Curiosity Code Podcast

Alex Khomyakov

Discover the fintech podcast "Curiosity Code", featuring exclusive interviews on fintech innovation and financial technology. Hosted by Alex, founder of the digital agency Productera, with a background in B2B and SaaS companies, the podcast brings together founders, CEOs, visionaries, and fintech leaders driving financial innovation, AI, open banking, and digital transformation.Each episode offers success stories, challenges, and groundbreaking ideas about the future of blockchain, AI agents, digital payments, wealth management, and other related financial technologies.

  1. 1h ago

    Jev vs LLMs: What Should Decide Fraud and Credit?

    Lovely listener, share with us your thoughts! When you tap your card, a model decides whether the payment is fraud in less than a tenth of a second. This autumn a startup raised $870 million for a model built for exactly these yes-or-no calls, one that cannot write a single word. Six days after it launched, independent researchers found a problem with the one thing it was sold on. A solo episode of Curiosity Code. Should the systems that decide fraud, credit and payment routing run on small specialized models or on large language models? My answer: small against large is the wrong line. Draw it at numbers against text. Transactions go to specialized models you own and can explain. Text judgment calls go to a cheap classifier first, with an LLM for what it isn't sure about. And a probability you haven't checked on your own data is only a number. Three things to do: list the decisions your systems make and mark each one as numbers or text, and keep the numbers ones on models you own, can explain and can retrain. For the text ones, put a small, cheap model in front, set the threshold by what a mistake costs, and send what it is unsure about to a large model or a person. Before you trust any threshold, take a few hundred of your own cases where you know the answer and check that 90% means 90%, for the small model and the large one. The question I can't settle: US bank regulators left generative AI out of their new model-risk guidance. A model like Jev generates no text, but a security firm says it appears to be a language model underneath. So which rulebook is it under? Tell me in the comments, or message me on LinkedIn. Chapters 0:00 A model that can't write a word 0:36 Where I stand, and the model that started this 1:37 Numbers vs text: where I draw the line 2:17 What a past guest already told me 3:50 Numbers: what fraud models run on today 5:25 Text: cheaper, and the calibration test 7:59 What the rules already say 8:47 Three things to do, and what I can't settle Episodes mentioned Abhishek Taneja, "How Do We Make Financial Advice Independent of the Advisor?": https://youtu.be/ZzGozVZEVY4 Srishti Jain Andreasen, "AI Fraud Detection in Payments": https://youtu.be/VytBROWzqDA Sources TypeSafe AI, "Introducing System One models and Jev" (15 September 2026): https://typesafe.ai/blog/introducing-system-one-models-and-jev TypeSafe AI, "TypeSafe AI raises Series AI", $870M led by Andreessen Horowitz (9 October 2026): https://typesafe.ai/blog/series-ai Sardine, "Testing TypeSafe AI's Jev for real-time fraud decisions" (2 November 2026): https://sardine.ai/blog/typesafe-ai-jev-fraud-detection Stripe, "How we built it: Stripe Radar": https://stripe.com/blog/how-we-built-it-stripe-radar Stripe Sessions 2025, payments foundation model (7 May 2025): https://stripe.com/newsroom/news/sessions-2025 Mastercard, "Mastercard supercharges consumer protection with gen AI" (1 February 2024): https://newsroom.mastercard.com/news/press/2024/february/mastercard-supercharges-consumer-protection-with-gen-ai Adyen, "The AI behind Uplift" (17 January 2025): https://www.adyen.com/knowledge-hub/the-ai-behind-uplift Visa, Large Transaction Models (2026): https://usa.visa.com/content/dam/VCOM/regional/na/us/Solutions/documents/large-transaction-models.pdf Revolut, PRAGMA (arXiv 2604.08649): https://arxiv.org/abs/2604.08649 Nubank, nuFormer (arXiv 2507.23267): https://arxiv.org/abs/2507.23267 Valdes Gonzalez, small models vs LLMs on cost, latency and accuracy (arXiv 2602.06370, February 2026): https://arxiv.org/abs/2602.06370 Ibrahim & Zaki, "Evaluating Decision Models for Text Annotation in Computational Social Science" (arXiv 2609.24574, September 2026): https://arxiv.org/abs/2609.24574 TransUnion, score migration study (12 July 2023): https://newsroom.transunion.com/score-migration-study/ EU AI Act, Annex III: https://artificialintelligenceact.eu/annex/3/ US Regulation B, 12 CFR 1002.9 (adverse action notices): https://www.ecfr.gov/current/title-12/chapter-X/part-1002/section-1002.9 Federal Reserve, SR 26-2 attachment, footnote 3 (17 April 2026): https://www.federalreserve.gov/supervisionreg/srletters/SR2602a1.pdf 🎙️ Subscribe for weekly episodes on fintech, AI, and innovation 🌐 Website: https://curiositycodepodcast.com/ 📱 Follow on LinkedIn: https://www.linkedin.com/in/alexkhomyakov/ 🎧 Listen on Spotify: https://open.spotify.com/show/4ePKf2qCLzcKATS7o6w2U5 🍎 Apple Podcasts: https://podcasts.apple.com/podcast/id1797349967 Music: "Soundbed" by Blue Saga, via Epidemic Sound. 🎙️ Subscribe for weekly episodes on fintech, AI, and innovation 🌐 Website: https://curiositycodepodcast.com/ 📱 Follow on LinkedIn: https://www.linkedin.com/in/akhomyakov/ 🎧 Listen on Spotify: https://open.spotify.com/show/curiosity-code 🍎 Apple Podcasts: https://podcasts.apple.com/podcast/the-curiosity-code-podcast/id1748596092

