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Kaitlyn Thinn
Chief Strategy Officer
Global Finance & Technology Network (GFTN)
Kaitlyn Thinn heads strategy at the Global Finance & Technology Network (GFTN), where she spearheads the organisation’s strategic direction and translates it into execution. She shapes long-term priorities, identifies new growth opportunities, and steers the organisation’s most critical partnerships and strategic initiatives to advance financial innovation globally.
Kaitlyn brings both a policymaker’s and a practitioner’s perspective, operating at the intersection of regulation, banking, and technology. Prior to her current role, she served in the Monetary Authority of Singapore’s FinTech & Innovation Group, where she led national digital infrastructure and global FinTech initiatives. She also served at Citi, driving commercial lending product strategy and digital transformation across Asia Pacific.
Speaking At
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9:45–11:15amMon 16 NovPublic-Private RoundtableRoundtable Room 2OpenModerator
Accountability & Liability In Agentic Systems
Build, Buy, or Own: Strategies for Responsible AI Adoption in Finance
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Bojan ObradovicGroup Head of Innovation & Digital PartnershipsHSBC
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Jason LeeCo-Head, Legal & RegulatoryTemasek
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Jeremy HorExecutive Director, Enterprise Knowledge DepartmentMonetary Authority of Singapore
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Louise JohnstonDigital Assets & Blockchain, Global PolicyMastercard
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Norbert IzerExecutive Director for Financial Markets & DigitalizationCentral Bank of Hungary (Magyar Nemzeti Bank, MNB)
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Sunayna TutejaFormer Chief Innovation OfficerFederal Reserve System
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Varun KapoorDirector, Digital Strategy Group (FSI)Adobe
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Dr. Zhao JingyuanHead, Group Data OfficeOCBC
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Nilanshuk HaldarVice President – Global Head of Partnerships & AlliancesNucleus Software
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Prof. Heng WangProfessorSingapore Management UniversityModerator
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Kaitlyn ThinnChief Strategy OfficerGlobal Finance & Technology Network (GFTN)Moderator
About this session
Financial institutions are moving well beyond experimentation with AI, deploying it across credit decisioning, fraud detection, and everyday employee workflows at genuine scale. But how they get there differs sharply: some are building their own foundation models on proprietary data to avoid depending on external providers with their most sensitive information; others adopt commercial AI tools but wrap them in anonymization and strict governance before sensitive data reaches them; and some capital allocators are treating AI infrastructure itself — compute, data centres, foundation models — as a strategic asset worth direct ownership.
Regulation is catching up fast and unevenly, with major regimes now treating data location and third-party AI access as a risk to be supervised, not just a technology choice. Given the different AI strategies already in motion, what would it actually take to make them interoperable, auditable, and defensible across jurisdictions?
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