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JPMorgan Chase rolls out AI assistant powered by ChatGPT-maker OpenAI

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JPMorgan rolls out AI assistant powered by ChatGPT maker OpenAI

JPMorgan Chase has rolled out a generative artificial intelligence assistant to tens of thousands of its employees in recent weeks, the initial phase of a broader plan to inject the technology throughout the sprawling financial giant.

The program, called LLM Suite, is already available to more than 60,000 employees, helping them with tasks like writing emails and reports. The software is expected to eventually be as ubiquitous within the bank as the videoconferencing program Zoom, people with knowledge of the plans told CNBC.

Rather than developing its own AI models, JPMorgan designed LLM Suite to be a portal that allows users to tap external large language models — the complex programs underpinning generative AI tools — and launched it with ChatGPT maker OpenAI’s LLM, said the people.

“Ultimately, we’d like to be able to move pretty fluidly across models depending on the use cases,” Teresa Heitsenrether, JPMorgan’s chief data and analytics officer, said in an interview. “The plan is not to be beholden to any one model provider.”

The move by JPMorgan, the largest U.S. bank by assets, shows how quickly generative AI has swept through American corporations since the arrival of ChatGPT in late 2022. Rival bank Morgan Stanley has already released a pair of OpenAI-powered tools for its financial advisors. And consumer tech giant Apple said in June that it was integrating OpenAI models into the operating system of hundreds of millions of its consumer devices, vastly expanding its reach.

The technology — hailed by some as the “Cognitive Revolution” in which tasks formerly done by knowledge workers will be automated — could be as important as the advent of electricity, the printing press and the internet, JPMorgan CEO Jamie Dimon said in April.

It will likely “augment virtually every job” at the bank, Dimon said. JPMorgan had about 313,000 employees as of June.

ChatGPT ban

The bank is giving employees what is essentially OpenAI’s ChatGPT in a JPMorgan-approved wrapper more than a year after it restricted employees from using ChatGPT. That’s because JPMorgan didn’t want to expose its data to external providers, Heitsenrether said.

“Since our data is a key differentiator, we don’t want it being used to train the model,” she said. “We’ve implemented it in a way that we can leverage the model while still keeping our data protected.”

The bank has introduced LLM Suite broadly across the company, with groups using it in JPMorgan’s consumer division, investment bank, and asset and wealth management business, the people said. It can help employees with writing, summarizing lengthy documents, problem solving using Excel, and generating ideas.

But getting it on employees’ desktops is just the first step, according to Heitsenrether, who was promoted in 2023 to lead the bank’s adoption of the red-hot technology.

“You have to teach people how to do prompt engineering that is relevant for their domain to show them what it can actually do,” Heitsenrether said. “The more people get deep into it and unlock what it’s good at and what it’s not, the more we’re starting to see the ideas really flourishing.”

The bank’s engineers can also use LLM Suite to incorporate functions from external AI models directly into their programs, she said.

‘Exponentially bigger’

JPMorgan has been working on traditional AI and machine learning for more than a decade, but the arrival of ChatGPT forced it to pivot.

Traditional, or narrow, AI performs specific tasks involving pattern recognition, like making predictions based on historical data. Generative AI is more advanced, however, and trains models on vast data sets with the goal of pattern creation, which is how human-sounding text or realistic images are formed.

The number of uses for generative AI are “exponentially bigger” than previous technology because of how flexible LLMs are, Heitsenrether said.

The bank is testing many cases for both forms of AI and has already put a few into production.

JPMorgan is using generative AI to create marketing content for social media channels, map out itineraries for clients of the travel agency it acquired in 2022 and summarize meetings for financial advisors, she said.

The consumer bank uses AI to determine where to place new branches and ATMs by ingesting satellite images and in call centers to help service personnel quickly find answers, Heitsenrether said.

In the firm’s global-payments business, which moves more than $8 trillion around the world daily, AI helps prevent hundreds of millions of dollars in fraud, she said.

But the bank is being more cautious with generative AI that directly touches upon the individual customer because of the risk that a chatbot gives bad information, Heitsenrether said.

Ultimately, the generative AI field may develop into “five or six big foundational models” that dominate the market, she said.

