Post-trade is the place for agentic AI workflows

§ 01 Executive Snapshot

• What: Agentic AI is poised to revolutionize post-trade operations.
• Who: Smartstream and Yogesh Shenai are key players in this transformation.
• Why it matters: The integration of agentic AI is critical for enabling faster T+1 settlement processes in financial markets.

§ 02 Key Developments

• Smartstream is focusing on the application of agentic AI to streamline post-trade workflows.
• The global shift to T+1 settlement is a significant driver for enhancing operational efficiency in trading environments.
• Agentic AI can automate various tasks within post-trade operations, reducing manual intervention and errors.

§ 03 Strategic Context

• The post-trade landscape has been evolving towards faster settlement cycles, with T+1 becoming an industry standard.
• The implementation of agentic AI aligns with broader trends in financial technology aimed at increasing efficiency and reducing costs in trading operations.

§ 04 Strategic Implications

• Immediate implications include enhanced operational efficiency and reduced settlement times, which can improve liquidity in markets.
• Long-term implications may involve a shift in workforce requirements as automation takes over repetitive tasks, leading to a need for reskilling in the finance sector.

§ 05 Risks & Constraints

• Potential regulatory challenges may arise as firms implement AI-driven solutions in financial operations.
• Technical hurdles such as integration with existing systems and ensuring data security could impede the deployment of agentic AI.

§ 06 Watchlist / Forward Signals

• Monitoring the timeline for T+1 settlement implementation across different markets will be crucial.
• Future developments in AI technology and regulations will signal the extent of agentic AI adoption in post-trade operations.