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Half-Day Event Series (Free Admission)
AI in Financial Markets 2026: From Hype to Impact
· PART OF OUR COMMUNITY · JP MORGAN · DEUTSCHE BÖRSE · STATE STREET · STANDARD CHARTERED · LLOYDS BANK · HSBC · NORTHERN TRUST · DEKABANK · TRUSTED BY LEADERS IN FINANCE · DTCC · CLEARSTREAM · NASDAQ · BROADRIDGE · KFW · FRANKLIN TEMPLETON · BUILDING THE FUTURE OF FINANCE · VISA · CHAINLINK · RIPPLE · FIREBLOCKS · BITGO · R3 · APEX GROUP · QUANT
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Moments from the Event
Panels & Key Discussion Topics
Keynote Address by Prof. Philip Treleaven, Professor & Director of UK Financial Computing Centre at University College London
The firms that understand agentic AI first will set the rules for everyone else. Professor Philip Treleaven, one of the UK's foremost voices on AI and financial computing, opens the day with a keynote on how autonomous agents and shared data are reshaping three fronts at once: personal investing, where AI-native advice is squeezing fund managers and intermediaries; retail banking, where hyper-personalisation is redefining customer engagement; and quantitative investing, where a new class of quant finance intelligence is changing how strategies are built. He will explain how integrated datasets and federated computing make it possible to do all of this without pooling sensitive data, and then pose the question every regulator and CEO in the room will want answered: is Open Regulation, an Open Banking for the rulebook, the next big shift? Come away with a clear view of where the advantage is moving, and what to do about it before your competitors do.
14.15 to 14.30
as Moderator
What is different about the AI Bubble?
1. The scale of the investment in AI is far out of proportion to the revenues generated by it, with even paying customers not covering the costs of serving them. Is there any development in prospect that is likely to yield a viable, commercial business model for the current version of AI?
2. We have had AI bubbles before, notably in the 1960s (“GOFAI”), and again in the 1980s (rules-based “expert systems”), where different approaches still ended in disappointment. Why should this Bubble have a different outcome?
3. The Dot Com Bubble deflated 25 years ago with an 80 per cent fall in the Nasdaq Composite Index, and more than half of Dot Com companies failed. Are Nvidia, Open Ai and Anthropic the Amazon, eBay and Facebook of the current Bubble or its Boo.com, Pets.com and Global Crossing?
4. The technology underlying the Dot Com boom was not in question. Can the same be said about AI based on Machine Learning?
5. It used to be said that the current AI Bubble is different because it is funded by cash not borrowing, but the big AI companies are increasingly funding their investments in AI with debt. Is the difference narrowing?
6. Start-ups are claiming to be using AI because it increases their chances of getting funded. What does that signify?
7. Established firms (buyers and sellers) are pretending to use AI, claiming to be AI-powered or AI-enabled or AI-driven. Does that have real consequences (such as ignoring solutions which are not AI but might be better and cheaper) or is it just marketing?
8. The sums being invested in data centres, GPUs and electricity generation to support AI are already running into hundreds of billions of dollars, but the amount of capital needed to scale AI is estimated at trillions. Is the gap between investment need and investment available bridgeable?
9. Is the scale of the investment in AI constraining investment in other sectors?
10. To what extent does the massive investment in AI erect barriers to entry to smaller companies that might achieve more with less - and should regulators do anything about it?
11. Is the current approach to AI leading to concentration risk among AI service providers (akin to Cloud services)?
12. The flows of funds between AI companies in Silicon Valley are often described as incestuous and circular. Should this worry investors and lenders active in the AI industry?
13. There is a view that AI can overturn the economics of the Internet, which are currently based on harvesting the attention of consumers. Does that explain the willingness of the leading Internet technology companies to invest so heavily in AI?
PANEL 1
14.30 to 15.15
Does AI work?
1. Machine learning has delivered in narrow areas, such as document summaries, grammar and shopping recommendations, translation, transcription, and face and voice recognition. Is that narrowness a function of technological immaturity or a fundamental weakness of machine learning (i.e., that every task requires a unique model, assumptions and data set so AI on this model cannot ever develop into a general-purpose technology comparable to electricity, the internal combustion engine or digital computing)?
