Capital Markets FinTech Forum

Hosted by ipushpull

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Latest episode

Oct 2026

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EN-GB

About the show

The Capital Markets FinTech Forum by ipushpull provides regular industry news, interviews and discussion on hot topics impacting our sector. Being a fintech gives us a unique perspective on the market as well as access to the views of leading banks, brokers, traders, asset managers and data services businesses. Whether you're interested in APIs, data distribution, chatbots or workflow automation, we aim to bring you interesting perspectives and insight from across the financial services sector. For more information on ipushpull head to https://bit.ly/3fKvY4n #fintech #data #automation #workflow #trading #brokers

Listen to episodes

36 recent

October 10, 202624 min

The Future of Fixed Income Trading AI, Liquidity & Data Standards | Matthew Coupe, Susquehanna

The future of fixed income trading is being shaped by AI, electronic trading, fragmented liquidity and the growing amount of information sitting inside trading conversations. In this episode of the Capital Markets FinTech Forum, Matthew Cheung, CEO of ipushpull, speaks with Matthew Coupe, Head of Fixed Income Sales & Product at Susquehanna and Co-Chair of the EMEA Fixed Trading Community. They discuss how liquidity in European credit markets is changing, why fixed income still depends heavily on trust and relationships, and how AI is making previously unstructured information from chat and voice conversations searchable and actionable. The conversation also explores the growing problem of information overload in trading chat rooms, the role of humans in curating market information, hybrid human and automated trading desks, post-trade workflows, and why data standards remain critical in an AI-driven financial market. Key topics covered: • The changing liquidity landscape in European credit • The rise of electronic trading in fixed income • AI and the future of trading chat • Turning unstructured conversations into structured data • Information overload in financial markets • Humans vs automation on the trading desk • Building trust as a new market entrant • Why data standards are critical for AI • AI-driven trade capture and workflow automation • The future of fixed income trading For more information go to: https://ipushpull.com

October 10, 202625 min

The Three Barriers Stopping AI Agents From Executing Trades | David Rickard, LSEG

AI agents are already moving into financial-market workflows, but when will they actually be trusted to execute trades? In this conversation, David Rickard, Global Head of Fixed Income at LSEG, explains why AI-powered trade execution is closer than many people think, but still faces significant challenges. We explore the three major barriers: the difficulty of processing real-time, low-latency data; governance and trust around automated execution; and the fragmented, deeply entrenched legacy systems that underpin many trading workflows. David also explains why fixed income is particularly difficult to electronify, how AI is already being embedded into trading workflows, and what needs to change before AI agents can move from assisting traders to actually executing trades. For more information go to: https://ipushpull.com

October 10, 202620 min

How AI Is Transforming Fixed Income Trading | Gareth Coltman, MarketAxess

How is AI changing fixed income trading, and how close are we to a world where AI agents can actually execute institutional trades? In this episode of the Capital Markets FinTech Forum, Matthew Cheung, CEO of ipushpull, speaks with Gareth Coltman, Head of Product for EMEA & APAC at MarketAxess, about the evolution of electronic fixed income markets and the growing role of AI, machine learning and automation. Gareth explains how MarketAxess has been using machine learning for more than a decade, how data is improving counterparty selection and trading decisions, and why generative and agentic AI could represent the next major shift in institutional trading. They also explore why voice trading continues to play an important role in fixed income, how firms are managing information leakage around large trades, and how AI agents are already being used across pre-trade research, analysis, simulation and post-trade workflows. The conversation also looks at one of the biggest questions facing capital markets: when will AI move from supporting human traders to actually executing trades? For now, Gareth explains why the human remains firmly in the loop — and what needs to change around governance, risk and regulation before truly agentic trading becomes a reality. Topics discussed: • AI and machine learning in fixed income • The electronification of bond markets • Data-driven counterparty selection • Voice vs electronic trading • Information leakage and large block trades • AI agents in institutional trading • Human-in-the-loop workflows • Risk, governance and regulation • The future of agentic execution • The next wave of fixed income electronification For more information go to: https://ipushpull.com

