JPMorgan Chase reportedly reorganized its private bank and named a global head of the bank, which is a new role. David Frame, who previously served as the U.S. head of the private bank, has been appointed to the new role. The reorganization of the private bank and the creation of the new role are meant to help the bank assist its wealthy customers in storing their money around the world. Previously, the private bank focused on clients in individual countries. The private bank requires a minimum balance of $10 million and has found that these wealthy individuals want to diversify their investments rather than keeping their assets in a single country or region. While there has long been demand for this sort of diversification, it has been accelerated by concerns about issues like global conflicts and trade tensions. In another effort focused on wealthy customers, JPMorgan Chase said in May that it is accelerating the rollout of the affluent banking offering it introduced late last year. After opening two J.P. Morgan Financial Centers in late 2024, the bank said it would add 14 more in May and would have 31 in operation by the end of 2026. This branch format is designed to cater to the needs of affluent clients by providing private meeting spaces, distinctive finishes, personalized support from a dedicated senior private client banker and a full range of banking and wealth management offerings.
KeyBank deepens its collaboration with Qolo- platform for card issuing, ledger management, and payment processing- allowing to add same-day ACH reconciliation, advanced dashboard reporting, and simplified statement reporting, which have improved the platform’s functionality and customer experience
The KeyBank-Qolo alliance reflects an evolution in how banks and fintechs can co-create value through deeper integration. Qolo is an all-in-one platform for card issuing, ledger management, and payment processing, designed to simplify and streamline your operations. The partnership has allowed KeyBank to add features like same-day ACH reconciliation, advanced dashboard reporting, and simplified statement reporting, which have improved the platform’s functionality and customer experience. The collaboration between the two firms goes beyond a typical vendor-client relationship, with both companies working closely on product development and infrastructure investments. The decision-making process for such partnerships at KeyBank involves considering factors like time to market, alignment with core competencies, regulatory complexity, total cost of ownership, and strategic control. The success of the KeyBank-Qolo partnership is attributed to factors like foundational business model alignment, cultural alignment, and deep technical integration. What sets this partnership apart is the shared ability to co-build and influence the evolution of embedded banking infrastructure together, according to Bennie Pennington, Head of Embedded Banking at KeyBank. Pennington noted that KeyBank’s operational depth — spanning the nuance of regulation, risk management, and customer workflow patterns — complements Qolo’s speed and technical flexibility, creating a joint capability greater than the sum of its parts. KeyBank’s core banking systems must connect smoothly with Qolo’s infrastructure, which handles transaction data but also sends near real-time updates on risk, compliance, and operational metrics, backed by redundant data flows. To achieve this level of coordination, KeyBank has configured its integration so that Qolo can receive payment settlement requests directly from its ACH settlement platform. Embedded banking infrastructure falls into what Pennington refers to as “specialized table stakes”: essential for all players, but not a space where banks can typically differentiate. Qolo’s tech gives the bank a competitive edge by focusing on implementation and customer experience, instead of relying on exclusivity.
Brex uses a “superhuman product-market-fit test” to figure out what tools are worth investing in beyond the pilot program
Brex completely changed its approach to software procurement to ensure it wouldn’t get left behind. Brex CTO James Reggio told that the company initially tried to assess these software tools through its usual procurement strategy. The startup quickly discovered its months-long piloting process was just not going to work. The company started by coming up with a new framework for data processing agreements and legal validations for bringing on AI tools, Reggio said. This allowed Brex to vet potential AI tools more quickly and get them into the hands of testers faster. Reggio said the company uses a “superhuman product-market-fit test” to figure out what tools are worth investing in beyond the pilot program. This approach gives employees a much larger role in deciding what tools the company should adopt based on where they are finding value, he added. “We’re basically, I would say, about two years into this new era where there’s 1,000 AI tools within our company. And we’ve definitely canceled and not renewed maybe five to 10 different larger deployments.” “By delegating that spending authority to the individuals who are going to be leveraging this, they make the optimal decisions for optimizing their workflows,” Reggio said. This approach has helped the company figure out where it needs broader licensing deals for software too based on a more accurate headcount of how many engineers are using what.
Wells Fargo financial scandal in 2016 diminished consumer trust in traditional banks while driving homebuyers to fintech lenders for mortgages, study suggests
The Wells Fargo financial scandal in 2016 diminished consumer trust in traditional banks while driving homebuyers to fintech lenders for mortgages, a University of California, Davis study suggests. The paper was written by Keer Yang, an assistant professor in the UC Davis Graduate School of Management specializing in finance and financial technology. The difference in interest rates and other fees for comparable 30-year, fixed-rate loans on single-family homes between banks and fintech lenders did not change after the 2016 scandal. “Therefore, it is trust, not the interest rate, that affects the borrower’s probability of choosing a fintech lender,” Yang wrote. Yang analyzed Gallup poll answers regarding trust in banks, internet searches on Google Trends on bank scandals, newspaper articles about the Wells Fargo fines and scandal, and deposits and mortgage loan data sets in traditional banks and in fintech lenders. The period examined was 2012 to 2021. Use of financial services increased from 2% of the market in 2010, before any knowledge of the Wells Fargo scandal, to 8% in 2016, according to the research paper. Furthermore, in areas with Wells Fargo banks—where exposure to the scandal was more pronounced—customers were on average 4% more likely to use non-bank lenders than any banks after 2016. The scandal had a minimal effect on bank deposits, probably because of the protections afforded by deposit insurance, Yang said.
