Small Banks Have Most to Lose From Stablecoin Legislation
Bankers are considering the risk of deposit flight in the face of potential competition from the crypto industry as landmark stablecoin legislation progresses through the US Senate, a concern which threatens to impact small banks across the country the most.
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AccessFintech Appoints Sarah Shenton Chief Executive Officer
Longtime board member and capital markets leader to guide company's next phase of product innovation and global growth NEW YORK, July 28, 2025 /PRNewswire/ -- AccessFintech, the data network driving capital optimization and greater operational capacity, today announced the appointment of Sarah Shenton as Chief Executive Officer, effective immediately. This strategic leadership change marks a significant step for the company as it positions itself for its next phase of growth. Shenton brings over 20 years of experience in operations, engineering and strategic investing to AccessFintech. Most recently, she led the Value Accelerator at Goldman Sachs' alternative assets business, where she collaborated with leadership teams at high-growth companies to drive scale, operational efficiency and commercial success. A long-time advocate for AccessFintech, Shenton led Goldman Sachs' Series A investment in the company and served as a Board Director from 2018 to 2025. She succeeds John Shay, who has effectively led the company as Interim CEO. John will remain part of the firm as Special Advisor to the CEO, ensuring a seamless transition and offering continued support and industry expertise to the leadership team. "Sarah brings a rare combination of operational depth, technical insight and strategic vision to the CEO role," said John Shay. "Her deep knowledge of our company and industry, alongside her commitment to our mission, will be invaluable as we embark on our next growth phase." "I am honored to take on the role of CEO," said Sarah Shenton. "We've created a strong foundation and an ecosystem that matters, and now is the time to build on this success and deliver exceptional value to clients. As technology continues to transform markets, I look forward to working with our amazing team to seize the exciting opportunities ahead." Shenton's appointment follows a unanimous decision by the Board, built on years of close collaboration during her tenure as a Director since 2018. "Sarah's deep market expertise and long-standing commitment to AccessFintech's vision make her exceptionally well-suited to guide the organization into its next chapter of growth," said Kevin Marcus, Partner at WestCap, on behalf of the AccessFintech Board. "We are also deeply grateful to John Shay for his steady leadership as Interim CEO and are pleased he will continue to play an active role on the team as an Advisor." AccessFintech has built a powerful data and workflow platform —the Synergy Network— that connects and distributes 75+ distinct data sets across 250+ leading financial institutions, enabling real-time collaboration and execution management across the post-trade lifecycle. Under Shenton's leadership, the company will continue to strengthen its role as a critical player in capital markets infrastructure and advance its mission to improve financial operations for clients. About AccessFintechAccessFintech enables improved data sharing and workflow collaboration to evolve the financial industry's operating model. At its core is the AccessFintech Synergy Network, a modern and secure collaboration network that allows for resolution and decision-making in one place. It facilitates data collaboration at scale and provides more visibility into transaction data and access to benchmarking insights. Synergy's workflow optimization speeds and simplifies transactions through digital automation, mutualizes risk and allows for better, more enlightened decision-making across organizations and functions. It also offers broad technology distribution that provides industry-wide connectivity to new technologies, reducing the cost of ownership for all. The Synergy Network has built a critical mass of data, participants and solutions with leading financial institutions and processes over a billion transactions every month. For more information, please visit or follow us on LinkedIn or X. Media Contactmarketing@ View original content to download multimedia: SOURCE AccessFintech Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
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In AI we trust?
