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Dr. Zhao Jingyuan
Head, Group Data Office
OCBC
View LinkedIn profileDr. Zhao Jingyuan is Head of Group Data Office at OCBC, where she drives business value through Data & AI, strengthens data-driven decision-making, and builds enterprise data & Intelligence foundations for long-term success.
With 20 years of experience across Great Eastern, Capgemini, Lazada (Alibaba Group), and A*STAR, she brings deep expertise in AI, big data, and data monetisation. Previously, as Managing Director at Great Eastern, she shaped the Group’s Data & AI strategy and led high-impact solutions.
Jingyuan holds a Ph.D. in Statistics from NUS, is an adjunct professor, and is recognised among Singapore 100 Women in Tech (2021) and recipient of the NUS Outstanding Science Alumni Award (2024) in recognition of her contributions to the Technology and Science sector.
“Data and AI unlock new value and reimagine what’s possible.”
Speaking At
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9:45–11:15amMon 16 NovPublic-Private RoundtableRoundtable Room 2Invite-Only
Accountability & Liability In Agentic Systems
Build, Buy, or Own: Strategies for Responsible AI Adoption in Finance
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Amith RajanExecutive Vice President, Head of Wholesale Digital Banking, Mashreq & Chief Executive Officer, NeoVenturesMashreq NeoVentures
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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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Dr. Zhao JingyuanHead, Group Data OfficeOCBC
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Prof. Heng WangProfessorSingapore Management UniversityModerator
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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