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Clearent by Xplor has launched Xplor Capital, an embedded financing solution for software providers and their merchants. This is made possible through a strategic partnership with Parafin, a capital as a service platform specializing in capital solutions for small and medium-sized businesses. The extension allows Software-as-a-Service (SaaS) providers to integrate merchant financing capabilities directly into their software ecosystems. With 67% of US small-business owners planning to pursue funding within the next 12 months, 77% are concerned about accessing capital. Xplor Capital allows software providers to offer pre-approved funding options based on sales data, fueling growth and increasing software platform engagement. Key Benefits of Xplor Capital: Frictionless Financing: Merchants receive pre-approved funding offers based on their sales volume, eliminating lengthy applications and credit checks. Embedded Growth Solution: Software providers can integrate Xplor Capital with a simple API or branded loan widget, increasing the value of their software. Increased Revenue: Businesses using financing can see a revenue boost. Predictable Transparent Repayments: Merchants repay their funding through a one-time fixed fee, and automatic deductions from a fixed percentage of sales, with no late fees or hidden interest.

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Salt Security announced the launch of the Salt Model Context Protocol (MCP) Server, giving enterprise teams a novel access point of interaction with their API infrastructure, leveraging natural language and artificial intelligence (AI). Built on the open MCP standard, Salt’s MCP Server enables AI agents to discover, understand, and analyze API behavior with contextual awareness and enterprise-grade precision. Salt’s MCP server creates a personal ChatGPT experience for our customers with new capabilities: Contextual API Search: Contextual search across their entire API inventory. API Explainer: Explain the use and functionality of each API in your environment so security teams can be more independent. Posture Gap Contextual Search and Analysis: Allow security teams to identify API posture gaps and misconfigurations in a free-form search. Remediation Guidance: Offers AI-driven, actionable recommendations to mitigate high-risk vulnerabilities.

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Stripe this week deployed more than 60 products covering a range of artificial intelligence and distributed ledger uses as it competed with legacy card networks and fintechs such as Block and PayPal. These deployments come as the entire payments industry assesses the impact of President Donald Trump’s tariffs, which are pressuring firms to support rapid changes in cross-border strategy.  Geographic flexibility is important for a payment tech firm operating in cross-border payments and affects both financial institutions and the global corporations they serve, Sean Viergutz, PwC principal and financial services transformation leader, told. “Being present or easily able to operate in multiple regions allows firms to hedge geopolitical and regulatory risks by not being overly reliant on a specific country or region,” Viergutz said.  “Additionally, it allows them to maintain operations in alternative markets when one becomes unstable.” Taking advantage of shifts in supply-chain strategies amid the trade war is a revenue opportunity for cross-border payment companies, according to FXC Intelligence, which reports these adjustments and currency hedges can help offset tariff-related cross-border payment risks, such as a decline in credit for exposed businesses and price volatility. “This is a big opportunity for international commerce,” Tony DeSanctis, a senior director at Cornerstone Advisors, told. “Because the time, currency risk, and cost of international transactions can be mitigated using dollar-denominated stablecoins, this represents a sizable opportunity for international transactions.” Stripe, which provides payment portals for businesses, among other products, has a roster of clients that includes software companies, AI developers , financial institutions and more traditional businesses. The tariffs don’t affect digital businesses because they don’t ship tangible items, Paul Harapin, head of Asia Pacific and Japan at Stripe said, “but that could change.” Stripe has seen an increase in clients that are seeking to pivot their cross-border businesses to deemphasize the U.S. in response to the trade war. “We’ve been told ‘We’re tripling down in the U.K.,'” Harapin said. “They’re already there but are now moving faster.” To deepen its presence in Asia, Stripe recently partnered with Luckin Coffee, a large China-based chain that is expanding elsewhere in the region. The payment company hopes the Luckin deal can raise Stripe’s profile, drawing attention to Stripe services that can address compliance and currency differences within the region.  “Asia Pacific is positioned very well for growth,” Harapin said. “The problem is the complex regulations and managing fraud. There are also different payment methods in different countries.”