  2. Nov 3

    How Do We Make Financial Advice Independent of the Advisor? | Abhishek Taneja

    Lovely listener, share with us your thoughts! The most important data in wealth management is not in a database. It lives in advisors' heads and in their notebooks. Abhishek Taneja, an AI and machine learning practitioner who spent two decades across retail banking and wealth management, explains why that matters and what it would take to give every client a baseline of good advice, whichever advisor they get. In this episode, Abhishek compares retail banking, where every transaction is data and a model learns within months, with wealth, where the data is thin and the feedback loop runs for decades. He explains why wealth used machine learning for portfolio construction long before it touched advice, why the advice itself should still come from people rather than an LLM, and what hedge funds can do with data that a regulated manager of retail money cannot. He walks through how people in Asia actually get financial advice, through bank relationship managers, tied insurance agents and bancassurance, and why clients move from robo-advisors to human advisors once their savings grow. He closes on why tree-based models still beat tabular foundation models for decisions a regulator has to understand, and what an advisor's day looks like in five years. Key topics discussed: 0:00 Introduction 1:12 Moving from retail banking to wealth 4:13 Where wealth still runs on the advisor's judgement 6:33 Why wealth adopted machine learning later than banking 11:44 Why advice is where AI hesitancy is strongest 15:47 What hedge funds can do that asset managers can't 19:38 How people in Asia get financial advice 24:22 Making advisory independent of the advisor 28:08 Will people accept AI advice? 31:04 AI copilots vs human advisors 35:50 Tree models vs tabular foundation models 40:58 The advisor's job five years from now Abhishek Taneja is an AI and machine learning practitioner with over two decades of experience across retail banking and wealth management, focused on data, machine learning and business transformation. He has worked at institutions including Citibank and HSBC, and his work centres on scaling data capabilities, putting machine learning models into production and driving AI transformation in financial services. He appears on the podcast in a personal capacity. 🎙️ Subscribe for weekly episodes on fintech, AI, and innovation 🌐 Website: https://curiositycodepodcast.com/ 📱 Follow on LinkedIn: https://www.linkedin.com/in/alexkhomyakov/ 🎧 Listen on Spotify: https://open.spotify.com/show/4ePKf2qCLzcKATS7o6w2U5 🍎 Apple Podcasts: https://podcasts.apple.com/podcast/id1797349967 This conversation explores AI in wealth management, the future of financial advice, explainable machine learning and how wealth and asset managers in Asia reach their clients. For anyone building in wealthtech, advisory or AI for financial services who wants to understand where human advice ends and automation begins. 🎙️ Subscribe for weekly episodes on fintech, AI, and innovation 🌐 Website: https://curiositycodepodcast.com/ 📱 Follow on LinkedIn: https://www.linkedin.com/in/akhomyakov/ 🎧 Listen on Spotify: https://open.spotify.com/show/curiosity-code 🍎 Apple Podcasts: https://podcasts.apple.com/podcast/the-curiosity-code-podcast/id1748596092

