Hong Kong, 28 January 2026

• The Global AI Competitiveness Index (GAICI) Part 5: Analyzing AI Competitiveness from a Finance, Economy and Financial Services Perspective delivers a structured benchmark ranking 20 countries and 15 finance hubs using a pillar-based AI-for-Finance scoring framework.
• Finance-Grade AI Gap Widens: leaders scaling AI in risk, compliance, market operations.
• US #1, China #2: scale and implementation capacity dominate the top of the Index.
• Hubs Compete on the Flywheel: the City / Finance Hub Index highlights how capital markets + institutional adoption reinforce each other.
• Governance Readiness Is Now an Accelerator: clearer supervisory expectations reduce friction and shorten time-to-deployment.
• Infrastructure and Data Layers Decide Speed: interoperability, secure compute, and data foundations increasingly separate top-tier markets from the middle tier.
• GenAI Moves Into Regulated Workflows: competitiveness depends less on experimentation and more on controls, traceability, and resilience.

Deep Knowledge Group has released the Global AI Competitiveness Index (GAICI) Part 5, a benchmark analysing AI competitiveness from a finance, economy and financial services perspective, with the Financial Services Development Council (FSDC) serving as Observer,. The report combines a global landscape overview of AI adoption in finance with an indicator-based competitiveness index that ranks 20 countries and 15 city-level finance hubs on AI-for-Finance capability and maturity.

In Part 5, the country index is led by the United States (98.84) and China (83.41), followed by the United Kingdom (78.26) and Switzerland (73.09), with Singapore (69.12) next. The leaders are not defined by a single strength, but by multi-pillar performance that supports production-grade AI in finance—including deployment readiness, institutional capacity, and ecosystem breadth. The U.S. leads with large-scale capability across AI, capital markets, and financial services adoption. China ranks second on the strength of ecosystem scale and rapid implementation dynamics in AI-enabled financial services. The U.K. and Switzerland follow as high-performing financial centres where strong institutional environments and finance-grade expectations—governance, accountability, and risk discipline—support consistent AI adoption. Singapore rounds out the top tier, reflecting strong ecosystem coordination and high deployment readiness relative to its size.

“Leadership in AI is ultimately defined not by experimentation, but by execution. The jurisdictions that lead in the Global AI for Finance Index translate AI capability into trustworthy financial systems, grounded in governance, resilience, and market integrity as foundations of national strategy.” said Mr Dmitry Kaminskiy, General Partner of Deep Knowledge Group.

Meanwhile, city-hub ranking places New York (99) and London (81) first and second, with Hong Kong (76) third—reflecting their combined advantages in market connectivity, institutional concentration, and capital formation for AI-enabled financial activity. The next positions—San Francisco (70) and Shanghai (67)—reflect the interaction between AI capability and financial-market pull. Mid-table hubs (e.g., Toronto, Singapore, Tokyo, Chicago, Riyadh) typically show strengths in one or two dimensions but less complete end-to-end breadth. Lower-ranked hubs are often constrained by thinner ecosystem density, fewer scalable deployment pathways into regulated institutions, or weaker global market connectivity. Moving up the ranking generally requires (i) strengthening capital-formation and listing pathways, (ii) expanding production-grade adoption mechanisms across regulated institutions, and (iii) increasing ecosystem breadth so that AI capabilities translate into repeatable, auditable deployments rather than isolated pilots.

Dr King Au, Executive Director of the FSDC, remarked, “Hong Kong’s ranking among leading global finance hubs reflects the city’s excellent market connectivity and top-notch institutional quality—two conditions that matter when AI for finance must operate under finance-grade expectations.”

The report identifies several key insights regarding AI adoption in finance hubs:

• Capital markets are becoming a competitiveness lever for AI-for-finance: hubs with stronger listing ecosystems can amplify visibility and scaling.
• The rankings show a “finance–tech flywheel”: institutional adoption pulls in vendors and talent; market infrastructure helps convert momentum into growth.
• Country leaders win on depth + deployment: large ecosystems matter most when paired with proven production use across core finance functions.
• Risk and compliance are the decisive use-cases: the most scalable value sits in risk modelling, surveillance, and compliance automation.
• Infrastructure readiness is now strategic: secure compute, data availability, and cyber/operational resilience shape time-to-market.
• Where the opportunity sits: jurisdictions that harden enabling layers can climb fastest as AI adoption accelerates.

Dr Patrick Glauner, Professor of AI at Deggendorf Institute of Technology, a co-author of the report, noted, “In finance, competitive advantage comes from trustworthy AI—models that are explainable, auditable, and robust under real-world constraints. The index makes clear that deployment quality matters as much as innovation.”

Report Scope, Dataset and Evidence Base

Global AI Competitiveness Index (GAICI) Part 5: Analyzing AI Competitiveness from a  Finance, Economy and Financial Services Perspective is supported by a structured evidence base that maps AI-for-Finance companies, investors and ecosystem actors across multiple financial services verticals. This enables the report to connect qualitative narrative with measurable competitiveness signals and to present pillar-based scoring that can be compared across markets. In addition to the rankings, the report provides country-level profiles that summarise the AI-for-Finance landscape, competitiveness drivers, and the enabling mechanisms that most directly influence time-to-deployment in financial institutions.

The report provides a decision-maker-friendly reference for regulators, financial institutions, investors and ecosystem builders. It focuses on production-level AI adoption across the finance value chain while emphasizing the conditions that determine whether AI can scale safely and repeatably (auditability, resilience, privacy, accountability and operational risk controls). It views competitiveness through the lens of execution capacity: the combination of capital formation, governance readiness, talent and research depth, infrastructure and data foundations, and market maturity that together convert AI capability into measurable performance at scale.

Additional Key Findings

1. AI for finance is shifting from novelty to infrastructure: competitive advantage now reflects repeatable deployment in regulated workflows.
2. Top-ranked countries pair ecosystem scale with execution capacity: strong performance typically requires strength across multiple pillars, not one-off advantages.
3. The hub ranking underscores concentration: AI-for-finance activity clusters in a limited set of global financial centres with strong market infrastructure.
4. Model governance and assurance are central: monitoring, auditability, and operational resilience are becoming baseline expectations.
5. Data exchange and interoperability remain most common bottlenecks in mid-tier markets.
6. Strategic takeaway: the next phase of competition is about institutionalisation—turning tools into operating systems.

Access the full report here.