The bank is testing LLMs from U.S. tech giants as well as open source models to onboard to its portal next, said the people, who declined to be identified speaking about the bank’s AI strategy.

Friend or foe?

Heitsenrether charted out three stages for the evolution of generative AI at JPMorgan.

The first is simply making the models available to workers; the second involves adding proprietary JPMorgan data to help boost employee productivity, which is the stage that has just begun at the company.

The third is a larger leap that would unlock far greater productivity gains, which is when generative AI is powerful enough to operate as autonomous agents that perform complex multistep tasks. That would make rank-and-file employees more like managers with AI assistants at their command.

The technology will likely empower some workers while displacing others, changing the composition of the industry in ways that are hard to predict.

Banking jobs are the most prone to automation of all industries, including technology, health care and retail, according to consulting firm Accenture. AI could boost the sector’s profits by $170 billion in just four years, Citigroup analysts said.  

People should consider generative AI “like an assistant that takes away the more mundane things that we would all like to not do, where it can just give you the answer without grinding through the spreadsheets,” Heitsenrether said.

“You can focus on the higher-value work,” she said.

— CNBC’s Leslie Picker contributed to this report.

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Cross-Border Settlement Innovation and Real-Time Payment Architecture

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The global banking system is undergoing a comprehensive modernization of cross-border payment infrastructure. Driven by real-time settlement networks, open banking APIs, and interoperable messaging standards, financial institutions and multinational corporations are eliminating multi-day delays and reducing transaction costs associated with legacy international wire transfers.

Transition to Real-Time Gross Settlement Networks
Historically, international business-to-business (B2B) payments relied on complex correspondent banking relationships involving intermediary fees and processing delays. In 2026, the widespread adoption of ISO 20022 messaging protocols alongside interconnected Real-Time Gross Settlement (RTGS) systems allows direct, end-to-end processing of cross-border transfers.

Commercial banks are providing corporate clients with continuous, 24/7 payment clearing capabilities. Real-time transaction confirmation and automated FX rate locking allow international businesses to settle cross-border trade obligations within minutes, significantly reducing counterparty risk.

Central Bank Digital Currency (CBDC) Interoperability
Wholesale Central Bank Digital Currency (CBDC) pilot initiatives are reaching operational maturity across several key financial centers. Collaborative multi-CBDC platforms enable participating central banks and commercial institutions to settle foreign exchange and international trade transactions directly on shared distributed ledgers.

These wholesale digital currency networks eliminate traditional clearinghouse delays and minimize foreign exchange slippage. Enterprise treasury departments benefit from enhanced liquidity management, as cross-border cash balances can be deployed and repatriated instantaneously.

Corporate Treasury Transformation
For enterprise treasurers, instant cross-border settlement transforms cash management strategies:
– Working Capital Optimization: Reduced transaction float allows companies to lower precautionary cash reserves and optimize short-term liquidity investments.
– Automated Reconciliation: Enriched data formats embedded in ISO 20022 payment messages streamline automated general ledger posting and invoice matching.
– Reduced Processing Overhead: Account-to-account (A2A) real-time clearing bypasses costly intermediary correspondent banking fees.

Strategic Financial Priorities
1. Upgrade Treasury Systems: Ensure internal core enterprise software supports real-time ISO 20022 payment messaging standards.
2. Leverage Instant Clearing Rails: Utilize direct payment networks to lower cross-border transaction fees and eliminate settlement delays.
3. Evaluate Multi-Currency Liquidity: Modernize liquidity management frameworks to capitalize on 24/7 real-time settlement capabilities.

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Private Credit Expansion and Regulatory Oversight in 2026 Capital Markets

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The global private credit market has solidified its role as a fundamental pillar of enterprise finance, expanding rapidly across middle-market lending, asset-backed finance, and infrastructure financing. As non-bank financial institutions capture a larger share of corporate debt origination, global regulatory bodies are increasing oversight to evaluate market transparency and systemic risk interconnections.

Growth Drivers in Direct Lending
Direct lending platforms have continued to attract substantial institutional allocations from pension funds, sovereign wealth entities, and insurance companies seeking attractive risk-adjusted yields. Private debt funds offer corporate borrowers customized financing structures, faster execution timelines, and confidentiality compared to syndicated loan markets.

