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, reaching a tipping point when latency, scale, and complexity start to impact a company's ability to compete. This forces firms to reconsider 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 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, pointed out 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 noted that this experience 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 happen 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 in 10 years will be settled on public infrastructure, while Borden envisioning a more integrated approach that links public blockchains, private networks, and existing rails.