  3. Oct 23

    AI Coding Agents in Finance: What to Hand Over, What to Keep

    Lovely listener, share with us your thoughts! 9 out of 10 professional developers now use AI coding agents every week. This year, the best agent in a benchmark built to look like real work passed fewer than 15% of its checks. Both are true at once. A solo episode of Curiosity Code on which engineering work a financial firm should hand to an AI-driven team and which it should keep with conventional developers. My company, Productera, sells both, so I make money whichever way you decide. My answer: writing the code got cheap, being accountable for it did not. Hand over the support work, new builds a senior engineer can read end to end, reading old code and small bounded changes. Keep long-lived core systems, regulated and security code, and anything that needs an independent sign-off. Europe's DORA already draws that line. The three questions for your backlog: Will this code still be running in three years? Does it touch a regulation, money movement or security? And is there someone senior who didn't write it, with time to read all of it? Short-lived, low stakes and reviewable goes to an AI-driven team. Long-lived, regulated, or not properly reviewable stays with conventional developers. Chapters 0:00 90% use AI agents, the best passes under 15% 0:41 Why this is a solo episode, and where I stand 2:16 Writing code got cheap, accountability didn't 2:48 What I'd hand to an AI-driven team 4:33 What I'd keep with conventional developers 6:36 The rule that already draws the line 7:35 Three questions for your backlog Sources JetBrains, AI coding agent adoption in 2026 (survey of 15,000+ developers, August 2026): https://blog.jetbrains.com/research/2026/08/ai-coding-agent-adoption-2026/ Orlanski et al., SlopCodeBench, University of Wisconsin-Madison (arXiv, May 2026): https://arxiv.org/abs/2603.24755 Brett Codes, "Real risks of using AI as a dev team" (September 2026): https://youtu.be/g0jjNoR09Tc Fred Brooks, The Mythical Man-Month (1975): https://en.wikipedia.org/wiki/The_Mythical_Man-Month Morgan Stanley's DevGen.AI, 9 million lines and 280,000 hours (Business Insider, June 2025; first reported by the Wall Street Journal): https://www.businessinsider.nl/a-team-of-engineers-saved-morgan-stanley-more-than-280000-hours-this-year-the-bank-says-their-tool-wont-take-jobs/ Veracode, GenAI Code Security Report (July 2025): https://www.veracode.com/blog/genai-code-security-report/ Faros AI, The AI Productivity Paradox (July 2025): https://www.faros.ai/blog/ai-software-engineering DORA, Commission Delegated Regulation (EU) 2024/1774, Article 17 on ICT change management: https://www.springlex.eu/en/packages/dora/rts-rmf-regulation/article-17/ Federal Reserve SR 26-2, model risk management guidance (April 2026): https://www.federalreserve.gov/supervisionreg/srletters/SR2602a1.pdf 🎙️ Subscribe for weekly episodes on fintech, AI, and innovation 🌐 Website: https://curiositycodepodcast.com/ 📱 Follow on LinkedIn: https://www.linkedin.com/in/alexkhomyakov/ 🎧 Listen on Spotify: https://open.spotify.com/show/4ePKf2qCLzcKATS7o6w2U5 🍎 Apple Podcasts: https://podcasts.apple.com/podcast/id1797349967 Disclosure: Productera supplies both AI-driven teams and conventional developers to financial firms. Music: "Soundbed" by Blue Saga, via Epidemic Sound. 🎙️ Subscribe for weekly episodes on fintech, AI, and innovation 🌐 Website: https://curiositycodepodcast.com/ 📱 Follow on LinkedIn: https://www.linkedin.com/in/akhomyakov/ 🎧 Listen on Spotify: https://open.spotify.com/show/curiosity-code 🍎 Apple Podcasts: https://podcasts.apple.com/podcast/the-curiosity-code-podcast/id1748596092

