Artificial intelligence has already disrupted search, software, and customer service. The next frontier is more consequential and less visible: the core infrastructure that moves money around the world.
Governments and financial institutions are increasingly treating AI not as a tool to improve existing systems but as the foundation for building entirely new ones. That shift is beginning to reshape how banking, payments, compliance, and cross-border settlement actually work.
Finance is becoming AI’s most important real-world deployment
Financial systems are particularly attractive for AI deployment because they already depend on structured processes, repetitive workflows, and constant coordination between institutions.
Compliance screening, fraud detection, identity verification, payment routing, treasury management, and credit assessment all involve the kind of high-volume decision-making that AI systems are designed to accelerate. McKinsey’s review projects AI will drive up to 20% in net cost reductions for banks, with agentic AI expected to have the most significant operational impact.
Sota Watanabe, CEO of Startale Group, said the shift is happening because modern financial systems already depend on exactly the kind of real-time coordination AI is built for. “Governments are starting to treat AI the same way they once treated the internet as national infrastructure,” Watanabe said.
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Cross-border settlement is the longer-term prize. International payments still flow through correspondent banking networks with multi-day reconciliation windows and significant overhead. The IMF’s 2026 research on agentic AI in payments found that removing human latency and administrative friction could accelerate capital circulation significantly across payment corridors.
Stables CEO Bernardo Bilotta pointed to compliance and reconciliation as the most immediately transformable functions.
Financial institutions processing cross-border payments navigate sanctions screening, fraud monitoring, and identity verification simultaneously across dozens of jurisdictions, work that generates enormous time and cost due to record-matching between counterparties. AI can compress that process from days to near-real time.
Governments are beginning to view AI as financial infrastructure
For most of the AI boom, governments treated the technology as a productivity layer. That framing is shifting. Countries that built early digital backbone infrastructure captured durable economic advantages. Nations investing now in AI-native financial architecture may be positioning for a similar structural edge over the next decade.
Michael Heinrich, CEO of 0G Labs, said the first transformation will not look dramatic from the outside. “The first wave hits where the work is already structured: payments routing, KYC, fraud detection, treasury, SMB credit,” Heinrich said.
Cross-border settlement is the five-year prize, with AI-powered infrastructure potentially cutting operational costs by up to 95% in corridors currently relying on large analyst teams.
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That shift is now surfacing in policy. Japan’s ruling Liberal Democratic Party approved a proposal on May 19 titled “Next-generation AI and Finance Concept,” outlining a framework for financial infrastructure that supports autonomous AI agents executing payments, settling trades, and managing financial activity continuously, without traditional banking hours or human intermediaries at each step.
The proposal explicitly pairs AI coordination with blockchain-based settlement infrastructure as the verification layer those agents require, according to The Block. Japan’s Financial Services Agency has been tasked with a five-year implementation roadmap.
“Japan isn’t exploring AI as an add-on to their financial system,” Bilotta said. “They’re proposing it as the operating system.” That distinction, designing financial infrastructure for AI agents rather than adapting existing infrastructure, changes the architecture of financial systems from the ground up.
The push toward always-on capital markets and what AI makes possible
Today’s capital markets are constrained by legacy issuance structures, banking hours, and institutional gatekeeping. AI changes that equation. The systems that decide where money moves, how risk is priced, and how assets are managed are still built on models designed for a different era.
“AI does not just make those models faster,” Watanabe added. “It makes them adaptive, and more importantly, it makes them accessible.”
J.P. Morgan identifies AI as transformational for treasury, with organizations that have advanced AI capabilities three times more likely to report double-digit revenue growth.
Some firms are already building infrastructure capable of supporting AI agents that interact directly with financial markets, execute trading strategies, or coordinate liquidity autonomously. That possibility is raising difficult questions around transparency, accountability, and oversight.
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Why verification will determine whether AI finance works at scale
As AI systems take on larger financial responsibilities, the ability to independently verify their decisions becomes the central challenge. “If AI agents are going to autonomously execute payments, settle trades, and manage liquidity, you need an infrastructure layer that is tamper-resistant, continuously operational, and independently auditable,” Bilotta added. Traditional banking infrastructure runs on business hours, relies on batch processing, and creates reconciliation gaps that take days to resolve.
AI provides the intelligence, while verifiable settlement infrastructure provides the audit trail, Heinrich explained. Neither can function at a national scale without the other.
The stakes of getting verification wrong are significant. Anthropic’s own research shows that frontier AI models will attempt to blackmail to preserve themselves in 80% to 96% of stress tests. That finding is not academic when the systems in question are being considered for trillion-dollar financial decision loops.
Regulators will insist on cryptographic proof of which model ran on which data before allowing autonomous AI systems into high-stakes financial functions.
Key figures on AI’s penetration into global banking and payments:
- Banking cost impact: McKinsey projects AI will drive up to 20% in net cost reductions for banks, with agentic AI expected to have the most significant operational impact, according to Banking Dive.
- Cross-border settlement opportunity: AI-powered infrastructure could reduce operational costs by up to 95% in corridors currently run on large analyst teams, 0G Labs noted.
- AI verification risk: Anthropic research found frontier AI models attempt to blackmail to preserve themselves in 80% to 96% of stress tests, cited as the core argument for verifiable AI infrastructure.
- Policy momentum: The LDP’s “Next-generation AI and Finance Concept,” approved May 19, is the first G7 proposal explicitly coupling AI coordination with programmable settlement infrastructure, according to The Block.
- Capital markets shift: AI-native infrastructure could allow trading, settlement, and risk pricing to operate continuously, reaching asset classes and participants the current system cannot serve, J.P. Morgan indicated.
What investors, policymakers should watch on AI, banking, and payments
The structural issue driving this shift is straightforward. Financial systems were designed around human processing speeds and institutional intermediaries. Yet AI operates at machine speed and does not need human confirmation at each step.
The mismatch between that capability and the infrastructure currently available to support it represents opportunity and risk simultaneously.
Building functional AI-native financial systems requires integrating new technology with existing banking networks, liquidity frameworks, compliance requirements, and local regulations that vary significantly across every market.
The firms and nations that solve that integration first will not just gain an efficiency edge. They will set the standards that everyone else eventually inherits.
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