As AI agents take on more responsibility, and especially as the convergence between crypto and TradFi accelerates, worries around transparency and market manipulation will grow. DLT offers a solution. The Identity Management Institute reported companies that integrated blockchain identity systems have already cut fraud by 40% and identity theft by 50%. Applying these guardrails to AI-driven finance can counter manipulation and promote fairness. Moreover, the use of DLTs with fair ordering is growing rapidly, ensuring transactions are sequenced fairly and unpredictably, addressing MEV concerns and promoting trust in decentralized systems. A blockchain-powered, trust-centric model could unlock a new paradigm, “DeFAI”, in which autonomous agents can operate freely without sacrificing oversight. Open-source protocols like ElizaOS, which have blockchain plugins, are already enabling secure and compliant AI interactions between agents across DeFi ecosystems. As AI agents take on more complex roles, verifiable trust becomes non-negotiable. Verifiable compute solutions are already being built by firms like EQTY Lab, Intel and Nvidia to anchor trust on-chain. DLT ensures transparency, accountability and traceability. This is already in motion; on-chain agents are now operating that offer services ranging from trade execution to predictive analytics.
StarTree integrates Model Context Protocol (MCP) support to its data platform to allow AI agents to dynamically analyze live, structured enterprise data and make micro-decisions in real time
StarTree announced two new powerful AI-native innovations to its real-time data platform for enterprise workloads: Model Context Protocol (MCP) support: MCP is a standardized way for AI applications to connect with and interact with external data sources and tools. It allows Large Language Models (LLMs) to access real-time insights in StarTree in order to take actions beyond their built-in knowledge. Vector Auto Embedding: Simplifies and accelerates the vector embedding generation and ingestion for real-time RAG use cases based on Amazon Bedrock. These capabilities enable StarTree to power agent-facing applications, real-time Retrieval-Augmented Generation (RAG), and conversational querying at the speed, freshness, and scale enterprise AI systems demand. The StarTree platform now supports: 1) Agent-Facing Applications: By supporting the emerging Model Context Protocol (MCP), StarTree allows AI agents to dynamically analyze live, structured enterprise data. With StarTree’s high-concurrency architecture, enterprises can support millions of autonomous agents making micro-decisions in real time—whether optimizing delivery routes, adjusting pricing, or preventing service disruptions. 2) Conversational Querying: MCP simplifies and standardizes the integration between LLMs and databases, making natural language to SQL (NL2SQL) far easier and less brittle to deploy. Enterprises can now empower users to ask questions via voice or text and receive instant answers, with each question building on the last. This kind of seamless, conversational flow requires not just language understanding, but a data platform that can deliver real-time responses with context. 3) Real-Time RAG: StarTree’s new vector auto embedding enables pluggable vector embedding models to streamline the continuous flow of data from source to embedding creation to ingestion. This simplifies the deployment of Retrieval-Augmented Generation pipelines, making it easier to build and scale AI-driven use cases like financial market monitoring and system observability—without complex, stitched-together workflows.
Speculation has resurfaced around a possible integration of Ripple’s XRP with SWIFT following integration by SBI Remit
A recent report by Mastercard, titled “Blockchain Technology Fuels New Remittances Business Cases,” highlights several examples of blockchain applications in remittance systems. Speculation has resurfaced around a possible integration of Ripple’s XRP with SWIFT, the global messaging network for cross-border transactions. Previous reports have indicated that banks have tested XRP’s compatibility with SWIFT. If confirmed, such a partnership could significantly boost XRP adoption among global financial institutions. The report also mentions SBI Remit, a Japanese money transfer service that uses XRP as a bridge currency. It places SBI alongside earlier examples such as MoneyGram and Stellar, suggesting a broader trend of using cryptocurrencies to cut costs and speed up cross-border transactions. Mastercard’s reference to Ripple and XRP adds credibility to the token’s role in remittances. It signals that mainstream payment firms are now taking a closer look at blockchain infrastructure. The inclusion gives Ripple added visibility in the financial ecosystem. SBI Remit’s ongoing use of XRP in Asia further illustrates how digital assets are being integrated into real-world payment systems. The Mastercard report underscores that blockchain solutions are being evaluated across regions and technologies.
Specialized blockchains are shaping the future of DeFi attracting robust ecosystems and offering developers more freedom to innovate in areas like algorithmic credit scoring, IP rights management, and tokenized commodities
Specialized blockchains like Berachain, Story (IPfi), Unichain, Monad, and MegaETH are leading a wave of specialized blockchain launches designed to serve diverse decentralized finance applications. These chains challenge the notion that a handful of general-purpose networks can support all use cases and declare that the future is not one monolithic chain to rule them all. Financial institutions are entering DeFi with expectations shaped by decades of traditional finance, and the demand is clear: performance-optimized platforms catering to high-speed trading, tokenized intellectual property, and sophisticated real-world asset markets. Critics warn that a highly fragmented landscape could dilute liquidity and create inefficiencies, making it harder for assets to flow seamlessly across different platforms. However, emerging data from beta deployments indicates that specialized networks can attract robust ecosystems, offering developers more freedom to innovate in areas like algorithmic credit scoring, IP rights management, and tokenized commodities. Experiments in liquid staking, real-world asset tokenization, and hybrid on-chain/off-chain data verification further validate the need for these chains as key infrastructure layers for the next wave of institutional DeFi. The long-term viability of this multi-chain paradigm will depend on whether interoperability frameworks can facilitate frictionless asset movement and whether institutions gain confidence in the governance and security of specialized chains. The future of blockchains is not monolithic; it’s modular, specialized, and taking off. As the market evolves, it’s crucial to develop seamless user interfaces and robust interoperability mechanisms that abstract away technical friction.