  4. Oct 14

    Who's Liable When AI Agents Make Payments? | Abril Benhumea

    Lovely listener, share with us your thoughts! An AI agent books your flight, stays inside the budget you gave it, and pays for the wrong date. Who is responsible? Abril Benhumea, fintech and payments executive and co-founder of Passport, walks through what has to happen between an agent deciding to buy and the money actually moving. In this episode, Abril explains how Passport worked as an interoperability and orchestration layer on top of a bank's own infrastructure, and how real-time payment systems settle and reconcile differently from batch systems like ACH. She splits an agent payment into three layers (decisioning, trust and execution) and argues that execution already exists while trust is still being built: how a bank knows who an agent represents and what it was allowed to do. She also covers know your agent (KYA), the competing protocols from Google, OpenAI and Anthropic, and who stands to earn from agentic commerce as it grows. The market figures Abril mentions come from Bain & Company's research on agentic commerce: AI is under 1% of retailers' total traffic today, and Bain forecasts agentic commerce at 15 to 25% of US e-commerce sales by 2030. Key topics discussed: 0:00 Introduction 1:23 Passport as a payments orchestration layer 3:25 Real-time vs batch payment reconciliation 6:23 Why trust is missing in agentic commerce 13:24 Decisioning, trust and execution explained 19:38 What breaks first when agents are the payer 22:25 Google, OpenAI and the orchestration opportunity 25:10 How soon agentic payments go mainstream 27:56 Market size and where the revenue flows 32:38 Closing thoughts and open questions Abril Benhumea is a fintech and payments executive and co-founder of Passport, where she helped build open banking and real-time payments infrastructure connecting financial institutions and fintechs across the Americas. Her career spans payments infrastructure, banking-as-a-service, cross-border payments and financial partnerships, including roles at Synapse and Veem, and today her focus is the infrastructure behind agentic commerce. Connect with Abril Benhumea on LinkedIn: https://www.linkedin.com/in/abril-benhumea 🎙️ Subscribe for weekly episodes on fintech, AI, and innovation 🌐 Website: https://curiositycodepodcast.com/ 📱 Follow on LinkedIn: https://www.linkedin.com/in/alexkhomyakov/ 🎧 Listen on Spotify: https://open.spotify.com/show/4ePKf2qCLzcKATS7o6w2U5 🍎 Apple Podcasts: https://podcasts.apple.com/podcast/id1797349967 This conversation explores agentic commerce, open banking, real-time payments infrastructure and the future of payments orchestration as AI agents begin transacting on behalf of people. If you're building in fintech, banking-as-a-service or payments infrastructure, this episode maps out where the agentic payments market is heading and what institutions need to prepare for. 🎙️ Subscribe for weekly episodes on fintech, AI, and innovation 🌐 Website: https://curiositycodepodcast.com/ 📱 Follow on LinkedIn: https://www.linkedin.com/in/akhomyakov/ 🎧 Listen on Spotify: https://open.spotify.com/show/curiosity-code 🍎 Apple Podcasts: https://podcasts.apple.com/podcast/the-curiosity-code-podcast/id1748596092

