The increasing use of artificial intelligence in financial systems is driving a shift towards machine-executed transactions, posing new challenges for the industry in terms of control, oversight, and infrastructure, according to executives from Microsoft and Chainalysis. Bill Borden, Microsoft's corporate vice president of worldwide financial services, noted that legacy systems will face growing pressure as transaction demands become more complex, ultimately reaching a tipping point where latency, scale, and complexity impact competitiveness.
This forces firms to reevaluate their system architecture. The focus has now shifted from capability to trust, with the question being whether automated systems 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 demonstrate what controlled a system and whether it followed policy when decisions are made without human input.
Jonathan Levin, co-founder and CEO of Chainalysis, pointed to the crypto sector as a model for automated finance, where blockchain networks process large volumes of transactions through smart contracts and software-driven wallets. This experience also extends to risk management, with efforts to track illicit funds across multiple wallets serving as an example of the kind of monitoring needed in systems with large-scale, automated transactions. Looking ahead, both executives expect a mix of systems to coexist, with Levin predicting that most commerce will be settled on public infrastructure in the next decade, while Borden envisions a more integrated approach linking public blockchains, private networks, and existing systems.