September 8, 202625 min

The Four Pillars of an Agent Strategy — Chat, Data, AI, Governance

AI agents are moving quickly into financial markets, but before firms think about agents, they need to think about the foundations underneath them. Matt Chung, CEO of iPushpull, speaks to Steve Grobe, founder of Vision57, about the four pillars of an effective agent strategy: chat, data, AI and governance. The conversation explores why chat has become an untapped source of valuable market data, how firms can turn unstructured conversations into real-time signals and workflows, and why the companies that gain access to this data early could have a significant competitive edge. Matt also explains why governance should not be seen as a constraint on AI, but as the layer that enables firms to move faster. From permissions and identity management to logging, audit and workflow controls, the conversation looks at what needs to be in place before agents can operate safely in regulated markets. Discussed in this episode: - The four pillars of an agent strategy: chat, data, AI and governance - Why firms need a data strategy before they build an agent strategy - Turning unstructured chat into real-time market signals and workflows - The hidden value inside hundreds of trading chat rooms - Why governance and controls can become a competitive moat - The risk of shadow AI and uncontrolled agent adoption - Buy versus build - The autonomy slider - Why AI is likely to change the trader's role rather than replace it Follow Matthew Cheung: https://www.linkedin.com/in/matthewcheung50/ For more information go to: https://ipushpull.com

August 11, 202634 min

Know Your Agent: What regulated trading needs before it trusts AI

AI agents are already shaping execution decisions in regulated markets, but the industry still has no agreed way to identify them, authorise them or trace what they did. Matthew Cheung, CEO of ipushpull, speaks to Rebecca Healey, Managing Partner of Redlap Consulting and author of the Mindful Markets newsletter. Rebecca co-chairs the FIX EMEA and FIX AI Working Groups, sits on the FCA and ESMA Secondary Markets Advisory Committees, and was Credit Suisse's first registered electronic trader for European cash equities. The conversation covers the case for a verified legal entity identifier attached to every agent, why "know your agent" has to sit alongside know your customer, and why one-and-done model testing no longer survives contact with agentic workflows. Rebecca also explains why FIX tag 527 is interpreted differently across sell side firms, what happens to MCP the moment a message leaves the building, and what the Mythos withdrawal exposed about operational resilience when a frontier model can disappear in days. Discussed in this episode - No LEI, no agent: linking agent identity to the firm's legal entity identifier - KYA instead of KYC: intent, guardrails and the deterministic control envelope - Runtime governance versus annual model risk sign-off - Where FIX, ISO TC68, MCP, A2A and FIDO each fit - Sovereign AI, Chinese open-source models and business continuity - Why the trader's job gets more complex rather than disappearing Follow Rebecca Healey: https://www.linkedin.com/in/rebecca-healey/ For more information go to: https://ipushpull.com

July 28, 202639 min

Tony Seale - The Knowledge Graph Guy on LLMs, Knowledge Graphs & Building for the Agentic Web

Knowledge graphs have gone from a niche data architecture concern to the thing every boardroom is suddenly asking about. Tony Seale, known across LinkedIn as the Knowledge Graph Guy, has been making the case for over a decade, first at Deutsche Bank and then as the architect of the UBS knowledge graph. In this episode he talks to David Jones, co-founder and CTO of ipushpull, about why relational databases lose the nuance that matters, what an ontology actually is once you strip away the mystique, and why large language models swing from expert to useless the moment they step outside their training distribution. Tony explains the neural symbolic loop: LLMs now remove roughly 80% of the effort of building a knowledge graph, and the graph in turn gives the model an internal, verifiable structure to reason over. He is blunt about the limits. There is no AI fairy. The work is still hard, and the organizations that put it off are the ones that will struggle in what he calls phase three, the agentic web, where data moves across organizational boundaries on open standards. Also covered: why your out-of-distribution data is the only thing left that is worth anything, why distributed identifiers and open standards are unavoidable, and why "context graph" and "agent memory" are mostly rebrands of work that has 30 years of research behind it. For more information go to: https://ipushpull.com

June 16, 202643 min

Inside NatWest's Approach to Agentic AI | Dr. Paul Dongha

In this episode of the ipushpull Capital Markets FinTech Forum podcast, Matthew Cheung sits down with Dr. Paul Dongha, Head of Responsible AI and AI Strategy at NatWest Group, to unpack what agentic AI actually means for banking, and why governance is what decides whether AI scales or stalls. Paul has been thinking about AI agents since before most people had heard the term. He finished his PhD on AI agent reasoning and the alignment problem back in the 1990s, then spent almost three decades putting it to work inside seven banks. Today he leads NatWest's efforts to deploy AI in a way that's safe, transparent, and auditable, and he leads the bank's collaboration with the FCA, where he's live testing agentic AI. Before NatWest he was Group Head of Data and AI Ethics at Lloyds. He's also co-author of Governing the Machine, How to Navigate the Risks of AI and Unlock Its True Potential, an FT book of the month, and he's about to launch a new course called Responsible AI in Practice. In our conversation, we covered: - Why AI governance is the unlock for scaling AI, not a compliance burden - Why LLMs will become commodities, and the browser war that proves it The levels of autonomy, from human in control to on the loop - The alignment problem he studied in the 1990s, still unsolved today - Using customer data to bring back the bank manager who actually knew you - Why agents should be treated as tools, not given employee logins - MCP as a base layer, and the governance checks still missing - How the EU AI Act and FCA consumer duty fit what firms were doing anyway - Why AI literacy matters most, and taking the hype with a pinch of salt Connect with Dr. Paul Dongha: https://www.linkedin.com/in/paul-dongha/ Resources: https://www.amazon.co.uk/Governing-Machine-navigate-unlock-potential/dp/139942629X https://maven.com/georgiana-marsic/ai-governance-framework For more information go to: https://ipushpull.com

