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 growing pressure as transaction demands become more complex, with the breaking point occurring when latency, scale, and complexity begin to impact a company's ability to compete. He emphasized the need for firms to rethink how their systems are built.
The focus is now shifting from capability to trust, with Borden noting that while technology can automate tasks, the question is whether it can be trusted, audited, and controlled. 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 the 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, with blockchain networks processing large volumes of transactions through smart contracts and software-driven wallets.
He noted that the experience extends to risk management, with efforts to track illicit funds across thousands of wallets as an example of the kind of monitoring needed in a system where transactions happen at scale without direct human input. Both executives expect a mix of systems to coexist in the future, with Levin predicting that the majority of commerce will be settled on public infrastructure and Borden envisioning a more integrated approach linking public blockchains, private networks, and existing rails.