The increasing use of artificial intelligence in financial systems is driving a shift towards a model where machines handle transactions on a massive 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 mounting pressure as transaction demands become more complex. The turning point occurs when factors such as latency, scale, and complexity start to impede a company's ability to compete, forcing firms to reassess their system architecture, he said at an event in New York City. While automation has long been a part of the finance sector, Borden noted that the focus is now shifting from capability to trust.
The question is no longer about whether technology can automate tasks like executing a hedging strategy, 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, companies must be able to demonstrate what controlled a system and whether it adhered to policy when decisions are made without human input, Borden said. Jonathan Levin, co-founder and CEO of Chainalysis, noted that the crypto sector already provides a working model of automated finance, with blockchain networks processing large volumes of transactions through smart contracts and software-driven wallets, creating an environment similar to agent-based systems.
Levin pointed to efforts to track illicit funds across thousands of wallets as an example of the kind of monitoring required in a system where transactions occur at scale without human input. Looking ahead, both executives expect a mix of systems to coexist, with Levin predicting that most commerce will be settled on public infrastructure in 10 years, while Borden envisions a more integrated approach linking public blockchains, private networks, and existing systems.