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Governor Kathy Hochul signed a new legislation as part of the FY26 Enacted Budget that will protect consumers across New York and fight back against scams or exploitative practices. From simplifying the process of cancelling recurring online subscriptions to cracking down on overdraft fees that target low-income consumers, these new laws will help New Yorkers fight back against unfair corporate practices. The FY26 budget includes legislation requiring businesses to notify consumers of upcoming renewals and price changes as well as provide clear instructions on how to cancel subscriptions. Under this legislation, cancellation processes must be simple, transparent, and fair – ensuring that it is just as easy to cancel a subscription as it was to sign up. With e-commerce sales rising and returns accounting for billions of dollars annually, New Yorkers deserve stronger consumer protections. The FY26 Budget also includes legislation to require online retail sellers to post return and refund policies in a way that is easily accessible for consumers; and a legislation to establish a licensing and supervision framework for BNPL providers. This legislation will introduce safeguards, such as disclosure requirements, dispute resolution standards, limits on all charges and fees, and data privacy protections to ensure consumers are better protected when using these financial products. The FY26 Budget includes first-in-the-nation legislation that requires businesses to disclose clearly to consumers when a price was set by an algorithm using their personal data, subject to certain exceptions.

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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.”

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AI startup Sakana has unveiled a new type of AI model architecture called Continuous Thought Machines (CTM). Rather than relying on fixed, parallel layers that process inputs all at once — as Transformer models do —CTMs unfold computation over steps within each input/output unit, known as an artificial “neuron.” Each neuron in the model retains a short history of its previous activity and uses that memory to decide when to activate again. This added internal state allows CTMs to adjust the depth and duration of their reasoning dynamically, depending on the complexity of the task. As such, each neuron is far more informationally dense and complex than in a typical Transformer model. CTMs allow each artificial neuron to operate on its own internal timeline, making activation decisions based on a short-term memory of its previous states. These decisions unfold over internal steps known as “ticks,” enabling the model to adjust its reasoning duration dynamically. This time-based architecture allows CTMs to reason progressively, adjusting how long and how deeply they compute — taking a different number of ticks based on the complexity of the input. The number of ticks changes according to the information inputted, and may be more or less even if the input information is identical, because each neuron is deciding how many ticks to undergo before providing an output (or not providing one at all). This represents both a technical and philosophical departure from conventional deep learning, moving toward a more biologically grounded model. Sakana has framed CTMs as a step toward more brain-like intelligence—systems that adapt over time, process information flexibly, and engage in deeper internal computation when needed. Sakana’s goal is to “to eventually achieve levels of competency that rival or surpass human brains.” The CTM is built around two key mechanisms. First, each neuron in the model maintains a short “history” or working memory of when it activated and why, and uses this history to make a decision of when to fire next. Second, neural synchronization — how and when groups of a model’s artificial neurons “fire,” or process information together — is allowed to happen organically. Groups of neurons decide when to fire together based on internal alignment, not external instructions or reward shaping. These synchronization events are used to modulate attention and produce outputs — that is, attention is directed toward those areas where more neurons are firing. The model isn’t just processing data, it’s timing its thinking to match the complexity of the task. Together, these mechanisms let CTMs reduce computational load on simpler tasks while applying deeper, prolonged reasoning where needed.

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Microsoft researchers have compared API-based and GUI-based AI agents, finding that each approach has distinct strengths and can work well together. API agents interact with software through programmable interfaces, while GUI agents mimic human use of software, navigating menus and clicking buttons. API agents are generally more stable and less error-prone, while GUI agents require multiple actions to accomplish the same goal. However, GUI agents can control almost any software with a visible interface, whether or not it offers an API. Microsoft outlines three strategies for combining both types of agents into hybrid systems: using API wrappers to hide GUI actions behind a programmable interface, using orchestration tools to coordinate both API and GUI steps in a workflow, and using low-code and no-code platforms for non-technical users to build automations using drag-and-drop interfaces. Recent advances in multimodal AI and new tools simplifying API development could lead to more flexible forms of automation that blur the line between front-end and back-end integration. Choosing the right agent for the job is crucial for long-term automation success. API agents are best for performance-critical tasks and security-sensitive environments, while GUI agents are better suited for legacy systems that lack APIs and mobile apps. Organizations can start with GUI agents and gradually switch to APIs as they become available.

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