Deep Knowledge Group Launches Two Keystone AI-for-Finance Analytical Projects in Hong Kong at Financial Services Development Council Press Conference

Deep Knowledge Group General Partner Dmitry Kaminskiy, Hong Kong Financial Services Development Council (FSDC) Executive Director King Au, and GAICI Co-Author Patrick Glauner presenting Global AI Competitiveness Index Part 5 and AI for Finance in Hong Kong for Press at FSDC headquarters. 

In January 2026, Deep Knowledge Group (DKG) formally launched two major analytical initiatives in Hong Kong: Global AI Competitiveness Index (GAICI) – Part 5: AI in Finance, and the AI for Finance in Hong Kong industrial ecosystem report and interactive platform.

Deep Knowledge Group General Partner Dmitry Kaminskiy, Hong Kong Financial Services Development Council (FSDC) Executive Director King Au, GAICI Co-Author Patrick Glauner and assembled press and journalists at the Press Conference for Global AI Competitiveness Index Part 5 and AI for Finance in Hong Kong at FSDC headquarters. 

Both projects were unveiled live at a press conference hosted by the Hong Kong Financial Services Development Council (FSDC), which participated as formal Observer on both projects. The Hong Kong launch reflected the city’s role as a major international financial centre and convening point for global finance, regulation, and applied AI—but it is important to emphasise that the two projects serve distinct analytical purposes and operate at different levels of scope.

Deep Knowledge Group General Partner Dmitry Kaminskiy, FSDC Executive Director King Au and Global AI Competitiveness Index CO-Author Prof. Patrick Glauner were joined at the press conference by key stakeholders who supported the projects and prvided pre-launch commentary on their contents: Jean-Louis Tse (CEO, FinTech Association of Hong Kong), and Neil Tan (Founder & Chairman, Artificial Intelligence Association of Hong Kong, which served as a Supporting Partner of Deep Knowledge Group’s 2025 AI in Hong Kong report and platform.

From Left to Right: Neil Tan (Founder & Chairman, Artificial Intelligence Association of Hong Kong) Dmitry Kaminskiy (General Partner, Deep Knowledge Group), Patrick Glauner (GAICI Co-Author and Professor of Artificial Intelligence at Deggendorf Institute of Technology), Jean-Louis Tse (CEO, FinTech Association of Hong Kong) and King Au (Executive Director, Hong Kong Financial Services Development Council) at the Press Launch for Global AI Competitiveness Index Part 5 and AI for Finance in Hong Kong at FSDC headquarters in Hong Kong. 

GAICI Part 5 is a global, comparative, and methodologically neutral index, assessing AI competitiveness across countries and financial hubs worldwide. AI for Finance in Hong Kong is a jurisdiction-specific ecosystem study, providing granular, operational insight into how AI is being deployed across Hong Kong’s financial system.

Together, the two projects reflect DKG’s broader analytical agenda: to move beyond abstract discussions of AI potential and instead document where, how, and under what conditions AI is being deployed at production scale in real economic systems, addressing a central question shaping global finance today: where AI has moved beyond experimentation and into production-grade financial infrastructure—and why.

Global AI Competitiveness Index – Part 5: From Innovation to Financial Infrastructure

Global AI Competitiveness Index (GAICI) Part 5: Analyzing AI Competitiveness from a  Finance, Economy and Financial Services Perspective focuses on the transformation of artificial intelligence from an experimental technology into core financial infrastructure. Building on earlier editions of the Global AI Competitiveness Index, this volume introduces a dedicated AI-for-Finance competitiveness framework, covering both countries and global financial hubs.

www.dkv.global/ai-index/part5

Analytical scope and methodology

The index evaluates competitiveness across multiple dimensions that jointly determine whether AI can be deployed reliably and at scale in financial systems. These include:

• AI deployment maturity in banking, capital markets, insurance, payments, and compliance

• Capital-market infrastructure, including access to funding, public markets, and institutional investors

• Governance and regulatory readiness, focusing on auditability, model risk, data protection, and market integrity

• Ecosystem scale and density, covering AI companies, financial institutions, infrastructure providers, and enabling organisations

• International connectivity, reflecting cross-border capital flows, regulatory interoperability, and global market access

Scores are normalised to enable comparison across jurisdictions, allowing the index to distinguish between jurisdictions where AI remains largely experimental and those where it is already embedded in production-level financial workflows.

Key findings

• 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.

One of the central findings of GAICI Part 5 is that leadership in AI for finance is increasingly defined by deployment capability rather than research volume alone. Jurisdictions that combine institutional finance depth, regulatory clarity, and ecosystem integration consistently outperform those with strong research output but weaker pathways to implementation.

The index highlights the emergence of a small group of global AI-for-finance hubs, led by cities such as New York, London, and Hong Kong, alongside strong national-level performance by countries including the United States and China.

Crucially, the report demonstrates that AI competitiveness in finance is path-dependent: once AI systems reach production scale within regulated financial environments, network effects around data, compliance tooling, and institutional trust tend to reinforce leadership positions.