  5. Oct 9

    Your Team Doesn't Need to Understand RAG. It Needs These 3 Things

    Lovely listener, share with us your thoughts! Prudential in Singapore built an AI assistant for its insurance representatives. Six of its own experts graded the answers, and 59% failed. Four months later 99% passed. Nobody on that team had to learn how the model works. A solo episode of Curiosity Code on what people in a financial firm actually need to know about RAG and AI agents. Most need to know how to use it and where it goes wrong. The firm needs three things: an owner who decides what a correct answer is, a set of graded questions it reruns whenever something changes, and a register of which model wrote which answer. I show the three ways an answer goes wrong on a small mock-up (a made-up travel insurer, a real model), then what OpenAI, Anthropic and Singapore's regulator have published about it. Three questions for your team: Which model produced the last AI answer that someone here relied on? If that model changed tomorrow, how would we find out? And who decides what a correct answer is? Chapters 0:00 An AI assistant that failed 59% of the time 0:24 What most people need to know, and what they don't 1:20 Why Prudential's assistant scored 41% 2:35 Three ways an answer goes wrong 6:27 Who does the work 7:28 Three questions for Monday Episodes mentioned Stéphane Malrait, "Burning Cash Too Fast? Why Most Fintechs Collapse Within 3 Years": https://youtu.be/iXeaZOwQ9SU Daniel Flowe, "The DARK Side of AI in Finance, Can We Trust It?": https://youtu.be/dUBv8jMKlSY Sources Project MindForge, AI Risk Management Implementation Examples (the Prudential case): https://www.mas.gov.sg/-/media/mas-media-library/schemes-and-initiatives/ftig/project-mindforge/mindforge-ai-risk-management-implementation-examples.pdf Jeff McMillan of Morgan Stanley on the RegulatingAI podcast (write-up, January 2026): https://americanbazaaronline.com/2026/01/26/jeff-mcmillan-on-morgan-stanleys-human-first-ai-governance-473926/ OpenAI, API deprecations page (read 9 October 2026): https://developers.openai.com/api/docs/deprecations Anthropic, "A postmortem of three recent issues" (17 September 2025): https://www.anthropic.com/engineering/a-postmortem-of-three-recent-issues Monetary Authority of Singapore, Guidelines on Artificial Intelligence Risk Management (October 2026): https://www.mas.gov.sg/-/media/mas-media-library/regulation/guidelines/bd/guidelines-on-artificial-intelligence-risk-management-for-financial-institutions/guidelines-on-artificial-intelligence-risk-management.pdf 🎙️ Subscribe for weekly episodes on fintech, AI, and innovation 🌐 Website: https://curiositycodepodcast.com/ 📱 Follow on LinkedIn: https://www.linkedin.com/in/alexkhomyakov/ 🎧 Listen on Spotify: https://open.spotify.com/show/4ePKf2qCLzcKATS7o6w2U5 🍎 Apple Podcasts: https://podcasts.apple.com/podcast/id1797349967 The mock-up in this video is an illustration: the insurer and its policy wording are made up, the model output is real (Claude Opus 5.5, 9 October 2026). Music: "Soundbed" by Blue Saga, via Epidemic Sound. 🎙️ Subscribe for weekly episodes on fintech, AI, and innovation 🌐 Website: https://curiositycodepodcast.com/ 📱 Follow on LinkedIn: https://www.linkedin.com/in/akhomyakov/ 🎧 Listen on Spotify: https://open.spotify.com/show/curiosity-code 🍎 Apple Podcasts: https://podcasts.apple.com/podcast/the-curiosity-code-podcast/id1748596092

  6. Oct 6

    Programmable Self-Custody for Crypto | Frank Hepworth

    Lovely listener, share with us your thoughts! Frank Hepworth breaks down programmable self-custody, an onchain model where clients own their crypto accounts and a manager can trade in them but never withdraw from them. Self-custody crypto, onchain asset management, and why most crypto still sits with exchanges and custodians. 🔑 Key Topics & Timestamps: 0:00 Introduction 1:12 What holding crypto on an exchange really costs you 4:49 Why banks entering crypto won't offer liquidity pools 6:32 Programmable self-custody explained with one key vs two 9:09 How the three-key system protects client withdrawals 11:56 Who decides the whitelisted protocols clients can trade 13:33 Why withdrawals are capped and reviewed carefully 16:33 Who the clients are and why trust comes first 20:14 Why the customer relationship matters more than the rails Frank Hepworth is the Founder and CEO of New Market Trading, an onchain asset manager built on self-custody. Each client owns their own account, and New Market Trading can trade in it but cannot withdraw from it. Before founding the company, Frank was a securities regulatory attorney working in digital assets, and later spent three years building Yieldschool, a crypto education company. Connect with Frank: LinkedIn: https://www.linkedin.com/in/frankhep X: https://x.com/frank_hep New Market Trading: https://newmarkettrading.com/ 🎙️ Subscribe for weekly episodes on fintech, AI, and innovation 🌐 Website: https://curiositycodepodcast.com/ 📱 Follow on LinkedIn: https://www.linkedin.com/in/alexkhomyakov/ 🎧 Listen on Spotify: https://open.spotify.com/show/4ePKf2qCLzcKATS7o6w2U5 🍎 Apple Podcasts: https://podcasts.apple.com/podcast/id1797349967 This episode covers crypto asset management, self-custody, and what it takes to run client money onchain without holding it. For fintech founders, wealth and asset managers, family offices, and anyone building in digital assets who wants to understand programmable self-custody and onchain crypto trading. 🎙️ Subscribe for weekly episodes on fintech, AI, and innovation 🌐 Website: https://curiositycodepodcast.com/ 📱 Follow on LinkedIn: https://www.linkedin.com/in/akhomyakov/ 🎧 Listen on Spotify: https://open.spotify.com/show/curiosity-code 🍎 Apple Podcasts: https://podcasts.apple.com/podcast/the-curiosity-code-podcast/id1748596092

