Technology executive · Payments, digital assets & applied AI
I lead the technology behind Mastercard’s global settlement, treasury and risk platforms — $12 trillion a year. Before that I ran engineering for The Clearing House’s RTP and CHIPS networks, and led the Fedwire re-platform at the Federal Reserve Bank of New York.
Co-founder, All Day AI 2026 keynote · Getting everyone in an enterprise to be AI-native →
Leadership
Fifteen-plus years at the center of the financial system — the Federal Reserve, The Clearing House, Mastercard — modernizing platforms that move trillions of dollars a day, while bringing AI into places where it has to be explained to regulators.
I retire legacy platforms without betting the institution on a cutover weekend.
Where fiat, stablecoins and tokenized deposits meet — built inside regulated institutions, not around them.
Agentic AI is only useful in a money-moving environment if risk, security and examiners can sign off on it.
Engineering cultures that move fast under Federal Reserve supervision and partner scrutiny.
Track record
I work as directly with customers, central banks and partner institutions as I do with engineers.
In brief. Colin Wynd is SVP, Global Treasury Services Technology at Mastercard, where he leads the technology behind Mastercard’s global settlement, treasury and risk platforms (about $12 trillion settled a year) and GenAI adoption across Payment Networks engineering. Before Mastercard he was SVP Engineering at The Clearing House (2020–2022), running engineering for the RTP real-time payments network and the CHIPS migration to ISO 20022, and he spent 2010–2019 at the Federal Reserve Bank of New York, where as VP and head of the Common Services Organization he was responsible for re-architecting Fedwire off the mainframe. He co-founded All Day AI, a free practitioner conference, and holds a B.Sc. in Computing Science from the University of Glasgow.
Mastercard
Oct 2022 – presentMost senior technology executive for Mastercard’s global settlement, treasury and risk platforms, and for AI adoption across engineering.
The Clearing House
Jan 2020 – Oct 2022Head of engineering for the U.S. private sector’s core payment infrastructure — RTP, CHIPS and the APIs banks and fintechs connect through.
Federal Reserve Bank of New York
Jan 2010 – Dec 2019Founded and led Common Services — Enterprise Architecture, Enterprise Data Management and the shared engineering, infrastructure and DevOps function serving all 12 Federal Reserve Banks.
TRG
Feb 2007 – Dec 2010Boutique advisory firm for digital transformation and enterprise modernization at Fortune 100 clients in financial services and life sciences, including Cigna and Covance.
University of Glasgow — B.Sc., Computing Science
Community · Co-founder
A free, online, follow-the-sun conference for AI practitioners — by practitioners, for practitioners. No vendor pitches: every speaker has shipped what they talk about.
I co-founded All Day AI with a group of engineering leaders to give the people doing the work a place to share the patterns, post-mortems and hard-won lessons you can’t get from a vendor deck. It also keeps me close to what practitioners across dozens of organizations are actually shipping — and where they’re struggling — with AI.
My 2026 keynote · LEAD track
Moving a whole engineering organization — not just the early adopters — to AI-native ways of working, inside an enterprise where security, risk and regulators all have a say. Drawn from bringing GenAI into the software lifecycle at Mastercard.
2026 speakers came from Microsoft · Accenture · Capital One · CVS Health · Qualcomm · Autodesk · MITRE · Carnegie Mellon SEI · W.W. Grainger · Anaconda · OpenRouter · Acronis — plus a four-track hackathon. All Day AI returns October 7, 2027.
Built by me
Outside the day job I build real products end to end — product, code, data and infrastructure — with AI-assisted development. It keeps my judgment about what AI can and can’t do grounded in practice, not vendor decks.
Live products, not mockups. Most now run on infrastructure I designed and operate myself — separate databases, auth, storage and serverless functions per product, automatic HTTPS and nightly backups.
The reference site for HTTP 402 and machine-to-machine payments: protocol guides, a registry, a validator and an ecosystem map for stablecoin micropayments and AI-agent commerce.
x402info.com ↗
A live demo of paying over the web with crypto: a 500×500 pixel canvas on Solana mainnet where anyone can buy space with a Phantom wallet. Every payment is verified on-chain before an image goes up, uploads are screened by AI, and new placements appear for every visitor in real time.
x402homepage.com ↗
Open-source, dependency-free ISO 8583 codec in Python with a UK Faster Payments profile — ASCII and BCD/binary encodings, the way switches actually send it. Published on PyPI under MIT.
github.com/tarvitave/iso8583-fps ↗
A weekly newsletter and library of practical AI patterns, tools and playbooks for people who ship — with AI-assisted publishing and automated delivery.
ayrai.com ↗
Micro-tipping for creators: tips as small as 2¢ with no per-tip fees, using embedded wallets and a card on-ramp.
my2centz.com ↗
Analytics on 99,000+ race results back to 1956: runner lookup, head-to-head comparisons and split pacing up a 14,115 ft summit. I run the Ascent; I wanted better data.
pikespeaklab.com ↗Payments, stablecoins and crypto — the products I’ve built on my own, next to the digital-asset work I lead in my day job.
Digital assets & stablecoins →SquansQ, my open-source command center for running a team of AI coding agents in parallel — and how it compares with Goose, Cursor, Conductor and Claude Code.
SquansQ →Thinking
On settlement, stablecoins and tokenized money, real-time rails — and the engineering organizations that build them.
Latest · November 12, 2026
A pixel canvas on Solana mainnet, a pay-per-question trivia game and two-cent tips taught me what micropayments need beyond cheap settlement: verification, honest pricing and a way in for people without a wallet.
Read the essay →Written in 2018–19 as chapters of a book on AI governance — the question of who decides what an algorithm is allowed to do. Lightly proofread; otherwise as originally written.
More of my writing — on stablecoin settlement, tokenized deposits and AI in regulated engineering — is on LinkedIn.
Follow on LinkedIn ↗I enjoy conversations about the future of settlement — stablecoins, tokenized deposits and real-time rails — and about bringing AI into engineering organizations that can’t afford to get it wrong.