Insider Brief
- Artificial intelligence in finance has shifted from experimentation to infrastructure, with competitive advantage consolidating in jurisdictions and cities that can deploy AI at scale within regulated, production-level workflows.
- The United States leads not through superior innovation alone but through institutional depth that converts AI into operational systems, while China advances faster under a coordinated governance model that differs fundamentally from Western financial frameworks.
- AI-finance leadership is increasingly concentrated in a small number of global hubs — notably New York, London, and Hong Kong — with Gulf centers such as Riyadh and Dubai gaining ground through faster institutional execution while many mid-tier hubs struggle to translate talent into sustained advantage.
Artificial intelligence has moved from experimentation to infrastructure in global finance and the competitive advantage is consolidating around jurisdictions and cities that can deploy AI inside regulated systems at scale rather than those that merely innovate fastest, according to a new study by Deep Knowledge Group.
The report, The Institutionalization of AI in Finance: Insights from the Global AI Competitiveness Index Part 5, analyzes more than 50 countries, thousands of institutions and roughly 8,800 AI-driven entities operating in financial services. Its central finding is that leadership in AI finance now reflects institutional depth — the ability to integrate AI into risk, compliance and operational workflows under regulatory supervision — rather than technological novelty alone.
That shift is reshaping global competition, according to the executive summary of the report. The gap is no longer between financial systems that use AI and those that do not, but between those that can run AI repeatedly in production environments and those still confined to pilot projects. As a result, advantage is compounding in a small number of countries and, more decisively, in a limited set of global financial hubs.
Institutional Depth Over Technical Prowess
The study places the United States at the top of its country-level rankings, but not because American institutions lead every category of AI innovation. Instead, the report attributes U.S. leadership to the breadth and maturity of its financial infrastructure, capital markets, regulatory systems and institutional adoption.
According to the analysis, U.S. financial institutions have been more successful than peers at moving AI into production environments across risk modeling, regulatory compliance, market surveillance and operational automation. These deployments generate proprietary data, embed AI into governance processes and create organizational capabilities that are difficult to replicate quickly.
The report characterizes this advantage as institutional rather than technical. The United States does not consistently outperform all competitors in raw model development or speed of experimentation. Its strength lies in its ability to translate innovation into operational systems that meet regulatory expectations and can be scaled across large, complex organizations.
The study finds that’s important because competitive advantage increasingly grows in systems that can be institutionalized. Once AI becomes embedded in core workflows, it produces feedback loops that reinforce leadership, including data accumulation, workforce specialization and higher switching costs for both institutions and vendors.
China ranks second overall, but the report indicates that its path to competitiveness follows a different model. China demonstrates high implementation velocity and ecosystem scale, with AI deployed widely across payments, lending and risk management. That speed reflects coordinated development between financial institutions, technology platforms and state authorities.
The study notes, however, that China’s governance structure differs substantially from those in Western financial systems. While coordination enables faster deployment, it operates within a framework that global banks and regulators outside China cannot easily replicate. As a result, the report treats the U.S. and Chinese models as parallel rather than convergent, reaching similar operational goals through distinct institutional rulebooks.
AI Finance Is Becoming a City-Level Competition
While national policy still matters, the report finds that AI-finance capability is concentrating more sharply at the city level. Capital formation, institutional buyers, regulators and technology vendors increasingly cluster in a handful of global financial hubs, creating what the study describes as self-reinforcing competitive dynamics.
At the top of the city-level rankings are New York, London and Hong Kong. Each combines deep capital markets, dense financial institutions, established regulatory frameworks and a growing concentration of AI vendors serving regulated clients.
The analysts suggest that these cities benefit from a “finance-tech flywheel.” Institutional adoption creates demand for specialized AI providers. Ecosystem density attracts talent and capital. Market infrastructure supports scaling and capital formation. Success then reinforces the city’s position, drawing in additional participants.
This concentration has implications for jurisdictions outside the top tier. According to the report, many mid-tier financial centers possess strong universities, technical talent and research capacity but lack the capital-market intensity and institutional adoption needed to trigger the flywheel. Without sufficient density of buyers, investors and governance-ready infrastructure, these hubs struggle to convert technical capability into sustained leadership.
The result is a growing divide not only between countries, but between cities. AI finance is increasingly a city-level game, according to the report.
Fast Movers, Stalled Centers and the Cost of Delay
The study specifically calls out the rapid ascent of Gulf financial centers. Cities such as Riyadh and Dubai rank below established hubs in ecosystem scale but outperform many peers in development velocity. The report attributes this to focused government support, targeted regulatory frameworks and efforts to attract international talent and technology firms.
These cities have adopted hub-based strategies, concentrating resources rather than dispersing them nationally. According to the analysts, this approach allows them to compress development timelines and move more quickly into production-level AI deployment within financial services.
By contrast, the report identifies cities such as Mumbai, Paris and Toronto as examples of mid-tier hubs facing structural constraints. Despite strong research institutions and skilled workforces, these centers often lack sufficient capital-market depth or institutional coordination to catalyze large-scale adoption. Talent alone, the study finds, does not generate leadership without sustained institutional demand and market infrastructure.
Across all regions, the report warns that delay is becoming increasingly costly. Because AI systems embedded in finance generate compounding advantages, late adopters face steeper barriers to entry over time. Institutions and jurisdictions that remain in pilot phases risk falling into a second tier that is difficult to escape.
The study frames this moment as the “institutionalization phase” of AI in finance. The next stage of competition will be shaped less by novel algorithms and more by the ability to standardize deployment, integrate governance from inception and operate AI as part of the financial system’s core infrastructure.
For policymakers, the report suggests that regulatory clarity, infrastructure investment and hub-focused strategies now matter more than symbolic innovation initiatives. For financial institutions, it argues that governance-ready deployment — particularly in risk management and compliance — represents the most durable source of advantage.
For more insights, check out Deep Knowledge’s report on AI in finance here.