  7. Oct 2

    Wise Owns Its Rails. Remitly Rents Them. Who's Winning?

    Lovely listener, share with us your thoughts! One remittance company owns its payment rails and keeps 52 cents of every $100. Another rents them, keeps about $2, and grew faster last year. So does owning the pipes decide who wins? A solo episode of Curiosity Code. I work through a question I've already answered twice on LinkedIn, in two different ways: crypto as the experiment where the rails couldn't be owned, what my guests Colin Butler and Elena Shiltseva said, Wise vs Remitly by the numbers, and what it means if you're building or backing a payments business in emerging markets. Plus the question I can't answer yet: in much of Africa, neither owns the last mile. The telco does. Chapters 0:00 Owning the rails vs renting them 0:44 Why remittances are worth fighting for 1:48 Crypto already ran this experiment 2:46 What my guests said 4:03 Where I land 4:38 Nuvei, Payoneer and distribution 5:29 The case for owning the rails: Wise 6:23 The case for renting them: Remitly 7:25 What it means if you're building 8:26 Who owns the last mile? 8:46 The question I can't answer yet Episodes mentioned - Colin Butler, "Crypto Won. So Where Did the Money Go?": https://youtu.be/VSVZj-sTb04 - Elena Shiltseva, "Stablecoins & DeFi: Banking's Future": https://youtu.be/9fIgozO80fY My LinkedIn posts - July 2026, on Nuvei and Payoneer: https://www.linkedin.com/posts/alexkhomyakov_fintech-paymentsindustry-mergersandacquisitions-activity-7489026193694961664-YjWN - September 2026, on build vs buy: https://www.linkedin.com/posts/alexkhomyakov_every-fintech-founder-asks-should-we-build-activity-7502697981754707968-73d6 Sources - Wise Group plc, FY2026 results (25 June 2026): https://owners.wise.com/news-releases/news-release-details/wise-group-plc-reports-full-year-2026-financial-results - Remitly Global, Form 10-K for FY2025: https://www.sec.gov/Archives/edgar/data/1782170/000162828026009038/rely-20251231.htm - Nuvei to acquire Payoneer for $2.75 billion (15 June 2026): https://www.nuvei.com/posts/nuvei-to-acquire-payoneer-for-2-75-billion-creating-a-leading-global-platform-for-local-and-cross-border-commerce - World Bank, Remittance Prices Worldwide, Issue 54 (Q3 2025): https://remittanceprices.worldbank.org/sites/default/files/2026-04/RPW_main_report_and_annex_Q325.pdf - Benji Fernandes (NALA) on Money Code: https://podcasts.apple.com/us/podcast/money-code/id1837743368 - NALA, "Why are payments in Africa still 1% built?" (22 March 2024): https://www.nala.com/blog/why-are-payments-in-africa-still-1-built-introducing-rafiki-api - Safaricom PLC, 2026 Annual Report (M-Pesa active customers): https://africanfinancials.com/document/ke-scom-2026-ar-00/ 🎙️ Subscribe for weekly episodes on fintech, AI, and innovation 🌐 Website: https://curiositycodepodcast.com/ 📱 Follow on LinkedIn: https://www.linkedin.com/in/alexkhomyakov/ 🎧 Listen on Spotify: https://open.spotify.com/show/4ePKf2qCLzcKATS7o6w2U5 🍎 Apple Podcasts: https://podcasts.apple.com/podcast/id1797349967 Every payments company decides what to own and what to rent. In cross-border payments that choice shapes margins, speed and growth, and in emerging markets the last mile may belong to neither side. Music: "Radar Focus" by Blue Saga, via Epidemic Sound. 🎙️ Subscribe for weekly episodes on fintech, AI, and innovation 🌐 Website: https://curiositycodepodcast.com/ 📱 Follow on LinkedIn: https://www.linkedin.com/in/akhomyakov/ 🎧 Listen on Spotify: https://open.spotify.com/show/curiosity-code 🍎 Apple Podcasts: https://podcasts.apple.com/podcast/the-curiosity-code-podcast/id1748596092