McKinsey’s I2I—Idea to Impact— framework helps integrate gen AI throughout the product development life cycle while giving clients greater control
Sallah Kokaina of McKinsey helps businesses transform through advanced technology. This involves software engineering and architecture, IT strategy, and digital delivery. In practice, I build platforms and lighthouses for the delivery life cycles of industrialized software. One of the biggest challenges when clients adopt new technology like gen AI is figuring out where to begin. It’s not as simple as waving a magic wand. But companies often rush into proof-of-concept projects to demystify the tech. This can lead to multiple failed attempts when transitioning to production. Fast is fine, but first you need a strategic approach. That’s why we tailor tools and platforms to maximize adoption and impact. We also help clients identify and establish the right operating model, manage change, and create a clear roadmap to their goals. Simply introducing a new tool isn’t going to guarantee results or successful scaling; transforming the whole organization is required. To that end, we’ve been working on distinctive assets and approaches to give clients a jump start in their AI journey. One of these is I2I—Idea to Impact—a framework that helps integrate gen AI throughout the product development life cycle while giving clients greater control. It supports key business needs like standardization, cost control, and data management. Clients who have used it have seen boosts in productivity and efficiency, giving them more time for collaboration and more impactful tasks.
Marco Argenti, Goldman’s chief information officer, argues that companies should empower young professionals with AI skills to help shape strategy
Goldman Sachs is paving the way for the next generation of finance leaders to shape the future of AI in the workplace. The bank hires about 2,500 to 3,000 interns each summer. For the 2025 internship class, Goldman received more than 360,000 applications—a 15% increase from last year. With an acceptance rate of just 0.7% this year, the program is highly competitive and serves as a pipeline for permanent positions. Marco Argenti, Goldman’s chief information officer, argues that companies should empower young professionals with AI skills to help shape strategy. While some predict agentic AI—autonomous systems that can perform tasks and make independent decisions—will displace junior roles, Argenti says the reality is more nuanced. Early-career workers are more essential than ever because they are “AI natives,” having grown up with generative AI and being uniquely equipped to adapt to and shape its future. Goldman recently launched its GS AI Assistant, an internal AI program that enables employees to interact with large language models securely firewalled within the company, reducing the risk of sensitive data leaks. The AI will be used for efficiency gains, the company said. Research shows that AI adoption among desk workers is accelerating. According to Salesforce’s latest Slack Workforce Index, a survey of 5,000 global desk workers found that daily AI users are 64% more productive and 81% more satisfied with their jobs than non-users. More than 95% of workers have used AI to perform tasks they previously lacked the skills to do themselves, and workers are now 154% more likely to use AI agents to enhance their performance and creativity rather than simply automate tasks, according to the findings. Notably, millennials are emerging as the leading AI power users at work: 30% say they thoroughly understand AI agents, surpassing even Gen Z (22%). As AI continues to redefine the workplace, companies like Goldman Sachs highlight the potential benefits of empowering AI natives. “How to Get ROI from AI in the Finance Function” is a report by Boston Consulting Group (BCG). The research finds that teams achieving strong ROI prioritize value from the outset, rather than pursuing learning for its own sake. Instead of focusing on isolated use cases, they adopt a broad, transformational approach. What sets outperforming organizations apart are the implementation tactics they use and the use cases they prioritize, according to the survey findings. Out of more than 30 implementation tactics that BCG tested, 10 stood out as the most successful—including integrating AI and generative AI into the overall finance transformation, systematic tracking, developing a clear data strategy, and focusing on quick wins.
