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 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, with the tipping point occurring when latency, scale, and complexity begin to impact a company's ability to compete. This forces firms to reassess how their systems are built.
While automation has long been a part of finance, Borden noted that the focus is now shifting from capability to trust, with the question being whether one can trust and audit automated systems. Microsoft is developing tools to manage this transition, including systems that assign identities and permissions to AI agents and track their actions.
Jonathan Levin, co-founder and CEO of Chainalysis, pointed out 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. Levin stated that the experience gained in the crypto sector extends to risk management, citing efforts to track illicit funds across thousands of different wallets as an example of the kind of monitoring needed in a system where transactions occur 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 in 10 years, while Borden foreseeing a more integrated approach that links public blockchains, private networks, and existing rails.