May 21, 202619 min

Governed Data as the Competitive Moat in the Agent Era - Regulation and What It Means for Markets

The EU AI Act isn't GDPR. It's product safety law. In this episode of the ipushpull Capital Markets FinTech Forum podcast, Matthew Cheung sits down with Rosie Nance, Senior Knowledge Lawyer at Norton Rose Fulbright, to unpack what the EU AI Act actually is, how it sits alongside existing regulation, and what the rise of agentic AI means for financial services firms operating across the UK, EU, and US. Rosie is part of Norton Rose Fulbright's data, cyber, and AI regulation team, advising financial services clients on AI Act compliance, specific use case rollouts, and the wider governance programmes needed to navigate a fast-moving regulatory landscape. She shares how firms should be thinking about provider vs deployer roles, high-risk use cases, and the multi-stakeholder effort now required to ship AI responsibly. In our conversation, we covered: - EU AI Act as a product safety framework - High risk AI use cases in finance - Data governance and audit trails for AI - Regulatory landscape across regions Resources: https://www.insidetechlaw.com/new-artificial-intelligence For more information go to: https://ipushpull.com

May 7, 202625 min

The Model Is Not the Moat: Alvin Wang Graylin on the Stanford Enterprise AI Playbook

Data is the real moat. Frontier models are becoming a commodity. In this episode of the ipushpull Capital Markets FinTech Forum podcast, Matthew Cheung sits down with Alvin Wang Graylin, Digital Fellow at the Stanford Digital Economy Lab and co-author of The Enterprise AI Playbook: Lessons from 51 Successful Deployments With over 35 years of experience across AI, semiconductors, immersive computing, and cybersecurity at HTC, Intel, IBM, and Trend Micro, four founded startups, and investments in 100+ early-stage firms, Alvin shares the findings from a five-month Stanford study of 51 enterprise AI deployments across 41 organisations and 9 industries on how AI is actually being adopted inside large organisations. In our conversation, we covered: Why MIT's "95% AI failure" headline collapsed under deeper methodology Why data, not model choice, is the durable enterprise moat The productivity gap between full automation and human-in-the-loop Why two-thirds of successful AI rollouts started as failures Why HR, legal, and compliance are the biggest internal blockers to enterprise AI Why 65% of white-collar work in the US, UK, and East Asia is exposed to AI Resources: https://digitaleconomy.stanford.edu/p... https://open.substack.com/pub/abundan... https://www.youtube.com/watch?v=pZXs0entv-c OurNextReality.com Follow Alvin Wang Graylin: https://www.linkedin.com/in/agraylin/ ABOUT US: ipushpull is a cloud-based real-time data sharing and workflow automation platform. Used across sell-side and buy-side, and front to back office, ipushpull improves workflow efficiency by allowing cross application secure, audited, access-controlled live data sharing, collaboration, and workflow automation. The API first platform handles static, live, and streaming data with APIs, integrations, and connectors into many data platforms, applications and services. For more information go to: https://ipushpull.com

December 17, 202522 min

Beyond the LLM: How Koch and ipushpull Automated Trade Capture from Chat

In this episode of the ipushpull Capital Markets FinTech Forum, Robert Kingham, Head of North America Sales, ipushpull, sits down with Tim Flynn, IT Leader at Koch Energy Services. Tim is a seasoned technology leader with over 20 years of experience delivering trading and risk management solutions in the energy markets. Currently leading technology for Koch Energy Services’ natural gas trading and marketing business In this conversation, we explore: - What was trade capture like before automation - How does Trade Capture & Booking turn chat into booked trades - How does AI trade capture improve compliance and auditability - Where is AI automation heading next for traders For more information go to: https://ipushpull.com

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