2. Machines "learn" by capturing the statistical regularities in work previously done by humans in the translation and transcription of texts and categorisation of objects. Does this reliance on the prior work of humans place an uncrossable boundary on their ability to excel at a task?
3. AI machines need to scan vast quantities of data to fill in the blanks in a line of code. Does this data inefficiency place limits on what is achievable or affordable by this method?
4. AI clearly improves incrementally, as shortcomings are fixed. Are the shortcomings getting harder to fix?
5. It is often said that AI aims only to aid or enhance the performance of workers, not to replace them. Is this a practical (or ethical) judgment or a confession of technical limitations?
6. Proofs of Concept and Pilot Tests regularly prove that AI works but, unlike software bugs, reliance on data rather than code means AI machines can still deliver predictions that are wrong but appear plausible. Is there a bias to success in AI experiments?
7. Is there survivorship bias in the literature about AI (i.e., are the lessons of failed AI projects being ignored)?
8. Is there a risk that humans will take AI too seriously, ignore its shortcomings and regard it as infallible, and therefore rely on it in situations where they should not - and does that require regulatory intervention?
9. AI jargon has expanded – Machine Learning, Generative AI, Large Language Models (LLMs), Natural Language Processing (NLP), Neural Networks, Agentic AI – but all models ultimately rely on the same thing: scanning large data sets. Is the jargon just marketing?
10. Is there a constituency inside large corporations that boasts of success in AI experiments because continued funding and employment depend on it - and are they hoodwinking senior management and outside investors?
11. Regulation (such as the EU AI Act) has so far focused on obliging providers to address AI issues such as safety, risk, privacy and bias, with fines for non-compliance. Are regulators over-estimating, under-estimating, understanding or misunderstanding, the current capabilities of AI?
12. Machine learning does not appear to offer a clear path to artificial general intelligence (AGI). Is there an alternative path that might?
PANEL 2
15.15 to 16.00
What are the use cases for AI in capital markets?
The panel will begin with a brief presentation of key findings from The ValueExchange’s latest research, followed by a wider panel discussion.
The ValueExchange’s ‘AI in the real world’ research explores how AI is being applied within the capital markets. With 49% of firms now running generative AI in production, the study examines where AI is delivering value, what is holding back wider adoption, and why human oversight remains central as firms move towards more advanced, agentic applications.
1. There are plenty of general, anonymous studies that claim to prove AI has achieved measurable improvements in processes, while individual cases that worked at scale inside a named organisation are much harder to find. What does that tell us?
2. The narrow but general use cases for AI (such as document summaries, transcriptions, translation, coding) are as useful in capital markets as in any industry. Can you think of a use-case specific to capital markets?
3. The common use-cases in the front office (chatbots, algorithmic trading, investment research, robo-advice) and the middle and back office (transaction processing, financial crime compliance, market surveillance) do not feel transformational. Can you think of a transformational use case?
4. Can you think of a problem in capital markets that AI has solved definitively (as opposed to merely having a positive impact) ?
5. Can you think of an initially promising use-case that ultimately failed?
6. Might AI have deleterious effects in capital markets (e.g., increasing market speed and volatility, reducing transparency into market participants and widening the scope for fraud by manipulation of data sets)?
7. Some believe there are synergies between AI and Blockchain (e.g., portable data and identities, data verification, micro-payments, smart contracts). Do you agree?
8. Do you think AI and Blockchain are converging?
9. If the AI Bubble pops can the damage to the wider financial system be contained (e.g., are private credit markets lending to AI companies a contagion transmission mechanism)?
10. AI advertisements make lavish claims (such as "What if you could multiply yourself?" and "With the right tools, work doesn't have to feel like work"). How seriously should employers take these claims?
11. How would you describe the user experience of current versions of AI?
12. The Chinese Communist Party (CCP) has made AI the centre of its next five-year plan. They think AI is going to transform not just finance but science and technology, industrial production, defence, governance, global cooperation and the quality of life. Are you surprised to find the CCP and Silicon Valley saying the same thing?
PANEL 3
16.45 to 17.30
PANEL 4
as Moderator
PANEL 5
as Moderator
Moments from Our Latest Event in the Series
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