AI in asset management operations moves beyond fixing errors faster
“Imagine identifying and correcting a settlement problem before sending the trade details out the door, or giving fund professionals a clearer picture of emerging risks while there’s still time to act ” ICI’s Andrew Kayiira, Director, Technology & Financial Innovation and R.J. Rondini, Senior Director, Securities Operations, wrote in the viewpoint accompanying the paper.
From disconnected systems to connected intelligence
The core argument in the ICI framework is that technology has helped firms manage complexity but hasn’t fully solved the problem. Operational teams still spend significant time piecing together data from siloed systems – a challenge that grows more acute as firms launch more products, extend into new distribution channels, and face tightening settlement windows under T+1 rules.
The paper introduces what ICI calls an Intelligent Operating Model, built around three objectives: preventing recurring operational errors before they occur, maintaining a broader view of activity across the business in real time, and surfacing potential risks early enough for employees to act rather than react.
Consider a straightforward example cited in the paper: a large shareholder redemption. The liquidity decisions that follow can depend on information held across trading and shareholder-servicing teams – information that, under current models, staff must assemble manually as events unfold.
An AI-enabled system, the ICI argues, could bring that picture together automatically, giving decision-makers context in the moment rather than after the fact.
