AI-native procurement platform Levelpath announced $55+ million in Series B funding led by Battery Ventures, bringing the total raised to $100 million. At their core, Levelpath’s AI Agents are designed to act autonomously and proactively on behalf of users, solving real procurement challenges such as sourcing event creation, supplier onboarding, and risk assessments to drive exponential productivity. These AI Agents do not simply support rigid and fragile task automation; they deliver smarter workflows, faster deployment, and predictable outcomes from day one. With preconfigured agents available out of the box, teams can immediately benefit from enhanced decision-making and operational efficiency without the need for complex IT support. Powered by Hyperbridge, Levelpath’s AI-native architecture, these AI Agents unify model grounding, context management, and orchestration while ensuring secure, compliant data handling. By identifying the relevant business context, routing queries to the most suitable large language models, and tailoring outputs to specific organizational needs, Levelpath empowers procurement teams to achieve greater efficiency and impact, with fewer resources. This foundation enables organizations to benefit from rapid AI innovation without the need for constant due diligence on the models or integrations themselves.
Pay-by-bank solution provider Trustly’s total payment value increases by 54% year over year in 2024 to $85 billion driven by new products and strategic partnerships in North America and Europe
Pay-by-bank solution provider Trustly reported that its total payment value increased by 54% year over year in 2024, driven by new products and partnerships in North America and Europe. The firm’s total payment value reached $85 billion, up from $55 billion in 2023. “Strategic partnerships have been critical for Trustly,” Trustly Group CEO Johan Tjärnberg said. “In Europe, our ongoing collaboration with [His Majesty’s Revenue and Customs (HMRC)] strengthens our public sector leadership. In North America, expanded engagements with financial institutions and gaming providers highlight the strength and adaptability of our risk engine.” Trustly processed 1.3 billion HMRC payments totaling 4.7 billion pounds (about $6.4 billion) in January. The company’s collaborations in North America include BNY’s Bankify, Newline by Fifth Third, Coinbase, IGT, Light & Wonder, and an expanded partnership with Cross River Bank that now includes the FedNow® Service in addition to the RTP® network. On the product front, Trustly launched its artificial intelligence-powered recurring payments solution in June 2024, saying that a single integration enables merchants to accept repeat transactions directly from customers’ bank accounts. “By eliminating friction in repeat transactions, we’ve enabled merchants to better serve their customers and capture more revenue,” Tjärnberg said. “Similarly, our proprietary data engine, Azura, is driving higher engagement and conversion — proof of our innovation’s tangible impact.”
Sezzle BNPL’s new budgeting and checkout tools simplify the repayment process for consumers through a pre-loadable digital wallet and auto-prompts shoppers to earn rewards and save with available coupons
Sezzle has introduced features it says are designed to help customers weather increased financial pressure. “With record-low consumer confidence — the Conference Board’s index recently plunged to its lowest level since May 2020 amid fears of tariffs and recession — the current climate makes budgeting tools more essential than ever,” Sezzle said. Among the new features are Sezzle Balance, designed to simplify the repayment process for consumers through a pre-loadable digital wallet. There are two additional products in beta: “Express Checkout,” described as a “streamlined flow that reduces friction for returning shoppers,” and Browser Extension, a tool that automatically prompts shoppers to earn Sezzle Spend and save with available coupons. “When shoppers see real savings, they come back — it’s that simple,” said Charlie Youakim, Sezzle Chairman and CEO. “That’s why we’re focused on delivering value at every touchpoint, whether it’s through smarter discovery, seamless checkout, or transparent pricing. The more we help consumers feel in control and save money, the more they trust and choose Sezzle as their go-to way to shop.”
