Shopping agents are positioned to become invisible sales conversion engines. As OpenAI, Perplexity, and others race to capture this trillion-dollar opportunity, the future of how consumers search and buy hinges on how these platforms will make money, and how their algorithms will decide which products to show consumers (or buy on their behalf). The answers will determine whether these chatbots deliver on their promise of personalized commerce in their truest and most authentic sense — or become a more sophisticated version of today’s pay-to-play search and commerce platforms. Their strength as a conversational interface, capable of understanding complex requests, makes them well-suited to complete purchases without users ever visiting a physical or digital store or leaving the conversation. An emerging Agentic AI commerce ecosystem now stands at the ready to help advance their ambitions. The speed at which the GenAI chatbots have amassed an audience shows the potential for how these models could upend the retail and commerce status quo by changing where consumers start their searches and end by making a purchase without a lot of steps or friction in between. As AI agents increasingly handle the search and presentation of results (or completed sales), traditional retailers risk becoming invisible in the commerce ecosystem altogether. In this world, payment credentials might emerge as the real winner as embedded offers, financing, rewards and other data-driven incentives become an invisible part of the transaction. The venture capital pouring into these platforms signals expectations for massive adoption and ROI. GenAI represents a new form of commerce orchestration across marketplaces, social signals and retail inventory through a simple and single conversational interface. The unique nature of conversational AI suggests that different approaches might not just be possible but necessary to compete. LLM platforms can build on GenAI’s growing sense of user trust, its potential for creating a distinctive new shopping and buying utility and its ability to monetize these new forms of value. How these models present recommendations and act on behalf of users presents new challenges because of their current lack of clarity — and new opportunities because of what they could become for the entire commerce ecosystem. And that could reshape how retailers and the ecosystem adapt their products and platforms to drive sales. For retailers and brands, that now means competing for both customer and AI attention. Retailers will need to ensure their inventory, pricing and product information are optimized for AI crawling and decision-making algorithms. The winners in this new era may be those who recognize that when conversations drive commerce, trust itself becomes the product. And that monetizing trust comes wrapped around a different business model.
Sam Altman’s World iris ID project is evolving into a superapp with a digital currency, a bank account number and by partnering gaming specialist Razer and dating platform operator Match Group
AI visionary Sam Altman is leading a project to distinguish real people from software fakes on the internet using eye scans. The World identification project, which uses eye scans to distinguish people from machines, is entering the money transfer and financial services business. Users can send money to friends and family free of charge via the World app and will have an account number for interactions with the banking system. The project aims to make it increasingly difficult to distinguish people from software online. Users create a profile called “World ID” using an eye scan on World scanners called Orb. As an incentive, World is launching its own digital currency. The project is also targeting online dating markets, such as gaming specialist Razer and dating platform operator Match Group. With these new functions, World is moving closer to the vision of a super app that covers all possible areas of everyday life, similar to WeChat in Asia. World, a web3 project started by Altman and Alex Blania that was formerly known as Worldcoin, is based on the idea that it will eventually be impossible to distinguish humans from AI agents on the internet. To address this, World wants to create digital “proof of human” tools; these announcements are part of its effort to get millions of people to sign up. After scanning your eyeball with one of its silver metal Orbs — or now, one of its Orb Minis — World will give you a unique identifier on the blockchain to verify that you’re a human.
IBM’s small models- Tiny Time Mixers — tackle network automation challenges where traditional large language models fall short and have an understanding time-series data
IBM Corp. is leaning into compact, specialized models — such as its new Tiny Time Mixers — to tackle network automation challenges where traditional large language models fall short. The key lies in understanding time-series data, something most large language models simply weren’t built to handle, according to Andrew Coward, general manager of software networking at IBM. “There’s new models, and IBM’s built one called Tiny Time Mixer. Very small parameters, million parameters, and they understand time. We can take network data, and then we can apply it to weather information or TV schedules. Then we can make predictions about what’s likely to happen. What we are seeing is the democratization of AI,” he said. “It’s almost free to put data in and run it against AI models, but if you need to train it, that’s the expensive bit. The training piece is coming down massively in costs.” Using small models, IBM helps address telco infrastructure problems, such as bandwidth congestion and poor network coverage. This explains why AI model accuracy takes center stage, Coward pointed out.