A recent study by Stanford University's Social and Language Technologies Lab (SALT) found that 45% of workers don't trust the accuracy, capability, or reliability of AI systems. That trust gap reflects a deeper concern about how AI behaves when the stakes are high, especially in business-critical environments. Hallucinations in AI may be acceptable when the stakes are low, like drafting a tweet or generating creative ideas, where errors are easily caught and carry little consequence. But in the enterprise, where AI agents are expected to support high-stakes decisions, power workflows, and engage directly with customers, the tolerance for error disappears. True enterprise-grade reliability demands more: consistency, predictability, and rigorous alignment with real-world context, because even small mistakes can have big consequences. This challenge is referred to as 'jagged intelligence,' where AI systems continue to shatter performance records on increasingly complex benchmarks, while sporadically struggling with simpler tasks that most humans find intuitive and can reliably solve. For example, a model might be able to defeat a chess grandmaster that is unable to complete a simple child's puzzle. This mismatch between brilliance and brittleness underscores why enterprise AI demands more than general LLM intelligence alone; it requires contextual grounding, rigorous testing, and continuous fine-tuning. That's why at Salesforce, we believe the future of AI in business depends on achieving what we call Enterprise General Intelligence (EGI) – a new framework for enterprise-grade AI systems that are not only highly capable but also consistently reliable across complex, real-world scenarios. In an EGI environment, AI agents work alongside humans, integrated into enterprise systems and governed by strict rules that limit what actions they can take. To achieve this, we're implementing a clear, repeatable three-step framework – synthesize, measure, and train – and applying this to every enterprise-grade use case. A Three-Step Framework for Building Trust Building AI agents within the enterprise demands a disciplined process that grounds models in business-contextualized data, measures performance against real-world benchmarks, and continuously fine-tunes agents to maintain accuracy, consistency, and safety. Synthesize: Building trustworthy agents starts with safe, realistic testing environments. That means using AI-generated synthetic data that closely resembles real inputs, applying the same business logic and objectives used in human workflows, and running agents in secure, isolated sandboxes. By simulating real-world conditions without exposing production systems or sensitive data, teams can generate high-fidelity feedback. This method is called 'reinforcement learning' and is a critical foundation for developing enterprise-ready AI agents. Building trustworthy agents starts with safe, realistic testing environments. That means using AI-generated synthetic data that closely resembles real inputs, applying the same business logic and objectives used in human workflows, and running agents in secure, isolated sandboxes. By simulating real-world conditions without exposing production systems or sensitive data, teams can generate high-fidelity feedback. This method is called 'reinforcement learning' and is a critical foundation for developing enterprise-ready AI agents. Measure: Reliable agents require clear, consistent benchmarks. Measuring performance isn't just about tracking accuracy, it's about defining what each specific use case requires. The level of precision needed varies: An agent offering product recommendations may tolerate a wider margin of error than one evaluating loan applications or diagnosing system failures. By establishing tailored benchmarks such as Salesforce's initial LLM benchmark for CRM use cases, and acceptable performance thresholds, teams can evaluate agent output in context and iterate with purpose, ensuring the agent is fit for its intended role before it ever reaches production. LLM benchmark Train: Reliability isn't achieved in a single pass — it's the result of continuous refinement. Agents must be trained, tested, and retrained in a constant feedback loop. That means generating fresh data, running real-world scenarios, measuring outcomes, and using those insights to improve performance. Because agent behavior can vary across runs, this iterative process is essential for building stability over time. Only through repeated training and tuning can agents reach the level of consistency and accuracy required for enterprise use. Turning AI Agents Into Reliable Enterprise Partners Building AI agents for the enterprise is much more than simply deploying an LLM for business-critical tasks. Salesforce AI Research's latest research shows that generic LLM agents successfully complete only 58% of simple tasks and barely more than a third of more complex ones. Truly effective EGI agents that are trustworthy in high-stakes business scenarios require far more than an off-the-shelf DIY LLM plug-in. They demand a rigorous, platform-driven approach that grounds models in business-specific context, enforces governance, and continuously measures and fine-tunes performance. The AI we deploy in Agentforce is built differently. Agentforce doesn't run by simply plugging into an LLM. The agents are grounded in business-specific context through Data Cloud, made trustworthy by our enterprise-grade Trust Layer, and designed for reliability through continuous evaluation and optimization using the Testing Center. This platform-driven approach ensures that agents are not only intelligent, but consistently enterprise-ready. As businesses evolve toward a future where specialized AI agents collaborate dynamically in teams, complexity increases exponentially. That's why leveraging frameworks that synthesize, evaluate, and train agents before deployment is critical. This new framework builds the trust needed to elevate AI from a promising technology into a reliable enterprise partner that drives meaningful business outcomes.