Volcano Exchange’s financial RWA digital asset leverages blockchain tech to transform high-threshold private banking services into divisible and tradable digital assets lowering the entry barrier for retail investors
Volcano Exchange (VEX), a global digital trading platform for Real World Assets (RWA), today announced the official launch of its first financial RWA digital asset–HL (Morgan Stanley Private Wealth RWA Token). Backed by the future returns of Morgan Stanley Private Bank’s premium wealth management products, HL has a total issuance of $20 million, corresponding to 200 million HL tokens, with an initial subscription price of $0.1 per token. This issuance marks a deep integration of traditional finance and blockchain technology, offering global investors more transparent, efficient, and flexible digital asset trading and staking services. HL is VEX’s first RWA digital asset underpinned by the revenue rights of a traditional financial institution, with its value directly pegged to the future earnings of Morgan Stanley Private Bank products. Leveraging blockchain technology, VEX transforms high-threshold private banking services into divisible and tradable digital assets, lowering the entry barrier for retail investors while maintaining the stability and compliance of traditional finance. With a total supply of 200 million tokens, HL is available for subscription on the VEX platform at an initial price of $0.1 per token. After the subscription period concludes, HL will be officially listed for trading, becoming VEX’s first RWA digital financial trading pair. Holders can freely trade on secondary markets or participate in value-added services such as staking and lending through VEX’s digital finance sector, maximizing asset liquidity. The launch of HL represents a major milestone in the RWA space. By digitizing traditional financial assets through blockchain technology, we enhance their liquidity and composability. Moving forward, VEX will continue to onboard premium assets from top-tier institutions, building a global RWA financial infrastructure.
Morgan Stanley research shows Apple Intelligence platform has been downloaded and engaged with by 80% of eligible U.S. iPhone owners in the last six months and has an above average NPS of 53
Consumers’ perception of Apple’s AI platform is more favorable than that of investors, Morgan Stanley said in a research note. Morgan Stanley said it found that the Apple Intelligence platform has been downloaded and engaged with by 80% of eligible U.S. iPhone owners in the last six months, has an above average net promoter score of 53, and is characterized by iPhone users as “easy to use, innovative, and something that improves their user experience.” “While much of the public critique of Apple Intelligence is warranted, and investor sentiment and expectations on Apple’s AI platform couldn’t be lower, our survey of iPhone owners paints a more positive picture,” Morgan Stanley said in the note. Since September, the share of iPhone owners who believe it is extremely or very important to have Apple Intelligence support on their next iPhone rose 15 points to reach 42%. Among iPhone owners who are likely to upgrade their device in the next 12 months, the percentage saying that about the AI platform rose 20 points to reach 54%, according to the note. Morgan Stanley also found that consumers are willing to pay more for Apple Intelligence than they were in September. Those who have used the AI platform are now willing to pay an average of $9.11 per month for it, a figure that’s 11% higher than the $8.17 average seen in September, per the note. While we don’t expect Apple to put Apple Intelligence behind a paywall until the platform is more built out, the potential long-term monetization of an Apple Intelligence subscription could reach tens of billions of dollars annually when considering a 1.4B global iPhone installed base, 32% (and growing) of US iPhone owners have an Apple Intelligence support iPhone, and users are willing to pay up to $9.11/month for Apple Intelligence,” Morgan Stanley said in the note.
Upwind’s ML cloud platform collects multi-layer telemetry data of the networking stack for real-time detection of threats to APIs, enabling 7X reduction in the mean time to respond
Upwind has added a feature to its cloud application detection and response (CADR) platform, allowing real-time detection of threats to application programming interfaces (APIs). The platform uses machine learning algorithms to collect telemetry data from Layers 3, 4, and 7 of the networking stack, enabling the identification of deviations and anomalous behavior in API traffic. The goal is to reduce the time required to investigate API security incidents by up to 10 times and mean time to response times by up to seven times. In the age of generative artificial intelligence (AI), there is a growing focus on API security. Many organizations are discovering that sensitive data is being shared inadvertently with AI models. Historically, responsibility for securing APIs has been unclear, with many cybersecurity teams assuming that application development teams are securing them as they are developed. However, this can lead to thousands of APIs that cybercriminals can exploit to exfiltrate data or modify business logic. Over the next 12-18 months, organizations plan to increase software security spend on APIs, DevOps toolchains, incident response, open source software, software bill of materials, and software composition analysis tools. Advancements in AI and eBPF technologies could simplify the entire software development lifecycle by streamlining the collection and analysis of telemetry data.
Data governance platform Relyance AI allows organizations to precisely detect bias by examining not just the immediate dataset used to train a model, but by tracing the potential bias to its source
Relyance AI, a data governance platform provider that secured $32.1 million in Series B funding last October, is launching a new solution aimed at solving one of the most pressing challenges in enterprise AI adoption: understanding exactly how data moves through complex systems. The company’s new Data Journeys platform addresses a critical blind spot for organizations implementing AI — tracking not just where data resides, but how and why it’s being used across applications, cloud services, and third-party systems. Data Journeys provides comprehensive view, showing the complete data lifecycle from original collection through every transformation and use case. The system starts with code analysis rather than simply connecting to data repositories, giving it context about why data is being processed in specific ways. Data Journeys delivers value in four critical areas: First, compliance and risk management: The platform enables organizations to prove the integrity of their data practices when facing regulatory scrutiny. Second, precise bias detection: Rather than just examining the immediate dataset used to train a model, companies can trace potential bias to its source. Third, explainability and accountability: For high-stakes AI decisions like loan approvals or medical diagnoses, understanding the complete data provenance becomes essential. Finally, regulatory compliance: The platform provides a “mathematical proof point” that companies are using data appropriately, helping them navigate increasingly complex global regulations. Customers have seen 70-80% time savings in compliance documentation and evidence gathering.