Upstart 1Q 2025: personal loan originations grow 83% year over year; 92% of loans were automated through AI driven activities; proportion of loans made to super-prime borrowers increased
Lending platform Upstart Holdings’ first quarter results saw loan originations nearly double from a year ago, driven in part by automated processes, while management pointed to strong credit metrics among the lending platform’s borrowers. Platform originations were up 89%, the company said, to $2.1 billion. Within that activity, personal loans of $2 billion were up 83% during the first quarter, year over year and flat sequentially, and super-prime borrowers accounted for 32% of originations. 92% of loans were automated through AI driven activities, with no human intervention in the mix. CEO Dave Girouard said the firm had seen “improved borrower health,” and said that higher conversion rates on lending helped boost revenues by 67%. In a nod to the firm’s automation efforts, Girouard said that during the quarter the company introduced embedding algorithms to Upstarts’ core personal loan underwriting model — with the result tied to “clustering data that have meaningful relationships, allowing seemingly random data to become valuable to predicting credit performance.” The algorithms, he said, lead to better model generalization, improved accuracy, and more informed credit decisions. Separately, the car loan platform grew originations 42% sequentially; home lending originations have also been growing, he said. “Our HELOC [home equity line of credit] originations grew 52% quarter on quarter and more than 6x compared to a year ago,” he said. Short-term lending continues to bring in new customers, and accounted for 16% of new borrowers in the quarter. “We’re rapidly automating routine tasks like processing payment failures and check handling, so we can spend human time on more valuable tasks,” said Girouard, who added that “in Q1, we automated 90% of hardship applications, making the process more seamless for borrowers and more efficient for Upstart. Beyond the technology, the work we’ve done to prioritize direct collections efforts for borrowers at risk of default have continued to have a meaningful impact. “For example, in Q1, we realized a 50% increase in debt settlement acceptances by extending repayment terms for at-risk borrowers.” CFO Sanjay Datta said that average loan size of about $8,865 “nudged up” from $8,580 in the prior quarter “as the proportion of loans made to super-prime borrowers increased.” But those gains among super-prime borrowers are also tightening contribution margins, which came in at 55% in the most recent quarter, down from more than 60% recently, and a similar mid-50% margin is forecast for the current quarter.
HSBC is offering a new loan product to US companies struggling to cover the cost of tariffs
HSBC is offering a new loan product to US companies struggling to cover the cost of President Donald Trump’s tariffs that have roiled international supply chains. The London-headquartered bank said on Wednesday its TradePay platform was being extended to directly cover the cost of tariff payments, allowing importers to effectively borrow to meet the increased expenses involved in shipping products into the US. “By settling import duties directly and frictionlessly through HSBC TradePay, our US clients have more visibility and control over their working capital,” said Vivek Ramachandran, head of global trade solutions at HSBC. Under the new loans, customers’ import payments will be automatically paid through pre-agreed credit with brokers or a direct deduction using automated clearing house credits, meaning companies will be better able to manage their cash flow and settle duties more efficiently. HSBC is the world’s largest trade bank and the biggest international bank in China, giving it a crucial role in oiling the wheels of international trade, particularly between the two biggest economies. The US has imposed tariffs as high as 145% on Chinese goods, while China has hit back with retaliatory rates of 125%. Talks aimed at de-escalating the situation are due to take place this week. Speaking last week, HSBC Chairman Mark Tucker said world trade was facing a “period of deep and profound change.” “The over-arching impact of the changing approach to global trade relations has been to increase economic uncertainty with serious potential risks to global growth,” said Tucker.
