The increasing use of artificial intelligence in financial systems is driving the industry towards a model where machines execute transactions on a large scale, posing new challenges related to control, oversight, and infrastructure. According to Microsoft and Chainalysis executives, this shift is putting pressure on legacy systems, which will struggle to keep up with growing transaction demands and complexity.
The turning point will be reached when latency, scale, and complexity begin to impact a company's ability to compete, forcing firms to reevaluate their systems. While automation has long been present in finance, the focus is now shifting from capability to trust, with the question being whether technology can be trusted to execute tasks such as hedging strategies without human intervention. To address this, Microsoft is developing tools to manage the transition, including systems that assign identities and permissions to AI agents and track their actions.
In regulated environments, companies must be able to demonstrate what controlled a system and whether it followed policy when decisions are made without human input. The crypto sector is already providing a working model of automated finance, with blockchain networks processing large volumes of transactions through smart contracts and software-driven wallets. This experience extends to risk management, with efforts to track illicit funds across multiple wallets serving as an example of the kind of monitoring needed in a system where transactions occur at scale without human input.
Looking ahead, executives expect a mix of systems to coexist, with the majority of commerce potentially being settled on public infrastructure in the future.