In 2026, private credit managers are increasingly financing larger corporate transactions, providing multi-billion-dollar credit facilities for buyout deals and corporate restructurings. The flexibility of private debt contracts—featuring unitranche pricing and tailored covenant packages—has made direct lending the preferred capital source for middle-market enterprise sponsors.

Regulatory Scrutiny and Systemic Risk Assessment
The rapid growth of non-bank intermediation has drawn heightened scrutiny from financial regulators in North America and Europe. Because private debt agreements are negotiated privately without public exchange disclosures, central banks are evaluating potential vulnerabilities related to asset valuation consistency and fund liquidity profiles.

Regulatory agencies are introducing guidelines aimed at improving reporting standards for private investment vehicles managing institutional assets. Key focus areas include monitoring leverage ratios within private credit funds and evaluating indirect credit exposures between commercial banking institutions and private debt funds.

Navigating Elevated Refinancing Costs
With benchmark interest rates remaining elevated, private debt borrowers face higher debt service obligations on floating-rate credit facilities. Financial advisory firms report an increase in proactive liability management strategies, including payment-in-kind (PIK) interest options, equity infusions from sponsors, and covenant modifications.

Private credit managers with deep operational capabilities are actively working alongside portfolio companies to optimize working capital and maintain cash flow coverage ratios during periods of higher borrowing costs.

Key Financial Takeaways
1. Mainstream Asset Class: Private credit has expanded beyond niche alternative asset status into a core corporate finance solution.
2. Enhanced Transparency Standards: Regulatory frameworks are evolving toward greater disclosure requirements for private debt managers.
3. Proactive Risk Management: Lenders and sponsors must prioritize debt sustainability and active portfolio monitoring amid high benchmark rates.

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Finance

Venture Capital and Startup Valuations in 2026: Focus on Unit Economics and Sustainable Growth

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The global venture capital (VC) ecosystem is operating under a disciplined investment framework in 2026. Following years of valuation adjustments and shifting liquidity environments, venture capital firms and private equity investors are prioritizing proven unit economics, positive cash flow pathways, and capital efficiency over rapid, unconstrained user acquisition.

The Shift Toward Disciplined Startup Valuations
Early-stage and growth-stage startup valuations have stabilized at sustainable historical averages. Venture capital partners are conducting rigorous due diligence processes before deploying capital, scrutinizing gross margins, customer acquisition costs (CAC), net revenue retention (NRR), and lifetime value (LTV) metrics.

While total capital deployed remains robust, seed and Series A funding rounds are taking longer to finalize. Founders are expected to demonstrate clear product-market fit and defensible intellectual property rather than relying on top-line revenue projections unsupported by strong underlying economics.

M&A Activity and Liquidity Solutions
The market for venture-backed exits is seeing renewed momentum through strategic mergers and acquisitions (M&A) and secondary market liquidity facilities. Established corporate enterprises are acquiring high-performing technology startups to integrate proprietary artificial intelligence models and specialized software solutions into their product ecosystems.

Simultaneously, secondary market transactions have become an essential liquidity mechanism for early employees and institutional investors. Specialized secondary funds are purchasing pre-IPO shares at discounted valuations, providing liquidity opportunities while companies remain private for longer durations.

Sector Allocation: Deep Tech, Clean Energy, and Enterprise Automation
Venture capital investment is heavily concentrated in deep technology and capital-intensive engineering sectors. High-growth investment themes include:
– Next-Generation Semiconductors: Hardware startups designing specialized AI processors and energy-efficient microchip architectures.
– Clean Technology: Battery chemistry innovations, carbon capture solutions, and grid-scale energy storage startups.
– Enterprise Process Automation: Software platforms that automate complex workflows in healthcare, financial services, and industrial logistics.

Key Insights for Entrepreneurs and Investors
1. Prioritize Capital Efficiency: Startups focused on achieving operational profitability receive higher valuation premiums from institutional investors.
2. Strategic Exit Planning: Corporate M&A is serving as a primary exit route for venture-backed startups navigating prolonged IPO windows.
3. Focus on High-Moat Technologies: Deep tech and proprietary software architectures are securing the majority of growth-stage capital allocations.

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