The increasing use of artificial intelligence in financial systems is driving a shift towards a model where machines perform transactions on a large scale, posing new challenges related to oversight, control, and infrastructure, according to executives from Microsoft and Chainalysis. Bill Borden, Microsoft's corporate vice president of worldwide financial services, stated that legacy systems will face mounting pressure as transaction demands become more intricate. The breaking point occurs when latency, scale, and complexity start to hinder a company's ability to compete, forcing firms to reassess their systems, he said at an event in New York City. While automation has long been present in finance, Borden noted that the focus is now shifting from capability to trust.
The question is no longer about whether technology can automate tasks, but rather whether it can be trusted and audited. To manage this transition, Microsoft is developing tools that assign identities and permissions to AI agents and track their actions. In regulated environments, firms must demonstrate what controlled a particular action and whether the system adhered to the policy when decisions are made without direct human input, Borden said. Jonathan Levin, co-founder and CEO of Chainalysis, pointed to the crypto sector as a working model of automated finance.
Blockchain networks process large volumes of transactions through smart contracts and software-driven wallets, creating an environment similar to agent-based systems. Looking ahead, both executives anticipate a mix of systems to coexist. Levin expects the majority of commerce to be settled on public infrastructure in the next decade, while Borden envisions a more integrated approach linking public blockchains, private networks, and existing systems.