The increasing use of artificial intelligence in financial systems is driving a shift 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. 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 latency, scale, and complexity start to impact a company's ability to compete, forcing firms to reassess how their systems are built. While automation has long been a part of finance, Borden emphasized that the focus is now shifting from capability to trust, with the question being whether technology can be trusted and audited. Microsoft is developing tools to manage this 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 system and whether it followed policy when decisions are made without direct human input.
Jonathan Levin, co-founder and CEO of Chainalysis, noted that the crypto sector already offers a working model of automated finance, with blockchain networks processing large volumes of transactions through smart contracts and software-driven wallets. Levin pointed to efforts to track illicit funds across thousands of wallets as an example of the kind of monitoring needed in a system where transactions occur at scale without direct human input. Looking ahead, both executives expect a mix of systems to coexist, with Levin predicting that the majority of commerce will be settled on public infrastructure in 10 years, while Borden envisioning a more integrated approach linking public blockchains, private networks, and existing rails.