Why Adyen, Stripe, and PayPal still can't fix global payments - Davi Strazza (CBO, Lightspark)

Will AI agents eventually manage our investments? Could tokenization make stock markets accessible 24/7? And will we still need trading apps in the future? In this episode of the WRAP UP podcast, I sit down with Jordan Sinclair, President of Robinhood UK, to explore how artificial intelligence, blockchain technology, and changing consumer expectations are reshaping the future of investing. Robinhood is expanding beyond traditional stock trading into crypto, tokenized assets, prediction markets, and AI-powered investing. But the wider financial industry is undergoing a similar transformation, with companies like Revolut and Coinbase competing to become the primary financial platform for consumers. One of the most interesting parts of our conversation was about the role of AI agents in investing. Robinhood has already introduced agentic trading capabilities in the US, allowing customers to connect AI agents to their investment accounts, allocate funds, and define trading rules. I asked Jordan a question I've been thinking about for some time: If my AI agent can research markets, access my portfolio, and execute trades, why would I still need a trading app? We also explored the potential of tokenization to change how financial assets are owned and transferred. Today, moving stocks between brokers can take weeks. Blockchain-based infrastructure could make these transfers much faster and enable trading outside traditional market hours. Another topic we discussed was the growing popularity of prediction markets. As trading volumes increase, where should regulators draw the line between financial instruments and gambling? In this episode, we discuss: AI agents and the future of investing: Will AI eventually manage our portfolios and make trading decisions? The future of trading apps: Could AI agents replace the way we interact with financial platforms? Tokenized stocks and private markets: How blockchain could enable 24/7 trading, faster settlement, and broader access to investments. Prediction markets: Are they legitimate investment products or another form of gambling? The financial super app race: How Robinhood, Revolut, and Coinbase are competing for customers. AI risks and regulation: Who takes responsibility when AI-driven investment decisions go wrong? The future of retail investing: Why millions of people still don't invest and how technology could change that. ________

What happens when one of the UK’s largest banks puts AI at the centre of how it serves millions of customers? In this episode of WRAP UP, I sit down with Suresh Balaji, Chief Marketing Officer of Lloyds Banking Group, to discuss how Lloyds is approaching AI, agentic systems, customer trust and the future of banking. Lloyds serves around 28 million customers, with 23 million interacting with the group digitally and billions of app logins every year. Now, the bank is testing an AI assistant designed to help customers discover financial products, receive proactive support and better manage their financial lives. We discuss: • Why Lloyds already has its consumer AI assistant in beta • Where AI could first appear inside the Lloyds banking app • Why fraud prevention could become one of banking’s most valuable AI use cases • How Lloyds built Cortex and its Envoy “agent factory” • Why Lloyds believes AI needs governance, testing and lifecycle management • How one internal AI agent reduced an eight-week process to around 30 minutes • What Lloyds learned when it tested humans vs AI vs humans working with AI • Why Suresh believes it should be “humans in the lead,” rather than simply humans in the loop • How AI is changing financial-services discovery and marketing • Why trust could become an even bigger competitive advantage in AI-powered banking • Whether legacy banks can use their data and scale to compete with digital challengers • The role AI could play in Britain’s wider economic competitiveness Suresh also explains Lloyds’ three-part framework for transformation: tasks, tools and talent, and why giving employees access to AI may matter just as much as choosing the underlying models. A conversation about what AI looks like when it moves beyond experimentation and starts being deployed across one of Britain’s biggest banking groups. _______ LinkedIn: https://www.linkedin.com/in/sirojboboev/ Newsletter: https://www.fintechwrapup.com/ Instagram: https://www.instagram.com/wrapuppodcast/ Tiktok: https://www.tiktok.com/@wrapuppodcast X: https://x.com/samboboev

