The increasing use of artificial intelligence in financial systems is driving the industry towards a model where machines handle transactions on a large scale, creating 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 the growing complexity of transaction demands.
The turning point will be reached when the limitations of these systems, such as latency, scale, and complexity, start to impact their ability to compete. As a result, firms will need to reassess how their systems are built. The focus is now shifting from the capability of technology to automate tasks, such as executing hedging strategies, to the issue of trust. The key question is whether these automated systems can be trusted, audited, and controlled.
To address this challenge, 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, firms must be able to demonstrate what controlled a particular action and whether the system followed the relevant policy when decisions are made without direct 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 can be applied to risk management, such as tracking illicit funds across multiple wallets. Looking ahead, both executives expect a mix of systems to coexist, with traditional rails continuing to exist and software acting as the layer that connects them.