Governed autonomy for CSDs : A dual-layer AI framework built on an event-driven foundation
Abstract
Global securities settlement faces ongoing friction from settlement fails, costing billions annually. While artificial intelligence (AI) promises a solution, grafting it onto legacy batch systems is pointless; AI cannot forecast effectively what it cannot see in real time. Drawing on operational practice with several central securities depositories (CSDs), this paper presents a foundation for meaningful modernisation. The paper suggests that shifting to an event-driven, in-memory architecture is the non-negotiable prerequisite for effective AI advancement. This foundation provides a dual-layer exception-handling approach where deterministic rules handle routine cases and a controlled AI layer tackles complex patterns. Through anonymised case studies, the paper quantifies achievable advantages, such as large reductions in settlement fails and resolution times and identification of hard limits tied to data quality. The framework includes governance mechanisms separating rule-based and AI-driven oversight, aligns with standards such as the National Institute of Standards and Technology (NIST) AI Risk Management Framework, and presents an 18–30 month risk-aware roadmap. This approach is described as augmented autonomy: a strategy centred on amplifying practitioner expertise, restricting automated actions with human oversight, and maintaining full audit trails to guide CSDs, exchanges, and regulators in deploying AI without sacrificing resilience or control. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
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Author's Biography
Bulat Nizamov is a seasoned capital markets professional with over 20 years’ experience architecting and implementing mission-critical financial infrastructure across four continents. In his current role, he leads market infrastructure product development, driving innovation while ensuring regulatory soundness and systemic stability. Alongside this role, he also consults independently, advising central banks, ministries of finance, and exchanges on modernisation programmes from feasibility studies through to post-implementation review. Bulat has led transformative projects in more than 20 countries including Indonesia, Chile, Jordan, Nigeria, Pakistan, and across Central Asia and Latin America. His leadership was instrumental in the global deployment of TRAD/X and DEPO/X v3, advanced trading and post-trade platforms with robust collateral risk management frameworks. A recurring theme in Bulat’s work is modernising legacy settlement architectures. He has deep experience guiding central securities depositories (CSDs) from batch-based to event-driven, in-memory processing foundations, as a non-negotiable prerequisite for effective artificial intelligence integration. Bulat is an active member of the International Securities Services Association. Earlier in his career, as a trader, he gained frontline experience. He holds degrees in economics and mathematics.
