves this by equipping AI agents with safe, scalable and highly-performant cloud infrastructure, as well as tools that help agents to scale in production,” he added. With E2B’s sandboxed cloud environments, AI agents get access to the same computational capabilities as human workers do, meaning their own computer plus a browser and other tools that enable them to retrieve information, as well as a file system to store that data, and compute platforms for executing AI-generated code. Companies can quickly spin up and shut down these sandboxed cloud environments as needed, and they can scale rapidly to millions of virtual personal computers, allowing enterprises to unleash armies of agents that can work even more efficiently than humans do. It’s all hosted in a simple and secure runtime that enables reinforcement loops for AI training and agentic workflows to run rapidly. E2B aims to position its open-source sandbox protocol as the universal standard for AI agents, and has plans to add additional functionality such as “secrets vaults” and orchestration tools for managing fleets of AI agents from a single console.

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The regulatory momentum around stablecoins has led to the first tangible piece of successful crypto policy in the U.S., the GENIUS Act which President Trump signed into law. The GENIUS Act gives stablecoins legal legitimacy, so long as they play by traditional financial rules of 1:1 reserve backing, anti-money laundering (AML) compliance, and dual charter options through state or federal regulators.  For enterprise users, the evolving landscape signals a new era of trust. It means the digital dollars they’re using to pay contractors in Venezuela or settle trade balances in Nigeria may no longer be Wild West tokens but federally recognized financial instruments by the world’s leading economy. But at the same time, for businesses leveraging stablecoins, whether for global B2B payments, instant invoice payments, or even payroll, the irrevocability of the mechanism, and the new-ness of its end-user experience, could create a new battleground. Instead of routing through correspondent banks or relying on volatile forex pairs, companies can denominate invoices in stablecoins, settle within hours and avoid the friction of legacy payment rails. It’s not about replacing Swift but about skipping it when firms can. Similarily, through USDC or other compliant coins, firms can also pay their employees or contractors in dollar equivalents, settled in minutes, recorded on-chain. Workers are happy when they get paid in a currency that holds value, while employers can benefit from predictability and ease of reconciliation. The dollar now has an internet-native payment rail that is fast, frictionless, and free of middlemen. This groundbreaking technology will buttress the dollar’s status as the global reserve currency, expand access to the dollar economy for billions across the globe.

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Jay Dearborn, president of Corporate Payments at WEX  described embedded payments maturity as a three-stage journey: enablement, customer value creation and monetization. Stage 3 is about turning embedded payments into a profit center. That’s where Dearborn said he sees the biggest gap and the biggest opportunity. “It almost takes someone to bring payments expertise onto the leadership team,” he said. Software companies are good at building great products, but payments is a domain business. Without deep expertise — or the right partner — the monetization piece rarely materializes, he said. That monetization comes in several forms. Dearborn said to start with participating in card economics, which means taking a slice of the acquiring fee, issuing virtual cards or receiving rebates through ACH+ networks. Next is participating in the funds flow itself. Companies that sit inside the accounts payable process, route payments and control disbursements aren’t just facilitators. They’re operators, and operators get paid. Then comes network monetization. Software platforms don’t just process payments; they connect buyers and sellers. That connection creates a closed-loop network that can be optimized, priced and monetized — without ever touching the payment itself, he said. That’s not the only monetizable layer. Speed matters too. Instant access to funds — on either side of a transaction — creates tangible value. Buyers are more likely to pay early. Sellers get paid faster. That value can be priced, packaged and monetized.  “You can build an ecosystem around that account, that payment credential,” Dearborn said. “That’s the next unlock.” Embedded payments are table stakes, he said. Embedding them well and building a business around them is still a competitive edge. 

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AI-native orchestration engines are redefining enterprise software delivery. Ascendion’s AAVA+ platform exemplifies how agentic AI is revolutionizing modern software engineering. Built from the ground up to operate via hundreds of specialized, autonomous agents embedded in every SDLC role — from product management to site reliability — AAVA+ enables lean, goal-based orchestration that aligns tightly with enterprise needs. These agents, numbering over 4,000 in production, connect seamlessly with more than 80 DevOps tools (including Jira, Jenkins, SonarQube, and AppDynamics), creating what Arun Varadarajan, chief commercial officer of Ascendion calls an “open kitchen model” that grants clients end-to-end process visibility. This approach enables organizations to define business outcomes upfront and let the agents generate user stories, code, tests and architecture — all while maintaining enterprise governance, compliance and quality standards. “We don’t just integrate AI,” he said. “We build from it. Every part of AAVA+ is agentic, from ideation to delivery.” “We are moving from task-based AI to goal-first systems,” Varadarajan said. “You tell AAVA+ the kind of portal you want, and the agents come back with a solution. You’re not writing specs, you’re defining outcomes.” By transitioning from task-level automation to goal-oriented orchestration, AAVA+ reduces friction, shortens timelines and bridges the gap between business strategy and technical execution.

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Regions Financial Corp.’s investments in technology and talent drove 10% year-over-year growth in revenue in the second quarter, bringing the regional bank’s total revenue for the quarter to $1.9 billion. “We are very proud of our second-quarter performance as we continue to reap the benefits of the investments we’ve made across our businesses and the successful execution of our strategic plans,” Regions Financial Corp. Chairman, President and CEO John Turner said. Regions Financial Corp., whose Regions Bank subsidiary serves customers across the South, Midwest and Texas, has seen the benefits of these investments across its businesses. In its corporate business, the bank is using natural language processing and other technologies to screen public filings and evaluate product opportunities for large corporate clients. In its consumer business, Regions Financial reskilled and reallocated bankers to focus on opportunities with small businesses and key customer segments, conducted financial education workshops, and centralized processes to save over 200,000 hours and allow bankers to focus on serving customers. In addition, the bank’s improvements to its digital funnel drove 10% year-to-date growth in digital channel checking. Over the past two years, the bank saw its number of mobile banking active users rise 6%, the number of mobile banking logins gain 14% and the share of customer transactions conducted through digital channels increase from 74% to 78%. In its wealth management business, Regions Financial completed a new cloud-based portal to improve infrastructure for its current and future applications serving this segment, enhanced its advisors’ customer relationship management (CRM) systems and fully launched a social media program on LinkedIn. In the wealth management segment, the bank earned a record nominal interest rate (NIR) during the quarter and grew its total number of wealth management relationships by 8.3% compared to last year. Across the bank’s operations, the efficiencies delivered by technology and normal attrition among the workforce will help to pay for continuing investments in technology, Regions Financial Chief Financial Officer David Turner said. “We just have to continue to look for ways to become more efficient,” Turner said. “We and the industry have to do a better job of leveraging all the new technologies that are coming at us pretty rapidly and let our attrition, which is about 6% to 7% of our workforce every year, help pay for some of this technology.” Looking ahead, Regions Financial will continue to modernize its core technology platforms, Turner said.

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