Citi plans to tokenize private companies on SIX Digital Exchange; Citi Token Services was named “Innovation of the Year” by American Banker in the On-Chain Finance category
Citi announced plans to become a custodian and tokenizer for Switzerland’s SIX Digital Exchange (SDX). The bank aims to tokenize the equity of venture backed, late stage companies on the digital exchange, with plans to go live in the third quarter of 2025. The aim is to reach eligible private and institutional investors. SDX consists of both a regulated DLT-based central securities depositary (CSD) and a marketplace, with Citi becoming a CSD member alongside several other Swiss and international banks. Standard Chartered joined in March. “This initiative will distinguish itself in the industry by using SDX’s regulated blockchain based technology to enable the efficient distribution of shares in mature international private companies, which are expected to generate strong investor interest,” said David Newns, Head of SDX. tokenization on its own won’t create demand. Hence, Citi and SDX are collaborating with Swiss digital asset bank Sygnum and Singapore’s SBI Digital Markets to help to drive demand from their client investors. SIX has a separate joint venture with SBI. “Switzerland’s regulatory framework and SDX’s infrastructure allows Citi to bring a new solution to market using technology to solve for challenges in private markets for issuers and investors,” said Marni McManus, Citi Country Officer & Head of Banking for Switzerland, Monaco & Liechtenstein. “Private markets is a major and growing opportunity and our work with SDX promises to simplify and digitize what is essentially a manual and paper-driven industry today.” SDX currently operates mainly on a permissioned blockchain under conventional regulations. Hence, it does not deal directly with private investors and always goes via brokers. To date most of its activities have been in digital bonds and cryptocurrencies. This partnership with Citi represents a significant step toward modernizing private market transactions, potentially creating new opportunities for liquidity and investment in late-stage private companies while maintaining regulatory compliance
CFPB won’t enforce 2024 interpretive rule that classified BNPL loans as credit cards, which put them under the purview of the Truth in Lending Act’s Regulation Z
The CFPB said it would not prioritize enforcement actions stemming from a 2024 interpretive rule that classified Pay in 4 loans as credit cards, which put them under the purview of the Truth in Lending Act’s Regulation Z. “The Bureau will instead keep its enforcement and supervision resources focused on pressing threats to consumers, particularly servicemen and veterans,” the announcement said. “The Bureau takes this step in the interest of focusing resources on supporting hard-working American taxpayers, servicemen, veterans, and small businesses. The Bureau is further contemplating taking appropriate action to rescind [credit card classification] for Buy Now, Pay Later.” The CFPB also recently said it would not enforce its payday lending rule at the end of March, just days before it was set to go into effect. The pullback marks a continuation of efforts by a more fintech-friendly administration. “I think fintechs can breathe another sigh of relief knowing that they may not need to ‘fit a square peg in a round hole’ with the imposition of credit card protections on BNPL products for which they may be at least somewhat ill-fitted,” Eamonn Moran, a partner at Holland & Knight said. Some BNPL loans, specifically Pay in 4 loans, live in a nebulous area of regulatory oversight. Installment loans with four or less payments are exempt from the Truth in Lending Act, meaning BNPL lenders are not required to provide customers with disclosures about the terms and costs of credit like credit cards and other installment loans with four-or-more payments. The CFPB’s interpretive rule, which former Director Rohit Chopra put forth in 2024, was an attempt to close that loophole. The CFPB’s research in January found that most BNPL loans were made to subprime consumers with high credit card balances and multiple loans, a segment of consumers that are particularly exposed to predatory lending. “Our members are focused on ensuring that consumers have access to responsible financial services like BNPL and often implement industry best practices long before being directed by regulators. We look forward to continuing our work with the CFPB and regulators across the country to enable responsible financial innovation without sacrificing consumer protection,” said Phil Goldfeder, CEO of the American Fintech Council,
loanDepot’s Q1 revenue jumps 23% powered by a multichannel sales model, proprietary mello tech stack, and a wider product array
loanDepot reported that its first-quarter 2025 revenue increased by 23% annually to $274 million, while its adjusted revenue was up 21% to $278 million on higher mortgage sales volumes and stronger margins. Revenues also increased on a quarterly basis, growing from a baseline figure of $257 million and an adjusted figure of $267 million in Q4 2024. loanDepot‘s origination volume for Q1 2025 was $5.2 billion, an increase of $0.6 billion or 14% annually. Purchase loans accounted for 59% of originations during the first quarter, down from 72% in Q1 2024. The company touted that its preliminary organic refinance consumer-direct recapture rate increased to 65%, compared to 59% in Q1 2024. The first quarter also saw the return of loanDepot founder and executive chairman Anthony Hsieh to the day-to-day operations at the California-based lender. Current CEO Frank Martell is set to transition to a board advisory role on June 4, and Hsieh will assume the interim CEO role at that time. “These investments will allow loanDepot to take advantage of our marketplace differentiators in this and upcoming cycles, as well as to continue to deliver a