AI, Data, and the New Architecture of Financial Markets

Signals to Systems

The integration of AI with systematic execution creates tighter feedback loops between research, decisioning, and trading. Institutions are redesigning research pipelines to capture differentiated data, apply adaptive models, and distribute insights into execution desks in near real-time.

Implications

• Faster discovery of microstructure edges drives demand for governance around model deployment.
• Risk normalisation must account for automation across venues, assets, and counterparties.
• Data lineage and validation become competitive moats as regulatory standards evolve.

Our advisory work focuses on aligning data, model oversight, and execution practices so that institutions can scale AI responsibly across market functions.

Educational use only; not investment advice.