  8. Sep 29

    AI Agents: Who's Accountable for Mistakes? | Erich Barlow

    Lovely listener, share with us your thoughts! AI governance for AI agents in financial services: when an agent makes a mistake at computer speed, who is accountable? Erich Barlow says the organization that deployed it, not the system and not the vendor. "Regulators and customers will not accept that the AI did it." In this episode he walks through where agents are already acting on their own in financial services, how much harm a simple chatbot can really do, whether a permission written into a system prompt counts as an access control, what AI certifications like ISO 42001 actually tell you, what auditors see that engineers miss, and his research into what he calls synthetic intelligence. 📌 Key Topics & Timestamps: 0:00 Cold open 0:29 Introduction 1:42 Are AI agents already acting on their own? 3:42 Who is responsible when AI makes a mistake? 6:03 Does it change if the agent came from a vendor? 6:37 Running agents week to week 7:56 Managing an agent like an employee 9:34 When an agent hits its goal and still causes harm 13:15 How much harm can a chatbot really do? 15:12 The governance layer for customer-facing AI 16:45 Third-party AI: blame the cook, not the cow 18:44 Assistant vs autonomous agent in payments 20:04 What an agent should never decide 21:28 Are system prompts access controls? 23:16 Prompt governance and data loss prevention 25:46 AI certifications: tick box or trust? 29:07 What auditors see that engineers don't 31:42 Synthetic intelligence vs artificial intelligence 35:46 Is synthetic intelligence already here? 37:26 Where to find Erich About the Guest: Erich Barlow (MIS, CITP, FBCS) is a Chartered Fellow of the BCS working in cybersecurity and AI governance, and a doctoral researcher studying how AI standardization should be built for South Africa. He writes about synthetic intelligence: machine intelligence as its own real thing, arrived at by a different route, the way a synthetic diamond is still a diamond. Connect with Erich Barlow on LinkedIn: https://www.linkedin.com/in/erichbarlow/ On X: https://x.com/erichbarlowllc Website: https://www.erichbarlowllc.com 🎙️ Subscribe for weekly episodes on fintech, AI, and innovation 🌐 Website: https://curiositycodepodcast.com/ 📱 Follow on LinkedIn: https://www.linkedin.com/in/alexkhomyakov/ 🎧 Listen on Spotify: https://open.spotify.com/show/4ePKf2qCLzcKATS7o6w2U5 🍎 Apple Podcasts: https://podcasts.apple.com/podcast/id1797349967 A practical conversation on AI governance for anyone deploying AI agents in banks, payments and fintech: who carries the risk, what controls survive an audit, and where the accountability sits when something goes wrong. 🎙️ Subscribe for weekly episodes on fintech, AI, and innovation 🌐 Website: https://curiositycodepodcast.com/ 📱 Follow on LinkedIn: https://www.linkedin.com/in/akhomyakov/ 🎧 Listen on Spotify: https://open.spotify.com/show/curiosity-code 🍎 Apple Podcasts: https://podcasts.apple.com/podcast/the-curiosity-code-podcast/id1748596092

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Discover the fintech podcast "Curiosity Code", featuring exclusive interviews on fintech innovation and financial technology. Hosted by Alex, founder of the digital agency Productera, with a background in B2B and SaaS companies, the podcast brings together founders, CEOs, visionaries, and fintech leaders driving financial innovation, AI, open banking, and digital transformation.Each episode offers success stories, challenges, and groundbreaking ideas about the future of blockchain, AI agents, digital payments, wealth management, and other related financial technologies.

Information

  • Creator
    Alex Khomyakov
  • Years Active
    2025 - 2026
  • Episodes
    68
  • Rating
    Clean
  • Copyright
    © 2026 Curiosity Code Podcast
  • Show Website
    Curiosity Code Podcast