Governance and expert oversight

GAICI Part 5 is informed by a distinguished Index AI Committee, whose members include, among others: 

• Dame Jennifer Mary Shipley DNZM PC, 36th Prime Minister of New Zealand; 

• Dr. Rudolf Scharping, former Federal Minister of Defence of Germany and Chairman of RSBK AG;

• Prof. Dr. Rudolf Mellinghoff, former President of Germany’s Federal Fiscal Court, among others.

The committee provides oversight on governance, methodology, and interpretation, reinforcing the index’s role as a decision-maker-oriented analytical tool rather than a promotional ranking exercise.

Country-Level Findings: Why the United States and China Lead

• Top-ranking countries leaders share strong and balanced multi-pillar performance that supports production-grade AI in finance—including deployment readiness, institutional capacity, and ecosystem size. 

• 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. 

At the country level, the United States ranks first in GAICI Part 5. Its leadership is driven not by abstract AI capability alone, but by the depth of its capital markets, the scale of AI deployment within large financial institutions, and the ability to translate innovation into production through public markets and private capital formation.

The US financial system exhibits strong integration of AI across risk analytics, trading infrastructure, fraud detection, and compliance automation, supported by deep pools of capital and a dense ecosystem of vendors and infrastructure providers.

China ranks second, with particularly strong performance in AI application density, data availability, and scale of deployment within large financial institutions and platforms. Chinese financial institutions demonstrate rapid adoption of AI-driven decision systems, supported by domestic technology champions and large internal markets. While governance and international interoperability differ structurally from Western systems, China’s ability to deploy AI at scale positions it firmly among global leaders.

The United Kingdom ranks third, reflecting its role as a mature international financial centre with strong regulatory institutions and advanced AI adoption in banking, insurance, and capital markets. The UK’s performance is reinforced by London’s ecosystem depth and regulatory frameworks that support safe deployment in regulated environments.

A key insight from GAICI Part 5 is that no single factor determines leadership. Instead, competitiveness emerges from the interaction of capital markets, governance, institutional adoption, and ecosystem coordination.

City-Level Findings: Financial Hubs as AI Deployment Engines

• New York, London and Hong Kong occupy the top 3 positions, 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. 

At the city / financial-hub level, GAICI Part 5 highlights a small group of global centres where AI has reached system-level financial relevance.

New York ranks first, reflecting its unparalleled capital-market depth, concentration of global financial institutions, and integration of AI into trading, risk management, and market infrastructure.

London ranks second, supported by strong regulatory institutions, a dense fintech and AI ecosystem, and international financial connectivity.

Hong Kong ranks among the global leaders, distinguished by a combination of:

• international financial-market access

• institutional maturity

• proximity to Mainland Chinese technology ecosystems

• and a regulatory environment enabling production deployment

GAICI Part 5 underscores that cities, not countries alone, increasingly function as the primary units of AI competitiveness in finance, because deployment decisions, regulatory supervision, and market access are often concentrated at the hub level.

AI for Finance in Hong Kong: Operational Reality at System Scale

www.hk-ecosystem.tech/ai-in-finance

While the Global AI Competitiveness Index provides a global comparative lens, AI for Finance in Hong Kong delivers granular, operational insight into how AI is being deployed within a single financial system.

The report and accompanying platform map hundreds of entities across:

• banking and capital markets

• insurance and asset management

• payments, RegTech, and compliance automation

• financial-grade data, cloud, and AI infrastructure

Transition from pilots to production

A central finding is that Hong Kong’s financial institutions have largely moved beyond pilot experimentation. AI systems are now embedded in mission-critical workflows, including:

• credit assessment and risk modelling

• transaction monitoring and financial crime prevention

• onboarding, KYC, and compliance automation

• algorithmic trading and portfolio analytics

This transition is supported by Hong Kong’s institutional finance depth, regulatory clarity, and international market access, which together reduce friction between innovation and deployment.

Beyond these structural characteristics, Hong Kong’s uniqueness lies in the simultaneous maturity of these elements within a single, highly deployable environment. Few global cities combine advanced capital markets, deep pools of institutional finance, dense concentrations of technology and DeepTech capability, and real-world pathways for deployment at comparable scale and speed. This convergence allows innovation not only to be financed, but to be tested, validated, and implemented within operational systems that matter—financial, industrial, and societal—rather than remaining confined to research or pilot phases.

Ecosystem mapping as decision infrastructure

The interactive platform is designed not as a directory but as a decision-support tool, enabling regulators, financial institutions, investors, and ecosystem organisations to:

• benchmark maturity

• identify collaboration pathways

• assess ecosystem gaps

• support policy and market engagement

The platform provides a structured, repeatable view of the ecosystem, enabling regulators, financial institutions, investors, and technology providers to understand who is doing what, where, and at what level of maturity. Rather than a directory, it functions as a decision-support tool, supporting ecosystem engagement, benchmarking, and policy analysis.

