The increasing use of artificial intelligence in financial systems is driving a shift towards a model where machines execute transactions on a large scale, creating new challenges related to control, oversight, 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 growing pressure as transaction demands become more complex. The turning point occurs when factors such as latency, scale, and complexity begin to impact 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 a part of 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 address this transition, Microsoft is developing tools to manage the shift, 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 system and whether it followed policy when decisions are made without direct human input.
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 expect a mix of systems to coexist. Levin predicted that the majority of commerce will be settled on public infrastructure in the next decade, while Borden envisioned a more integrated approach linking public blockchains, private networks, and existing systems.