Iterate.ai offers an on-premises AI appliance that delivers complete control, privacy, and enterprise-grade AI performance without relying on the cloud
Iterate.ai and ASA Computers have launched AIcurate, a turnkey, on-premises AI appliance that delivers complete control, privacy, and enterprise-grade AI performance without relying on the cloud. Built on Iterate.ai’s Generate platform and deployed on Dell PowerEdge servers, AIcurate empowers enterprises to run LLMs and AI workloads securely and within their own infrastructure. The system supports integration with popular business tools, is vendor-agnostic, and is optimized for performance-intensive applications such as document analysis, internal search, and workflow automation. Unlike public AI platforms, AIcurate enables secure deployment of powerful LLMs such as OpenAI, PaLM 2, Meta’s Llama, Mistral, and Microsoft’s models, all without sending data to the cloud. Businesses can build custom AI workflows while ensuring compliance with internal policies and industry regulations. “This collaboration makes advanced AI more accessible for organizations that can’t compromise on data control.” Ruban Kanapathippillai, SVP of Systems and Solutions at ASA Computers said “AIcurate puts enterprise-grade AI directly into customers’ data centers, giving them full control while supporting the flexible and secure architecture that modern IT teams demand.” Capabilities included in AIcurate: Secure on-prem deployment, Enterprise tool integration, Support for leading LLMs, Vendor-agnostic architecture, Advanced document processing, Role-based access control:, Workflow automation with agentic AI.
US Treasury report on stablecoins mulls upside of offering interest – based on estimates that stablecoins will grow to $2 trillion by 2028
The US Treasury’s Borrowing Advisory Committee (TBAC) explored the impact of stablecoins on the demand for short term Treasuries. One topic was mentioned repeatedly – the potential for stablecoins to offer interest. The last iteration of the Senate’s stablecoin bill, the GENIUS Act, introduced a clause that banned the payment of stablecoin interest before receiving a positive vote by the Senate Banking Committee. The TBAC report used a figure from Standard Chartered research that estimates that stablecoins will grow to $2 trillion by 2028 assuming stablecoins don’t pay interest. As an aside, Citi also recently published forecasts. The mid April capitalization of stablecoins was $234 billion, which accounts for approximately $120 billion investment in short-dated Treasuries. Combining that with Standard Chartered’s figure, the report estimates that stablecoin investment in Treasuries will expand to $1 trillion by 2028. If stablecoins were to offer interest, the figure could be quite a bit higher, although no forecast was provided. That would account for a significant slice of the short term Treasury Bill market, which currently has a $6.4 trillion issuance. A key reason why most global stablecoin regulation has not supported the payment of interest is because there is a concern that bank deposits might shift to stablecoins, potentially affecting the economy with less credit available from banks, or credit might become more expensive. The TBAC report states that transactional demand deposits at banks that total $6.6 trillion are most “at risk” from stablecoins. Apart from delving into the potential for stablecoins to offer interest, two other issues were floated – the potential to allow stablecoin issuers access to the Federal Reserve and / or deposit insurance. This would help reduce the impact of de-peg events.
Shopify’s first-quarter revenue surges 27% aided by the rapid checkout feature, Shop Pay’s 57% YoY growth processing $22 billion in GMV and by 64% penetration for Shopify Payments
Shopify’s first-quarter earnings results showed double-digit growth in gross merchandise volumes (GMV) and a continued movement toward streamlined checkout online and further inroads made in offline commerce. revenues were up 27% to $2.4 billion; GMV surged 22% to $74.8 billion. Offline GMV increased 23% and B2B GMV delivered triple-digit growth, up 109% from the year-ago first quarter, said President Harley Finkelstein. With a nod to the current fluid state of tariffs, Finkelstein pointed out that the company’s managed markets products give U.S. merchants “more options with our merchant of record service for collecting and remitting duties and taxes while managing other markets independently. This means if new duties are announced, most merchants can achieve compliance within hours. Later this month, we’ll introduce duty inclusive pricing, allowing merchants to set international prices that include duties in the product price. This ensures transparent pricing from the start and helps customers avoid surprise fees at checkout,” Finkelstein said. “Shopify Payments continues to be our largest product offering and a key driver. We made great progress in Q1, with payments GMV penetration hitting 64%,” Finkelstein said, as Shopify Payments expanded into new markets in Europe. Shop Pay, the rapid checkout feature, saw 57% growth year on year, processing $22 billion in GMV in the quarter. The Shop App continued its momentum in Q1, hitting over 94% year-over year growth in what Finkelstein termed “native GMV, an impressive acceleration and 84% growth last quarter.” Additionally, merchants are using Sidekick, the platform’s AI powered assistant, in increasing numbers, he said. The company expects current quarter revenues to grow in the mid 20% range year over year. As lower margin payment products are part of the mix, he said, gross dollar profit growth will come in at a rate lower than revenues; non-cash charges will also factor into margin impact.