Building on the Legacy of Other Recent Hong Kong-Launched Projects

The January 2026 launches build directly on DKG’s earlier work in Hong Kong. In 2025, DKG released Global AI Competitiveness Index Part 4 (with the FSDC as Observer) and the AI in Hong Kong report and platform (with the AI Industry Association of Hong Kong as SUpporting Partner).

Across these projects, a consistent analytical pattern emerges: Hong Kong functions as a high-signal environment for observing how advanced technologies transition into regulated, capital-intensive sectors. This continuity underscores why multiple DKG research initiatives have been launched in Hong Kong without implying that global indices themselves are jurisdiction-specific in intent.

Media and Institutional Reception

Both projects received substantial international media coverage, particularly among top-tier financial, technology, and policy outlets. Coverage highlights included South China Morning Post (SCMP), Arabian Business, The Star and others.

Media narratives focused on:

• the emergence of AI as financial infrastructure

• comparative positioning of major global financial hubs

• the role of governance and market connectivity in AI competitiveness

Press Coverage of Global AI Competitiveness Index Part 4 and AI in Hong Kong

Engagement data shows strong interest from government bodies, financial institutions, consultancies, academic institutions, and regulators, reinforcing the projects’ relevance beyond media visibility alone

Engagement at NTT Future Visions 2026

Deep Knowledge Group General Partner Dmitry Kaminskiy delivering the Opening Keynote for the FinTech & AI Session of the 2026 NTT Future Visions: Finance and Innovation Summit.

Shortly after the Hong Kong launches, DKG Founder and General Partner Dmitry Kaminskiy presented both GAICI Part 5 and AI for Finance in Hong Kong at NTT Future Visions: Finance and Innovation Summit 2026. His remarks focused on the structural shift underway in global finance, where AI is increasingly evaluated not as an innovation trend but as core operational infrastructure requiring governance, resilience, and international coordination.

The presentation positioned the two projects as complementary analytical tools supporting regulators, financial institutions, and policymakers navigating this transition.

Looking Ahead

Following these launches, Deep Knowledge Group continues to engage with like-minded institutions and partners in Hong Kong and internationally on future analytical initiatives. Multiple Hong-Kong-focused projects are currently in development, extending ecosystem mapping, competitiveness analysis, and sector-specific AI research across finance, technology, and adjacent domains.

Left Panel: InvestHK Director General Alpha Lau with Deep Knowledge Group General Partner Dmitry Kaminskiy. Right Panel: FSDC Executive Director King Au, DKG General Partner Dmitry Kaminskiy, Under Secretary for Financial Services and the Treasury of the Government of Hong Kong SAR Joseph H. L. Chan, JP, and GAICI Co-Author Patrick Glauner.

During his time in Hong Kong for the project launches, Deep Knowledge Group General Partner Dmitry Kaminskiy met with multiple ecosystem stakeholders spanning financial institutions, technology organisations, industry bodies, and policy-adjacent actors to discuss a range of current and prospective Deep Knowledge Group initiatives. These discussions focused on the practical realities of deploying advanced technologies within complex economic systems, the role of data-driven intelligence in supporting decision-making, and the importance of platforms that can translate innovation into measurable outcomes. While varied in scope and application, these conversations consistently reinforced Hong Kong’s role as a jurisdiction where advanced ideas can move rapidly from concept to execution.

As a result, Deep Knowledge Group treats Hong Kong as a strategic priority not on the basis of aspiration or branding, but because it functions as a tangible prototype of a future-facing city. Hong Kong uniquely bridges the core pillars that are likely to define national competitiveness and socioeconomic progress in the coming decades: artificial intelligence, DeepTech innovation, sophisticated financial infrastructure, and the capacity for real-world deployment. The coexistence of these pillars within a single, globally connected environment creates conditions that are difficult to replicate elsewhere and provides a natural setting for rigorous analysis, ecosystem-level intelligence, and scalable implementation.

In this sense, Hong Kong is not simply a beneficiary of technological and financial change, but an active system in which these forces interact continuously. The city’s ability to align capital formation with technological capability and operational deployment positions it as a living laboratory for how advanced economies may evolve—where innovation is not only created and funded, but embedded into the infrastructures that shape markets, industries, and everyday economic activity.

As with previous editions of the Global AI Competitiveness Index and regional ecosystem studies, DKG’s approach remains grounded in evidence-based analysis, methodological transparency, and close engagement with institutional stakeholders. The January 2026 Hong Kong launches represent not an endpoint, but a continuation of a multi-year research agenda aimed at understanding how advanced technologies are reshaping real economic systems—at scale, under regulation, and across borders.

These projects reflect DKG’s long-term commitment to Hong Kong and out continued collaboration with local institutions, industry associations, and ecosystem leaders. We are already actively working on next-stage projects in Hong Kong, expanding beyond AI-for-Finance into broader technology and industrial ecosystem platforms in close cooperation with our partners.