best-in-class customer experience.” Martell characterized Q1 2025 as a “quarter of positive momentum” before turning the call over to Hsieh. “As we go forward, the team and I will focus on capitalizing upon the things that already make loanDepot great,” Hsieh said. “Our multichannel sales model, proprietary mello tech stack, wide product array, powerful brand muscle and our servicing business are foundational places in which loanDepot can win. “By leveraging this unique constellation of assets, plus adding to our arsenal with new and emerging technologies and platform refinements, I believe we are well positioned to regain profitable market share and scale our business.” loanDepot’s first quarter saw solid mortgage revenue growth, which more than overcame the loss of $20 million in revenue tied to 2024 bulk sales of mortgage servicing rights (MSRs). As a result, loanDepot’s net loss of $40.7 million was down 43% compared to its $71.5 million loss in Q1 2024. Chief financial officer David Hayes said “Our strategy for hedging the servicing portfolio is dynamic, and we adjust our hedged positions in reaction to changing and straight environments. Our total expenses for the first quarter of 2025 increased by $12 million, or 4%, from the prior year quarter.” “The primary drivers of the increase were for higher volume-related commission, direct origination and marketing expenses. Our non-volume-related expenses decreased $7 million [during] the same period, … reflecting our ongoing cost management discipline and lower cyber-related costs,” Hayes said. loanDepot’s expectations for Q2 2025 include an origination volume of $5 billion to $7.5 billion. It estimates a pull-through weighted rate-lock volume of $5.5 billion to $8.0 billion, along with a pull-through weighted gain-on-sale margin of 300 to 350 basis points. “Because we service loans in-house, we directly interact with our customers, strengthening our brand and awareness loyalty and providing important self-serve opportunities throughout our customer portal,” Hsieh said. “This improves our recapture rates, which deepens our customer relationships and drives profitability by saving marketing expenses, avoiding much of the customer acquisition costs.”
Merger will combine Global Payments’ strength in SME segment and vertical-specific solutions with Worldpay’s enterprise and eCommerce capabilities, creating a comprehensive commerce solutions platform
TSYS-parent Global Payments is betting big on its $600 million synergy target as it pushes forward with the $22.7 billion acquisition of Worldpay, a move that is expected to shake up the competitive dynamics in merchant services and payments technology. The acquisition will see Global Payments divest its Issuer Solutions business to FIS for $13.5 billion, sharpening its focus as a pure-play merchant solutions provider. The combined entity will serve more than 6 million customers in 175 countries, processing $3.7 trillion in annual payment volume and 94 billion transactions — a scale that positions the company among the world’s largest payment processors. Central to the strategic rationale is an ambitious plan to realize $600 million in annual run-rate cost synergies within three years of closing. According to Global Payments, roughly a third of these savings will come from consolidating technology infrastructure and eliminating duplicative vendor and software spend. Additional synergies are expected from streamlining operations, integrating product offerings, and leveraging the expanded global footprint. The company also projects at least $200 million in annual revenue synergies from cross-selling opportunities enabled by the complementary merchant, enterprise and eCommerce capabilities of the two firms. On the company’s Q1 earnings call, CEO Cameron Bready emphasized the transformative nature of the deal: “We have a tremendous opportunity to drive substantial revenue and cost synergies from the transaction as we amplify our collective go-to-market strengths and simplify our business to become a pure-play merchant solutions provider with significantly expanded capabilities, extensive scale and greater market access. The transaction will drive an enhanced financial profile for the combined enterprise and unlock long-term value for our shareholders.” The merger will combine Global Payments’ strength in small and mid-sized businesses and vertical-specific solutions with Worldpay’s enterprise and eCommerce capabilities, creating a comprehensive commerce solutions platform that spans the full merchant spectrum.
Gyan is an alternative AI architecture built on a neuro-symbolic architecture, not transformer based, to create hallucination-free models by design
Gyan is a fundamentally new AI architecture built for Enterprises with low or zero tolerance for hallucinations, IP risks, or energy-hungry models. Gyan gives businesses full control over their data, keeping it private and secure — making it the trusted partner for enterprises in situations where reliability and accuracy are mandatory. Unlike with LLM’s, with Gyan, businesses can use an AI model without worrying about it making things up. Built on a neuro-symbolic architecture, not transformer based, Gyan is a ground-up hallucination-free model by design. “If the cost of a mistake is high, you certainly don’t want your AI causing it,” says Joy Dasgupta, CEO, at Gyan. “We built Gyan for companies and processes with zero tolerance for hallucination and privacy risks, with compute and energy requirements orders of magnitude lower than that of current LLM’s.” Gyan’s State of the Art performance in two key life sciences benchmarks (PubMedQA and MMLU) is proof of efficacy of its language model. Every inference by Gyan is traceable with full reasoning to exact ideas and arguments in the result, making them readily verifiable. This is not the case for any of the others on the Leaderboard. Gyan provides precise and accurate analysis which users can depend on.