Alacriti’s next-gen ACH solution provides a unified payments infrastructure to process wires and real-time payments through multiple rails while allowing configurable exception handling, posting and settlement
Alacriti has launched its enhanced version of Orbipay Payments Hub for ACH, bringing automation-first design and intelligent processing to the ACH payment lifecycle. By incorporating automation, intelligent routing, and real-time insights, Orbipay Payments Hub for ACH helps financial institutions reduce processing costs, improve transaction accuracy, and enhance customer experiences while maintaining compliance with Nacha operating rules and regulatory standards. This modern ACH processing solution provides seamless integration with the Federal Reserve’s clearing systems, supporting a full range of ACH transactions, including consumer payments, corporate disbursements, bill payments, and Same Day ACH. Designed with advanced automation, configurable exception handling, and embedded compliance tools, Orbipay Payments Hub for ACH helps financial institutions modernize operations and gain full visibility of their ACH performance while keeping their existing core banking systems or without changing their other existing systems. Beyond ACH, Orbipay Payments Hub provides a unified payments infrastructure to process wires and real-time payments through the RTP® network, the FedNow Service, and Visa Direct. By bringing these payment rails together under a single platform, financial institutions can optimize, report, and manage their operations today while preparing for future payment innovations. Key Features and Benefits of Orbipay Payments Hub for ACH: Automated exception handling, Seamless ecosystem integration, Configurable posting and settlement , Advanced fraud prevention and compliance, and Unified reporting and analytics.
Affirm expands beyond Experian to begin reporting all its pay-over-time loans to TransUnion but transactions will not be factored into traditional credit scores nor visible to lenders in the near-term
Affirm is expanding the credit reporting of its pay-over-time products to TransUnion. All Affirm pay-over-time loans issued from May 1, 2025 onward, including Pay in 4 and longer-term monthly installments, will be reported to TransUnion. Consumers will see details about all Affirm transactions on their TransUnion credit files, though these transactions will not be factored into traditional credit scores nor visible to lenders in the near-term. As more pay-over-time providers report account information to the credit bureaus, lenders who request TransUnion credit reports will also be able to view consumers’ pay-over-time history. In the future, as new credit scoring models are developed, this information may factor into consumers’ scores, with the aim of supporting more informed lending decisions and helping consumers build their credit histories. TransUnion research found nearly 40% of consumers who haven’t used buy now, pay later are likely or very likely to use them in the future. Notably, a higher 53% of non-users would be likely or very likely to use them if it had the potential to have a positive impact on credit scores.
New Sequential Monte Carlo algo makes AI-generated codes more accurate by using incremental static and dynamic analysis and instructing the LLM to adhere to the rules of each language
Researchers from MIT, McGill University, ETH Zurich, Johns Hopkins University, Yale and the Mila-Quebec Artificial Intelligence Institute have developed a new method for ensuring that AI-generated codes are more accurate and useful. In the paper, the researchers used Sequential Monte Carlo (SMC) to “tackle a number of challenging semantic parsing problems, guiding generation with incremental static and dynamic analysis.” Sequential Monte Carlo refers to a family of algorithms that help figure out solutions to filtering problems. This method spans various programming languages and instructs the LLM to adhere to the rules of each language. The group found that by adapting new sampling methods, AI models can be guided to follow programming language rules and even enhance the performance of small language models (SLMs), which are typically used for code generation, surpassing that of large language models. João Loula, co-lead writer of the paper, said that the method “could improve programming assistants, AI-powered data analysis and scientific discovery tools.” It can also cut compute costs and be more efficient than reranking methods. Key features of adapting SMC sampling to model generation include proposal distribution where the token-by-token sampling is guided by cheap constraints, important weights that correct for biases and resampling which reallocates compute effort towards partial generations.