Marco Argenti, Goldman’s chief information officer, argues that companies should empower young professionals with AI skills to help shape strategy
Goldman Sachs is paving the way for the next generation of finance leaders to shape the future of AI in the workplace. The bank hires about 2,500 to 3,000 interns each summer. For the 2025 internship class, Goldman received more than 360,000 applications—a 15% increase from last year. With an acceptance rate of just 0.7% this year, the program is highly competitive and serves as a pipeline for permanent positions. Marco Argenti, Goldman’s chief information officer, argues that companies should empower young professionals with AI skills to help shape strategy. While some predict agentic AI—autonomous systems that can perform tasks and make independent decisions—will displace junior roles, Argenti says the reality is more nuanced. Early-career workers are more essential than ever because they are “AI natives,” having grown up with generative AI and being uniquely equipped to adapt to and shape its future. Goldman recently launched its GS AI Assistant, an internal AI program that enables employees to interact with large language models securely firewalled within the company, reducing the risk of sensitive data leaks. The AI will be used for efficiency gains, the company said. Research shows that AI adoption among desk workers is accelerating. According to Salesforce’s latest Slack Workforce Index, a survey of 5,000 global desk workers found that daily AI users are 64% more productive and 81% more satisfied with their jobs than non-users. More than 95% of workers have used AI to perform tasks they previously lacked the skills to do themselves, and workers are now 154% more likely to use AI agents to enhance their performance and creativity rather than simply automate tasks, according to the findings. Notably, millennials are emerging as the leading AI power users at work: 30% say they thoroughly understand AI agents, surpassing even Gen Z (22%). As AI continues to redefine the workplace, companies like Goldman Sachs highlight the potential benefits of empowering AI natives. “How to Get ROI from AI in the Finance Function” is a report by Boston Consulting Group (BCG). The research finds that teams achieving strong ROI prioritize value from the outset, rather than pursuing learning for its own sake. Instead of focusing on isolated use cases, they adopt a broad, transformational approach. What sets outperforming organizations apart are the implementation tactics they use and the use cases they prioritize, according to the survey findings. Out of more than 30 implementation tactics that BCG tested, 10 stood out as the most successful—including integrating AI and generative AI into the overall finance transformation, systematic tracking, developing a clear data strategy, and focusing on quick wins.
Precisely’s code-light conversational interface uses MCP to connect APIs with LLMs through natural language prompts and enables instant access to location intelligence tools and rich datasets without requiring any code
Precisely has developed a lightweight setup using the Model Context Protocol (MCP) to connect APIs with large language model (LLM) interfaces like Claude Desktop. This approach eliminates the need for writing boilerplate code and allows for intuitive exploration of services through conversational interfaces. MCP offers a standardized method for AI applications to connect with APIs, data, and tools, enabling LLMs to dynamically decide which functions to invoke in response to user prompts. This aligns with Precisely’s goal of making it easier to integrate high-integrity data with applications and workflows. An MCP server was built to wrap all available endpoints from Precisely APIs, resulting in a code-light environment where Claude Desktop can execute API calls automatically based on a user’s request. The MCP server supports natural language prompts and enables instant access to location intelligence tools and rich datasets without requiring any code. It also helps scale the impact of data programs across the organization without adding to developer workload.
Ramp’s corporate cards for expense management integrate with leading accounting platforms, feature advanced fraud detection tools and enable setting customizable spending controls by employee or department and instant issuance of virtual and physical cards
Ramp has announced the official launch of its upgraded corporate cards for expense management. This new solution is designed to transform how businesses manage expenses, offering real-time visibility, tighter financial controls, and simplified reconciliation processes. With the launch of its corporate cards for expense management, Ramp is addressing the growing need for businesses to eliminate outdated manual expense workflows. The enhanced offering integrates with leading accounting platforms and equips finance teams with tools to reduce fraud risk, enforce spending policies, and enable accurate financial forecasting. Key features of the new corporate cards for expense management include customizable spending controls by employee or department, instant issuance of virtual and physical cards, and advanced fraud detection tools. Businesses can assign dedicated virtual cards for vendor subscriptions, improving vendor management and reducing the risk of unauthorized charges. As global teams and remote workforces expand, Ramp’s expense management solution ensures seamless financial oversight across geographies and departments. Audit trails are automatically maintained, providing businesses with ready access to clean, transparent transaction histories for compliance.
OpenAI enables retailers to build AI shopping agents that can search for products, add items to a cart, and generate checkout links without requiring authentication by connecting the store’s URL directly to the OpenAI Responses API
OpenAI unveiled a way for retailers to create artificial intelligence (AI) shopping assistants in Shopify. With a few clicks, developers can now connect the Storefront Managed Compute Platform (MCP) server directly to the OpenAI Responses API to build agents that can search for products, add items to a cart, and generate checkout links — all without requiring authentication. Go to the OpenAI Playground, under Tools, add MCP Server. Click on Shopify and add your store’s URL to create the AI shopping assistant. Now when users type, “I am looking for a lightweight men’s button up shirt for a vacation,” for example, the assistant will search for options from your inventory to show the shopper. If the shopper picks one of the options, the assistant can automatically move it to checkout. The move reflects OpenAI’s broader strategy to move deeper into offering shopping capabilities. Rival Perplexity already offers a shopping assistant within its AI chatbot. Shopify is one of the merchants whose products can be found through Perplexity. Google introduced Gemini Robotics On-Device, an AI model that runs directly on robots without the need for an internet connection. Amazon unveiled DeepFleet, an AI model that acts as a traffic controller for warehouse robots, improving travel efficiency by 10%.