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Nilanshuk Haldar
Vice President – Global Head of Partnerships & Alliances
Nucleus Software
Based in Singapore, Nilanshuk leads Nucleus's commercial business in Southeast Asia and is also responsible for Partnerships globally. He was a corporate and investment banker at Deutsche Bank, JP Morgan, and Kotak Bank before moving to (Fin)tech via a segue as a founding member of a wealthtech startup. Nilanshuk has led Financial Services Partnerships for APAC at AWS and at digital banking ISV Backbase before taking up his current role at Nucleus.
Speaking At
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9:45–11:15amMon 16 NovPublic-Private RoundtableRoundtable Room 2Open
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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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
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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