Citi report predicts stablecoin market size could grow to $3.7 trillion by 2030 from the current level of $240 billion; payment companies to represent 50% of the stablecoin volume within 12 months
The next five years will likely see stablecoins substitute for some overseas and domestic U.S. currency holdings, according to a Citi Future Finance report. “We’re looking at the integration of stablecoins into what you call the mainstream economy,” Ronit Ghose, the global head of Future of Finance, Citi Institute, said. The stablecoin market size is currently around $240 billion, led by Tether’s $145 billion USDT and Circle’s $60 billion USDC. In Citi’s base-case prediction, stablecoins will grow to $1.6 trillion by 2030, provided regulatory support and institutional integration take hold. In the bank’s more bullish scenario, the market could balloon to $3.7 trillion. (The global cryptocurrency market cap today stands around $3.45 trillion.) “Payment companies are leveraging stablecoins for a variety of pure-play payment flows, including cross-border transfer, remittance, merchant settlements and others,” CEO Michael Shaulov said. “Payment companies represent 11% of all of our clients, but 16% of the overall stablecoin transactions with over 30% growth of Q/Q in volumes. It is likely that this growth will continue, and they will represent 50% of the stablecoin volume within 12 months.”
Ally Bank’s AI platform can pick the right external LLM depending on the use case or combine answers from several LLMs; it removes PII, tracks all transactions and rehydrates PII for context
In an era where differentiation in banking is increasingly difficult, Ally Bank has emerged as a leader in creating exceptional digital banking experiences. Sathish Muthukrishnan, chief information and technology officer at Ally Financial said, “The intent behind launching our technology strategy was to ensure that technology will continue to be relevant in an all-digital bank, but more importantly, to create differentiation and drive significant business outcomes. We categorized our strategy into six different pillars. The first is security. Our second pillar was driving tremendous experiences. The third pillar is how I know my experience is working. That’s when data analytics came in. Measure what consumers do, but more importantly, measure what they don’t do. Our operational pillar involved migrating to cloud, driving automation and consistency in how we develop and deploy code. And then we needed to preserve our culture and take care of our talent. These pillars laid the foundation for our transformation. We now have about 75% of our applications running on the cloud and about 95% of the enterprise data in the cloud. This allows us to learn from consumer behaviors, understand what they’re expecting and create experiences in real time so consumers think they are our only customer. We had our cloud strategy and data in the cloud warehouse. At the beginning of 2022, we redefined our network. As we were thinking about AI, we launched our chat assistant, Ally Assist. We created Ally AI because we knew technology was fast-evolving, but there were concerns about sending data to external LLMs. To address this, we built an AI platform that could connect to external LLMs but with added security — it removes PII, tracks all transactions and rehydrates PII for context. Our platform can connect to multiple LLMs — from GPT to FLAN to Bedrock. We can pick the right LLM depending on the use case or combine answers from several LLMs. Our content creation LLM is different from what we use for code generation or risk assessment. We have different models for different use cases. My advantage is that the product team, UI/UX team and technology team are all part of the same technology organization. We rolled out savings buckets — your deposit account with multiple savings buckets that you can name yourself. If you start questioning why roadblocks exist and how to solve them, your brand becomes more relevant to consumers. You become their next best experience, deepening relationships.”
Android’s new security feature shows a warning with a button to close if a partner bank’s app is opened while sharing a screen with an unknown number
Google announced new security and privacy features for Android including new protections for calls, screen sharing, messages, device access, and system-level permissions. With these features, Google aims to protect users from falling for a scam, keep their details secure in case a device is stolen or taken over by an attacker, and enhance device-level security for various attacks. Phone scammers often ask users to take actions like tapping on unsafe links or downloading unknown apps. In order to protect users, Google is blocking some actions and warning users of a potential scam while they are on a call with someone not in their contact list. For Android 16, these actions include side-loading an app for the first time from a web browser, messaging app, or other sources that have not been verified by Google, and granting accessibility permission to an app so that a scammer can take control of the device. The company is also preventing users running Android 6 or later from disabling Google Play Protect, which scans the device for harmful apps while they are on a call. Google is adding screen-sharing protection as well by reminding users to stop sharing the screen after a call ends. The company is also testing a new warning screen with select banks in the U.K. to prevent fraud through screen-sharing. When users on devices running Android 11 or later open a partner bank’s app while sharing a screen with an unknown number, the device will show a warning screen with a button to quickly end the screen-sharing. The company is adding new features to its Google Play Protect live detection program as well, which detect unsafe apps that have hidden or changed icons. The company said it is now applying a new set of on-device rules to catch more categories of malicious apps. The company said it is now applying a new set of on-device rules to catch more categories of malicious apps.