AI agents are beginning to shop, negotiate and make payments on behalf of consumers and businesses. But most payment infrastructure, compliance rules and liability frameworks were designed for humans. In this episode of the WRAP UP Podcast, I sit down with Philip Bruno, Chief Strategy and Growth Officer at ACI Worldwide, to explore how the payments industry must adapt to the emerging machine economy. We discuss why merchants may need to stop blocking bots and start distinguishing trusted customer agents from malicious ones. Philip explains why businesses should remain independent of any single agentic commerce protocol and how payment orchestration could help them support new protocols, rails and payment methods without losing control of their customer relationships. We also examine one of the biggest unanswered questions in agentic commerce: who is liable when an AI agent makes a mistake? Philip shares his view on how today’s payment rules are being applied to agent-led transactions and why new commercial and regulatory frameworks will be needed. The conversation then turns to what Philip calls the “three-horse race” between stablecoins, tokenised bank deposits and instant payments. We break down where each option could succeed, why instant payments may eventually overtake debit cards globally, and how stablecoins could become especially important for cross-border payments and markets with unstable currencies. Philip also explains ACI Worldwide’s approach to intelligent payment orchestration, including technology that can identify transactions likely to fail, recommend repairs and reduce the time required to fix a wire payment from around 40 minutes to two minutes. We also discuss: Why B2B agentic commerce could become much larger than consumer agentic commerce How AI agents could transform procurement and automate bidding between businesses Whether AI companies require a new payments and usage-based billing stack How merchants can decide which payment methods and protocols to support Why payment orchestration must remain independent of individual processors and rails How banks are preparing for instant payments, stablecoins and tokenised deposits What the growth of local instant payment systems means for cards and cash Why experimentation may be the only viable strategy as payments continue to fragment ACI Worldwide’s first US customer for its Kinetic cloud-native payments platform _________

What does it take to turn one of the world’s largest digital banks into an AI-first company? In this episode of the WRAP UP podcast, I sit down with Eric Young, CTO of Nubank, to discuss how the company is using AI across engineering, credit, and customer experiences. Nubank has reported that its weekly AI token use grew almost 10x since the beginning of the year, while engineering productivity increased by 50% and testing cycles became 90% faster. Eric explains how the company thinks about the return on that investment, why there is no single perfect productivity metric, and how Nubank measures changes in code delivery, product velocity and automation without reducing the work to a simplistic scorecard. We discuss why Nubank is not betting on one AI provider or one model. Eric shares how the company dynamically routes workloads across different models based on performance, cost and quality, while also exploring self-hosted open-weight models. He explains why flexibility matters in a market where model capabilities are shifting quickly. Eric also takes us inside Nubank’s own foundation-model work. With 135 million customers and hundreds of terabytes of financial and app-event data flowing through the platform each year, Nubank is building models that can better understand financial behaviour, improve credit decisions, support responsible lending and personalise experiences at scale. We also discuss the harder side of AI in banking: • How AI can help banks detect fraud patterns in real time • Why clean, structured data can be a bigger advantage than simply having more data • How Nubank tests and evaluates AI systems before exposing them to customers • Where human oversight remains essential when AI interacts with financial decisions • How the company approaches accountability, guardrails and customer protection • Why incumbents with decades of data may still struggle if their data is fragmented or poorly structured • What Nubank sees as the real competitive challenge: rising customer expectations • The skills engineers, product managers and designers need to succeed in an AI-first organisation ______________ LinkedIn: https://www.linkedin.com/in/sirojboboev/ Newsletter: https://www.fintechwrapup.com/ Instagram: https://www.instagram.com/wrapuppodcast/ Tiktok: https://www.tiktok.com/@wrapuppodcast X: https://x.com/samboboev

AI has made fraud cheaper, faster, and harder to spot. Deepfakes can pass visual checks, agents can move through websites on behalf of users, and fraud rings are buying real accounts through Telegram marketplaces. In this episode of WRAP UP, I sit down with Tamas Kadar, co-founder and CEO of SEON, to discuss how AI is making online fraud more sophisticated and what banks, fintechs and digital businesses can do to stop it. We explore how deepfakes, synthetic identities and AI agents are helping fraudsters automate attacks at scale. Tamas explains why a face, document or video is no longer enough to verify a customer, and why fraud detection now needs to go beyond a single KYC check. We discuss the signals that matter, including email, phone, IP, device intelligence, digital footprints and behavioural data. Tamas explains why risk teams need to monitor the full customer journey, from registration and onboarding to login and transaction, rather than rely on separate tools that only assess risk at one point in time. We also cover account takeover through breached credentials and the growing threat of account handover, where real people open verified accounts that are later sold through Telegram and dark web marketplaces. These accounts can then be used for mule activity, scams and moving illicit funds. Tamas shares how SEON has expanded its risk intelligence to more than 1,100 signals, how financial institutions can combine this data with their own internal AI models, and why dynamic friction is key: letting genuine customers move smoothly while introducing extra verification only when activity looks suspicious. Finally, we discuss what banks and fintechs should do now to prepare for AI-driven fraud, including how to handle AI agents, why live proofs of concept matter when selecting a fraud platform, and why simple integration should be part of every vendor decision. Learn more about SEON: https://seon.io/editions/signal-intelligence-2026/?utm_source=seon&utm_medium=fintech-wrap-up&utm_campaign=podcast_fintech-wrap-up-podcast-launch_fy26-q3&utm_content=