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

AI as Financial Infrastructure, Deployment Readiness, and the Next Phase of Global Competitiveness

In early 2026, Deep Knowledge Group (DKG) delivered the Opening Keynote for the FinTech and AI session of NTT Future Visions: Finance & Innovations Summit 2026, a high-level forum convening senior leaders from finance, technology, infrastructure, and policy to examine how emerging technologies are transitioning from experimentation into system-level economic infrastructure.

The summit provided a setting for substantive, technical discussion rather than promotional narratives. Against this backdrop, Dmitry Kaminskiy, General Partner of Deep Knowledge Group, delivered a presentation focused on AI deployment readiness, ecosystem maturity, and the structural conditions required for artificial intelligence to operate as financial and economic infrastructure at scale.

During the presentation, Dmitry showcased and discussed findings from two of DKG’s most recent analytical initiatives:

• Global AI Competitiveness Index (GAICI) – Part 5: AI in Finance

• AI for Finance in Hong Kong Industrial Ecosystem Report and Platform

These projects were presented not as advocacy tools, but as evidence-based analytical frameworks designed to help decision-makers understand where AI is genuinely operational, scalable, and governable within financial systems.

From Innovation to Infrastructure: The Core Theme of the Presentation

A central theme of Dmitry Kaminskiy’s presentation was that AI is no longer best understood as a discrete innovation category. Instead, in leading jurisdictions, it is becoming embedded financial infrastructure, comparable in importance to payment rails, market data systems, and clearing and settlement mechanisms.

This shift reframes competitiveness. Leadership in AI is no longer defined primarily by research output or startup density, but by:

• Production-grade deployment across core financial workflows

• Integration with capital markets and regulated institutions

• Data, compute, and governance architectures that enable scale without fragility

These themes formed the analytical backbone of both projects discussed at the summit.

Global AI Competitiveness Index (Part 5): Finance as the Lens

The Global AI Competitiveness Index (GAICI) – Part 5 extends DKG’s established index framework by focusing specifically on AI in finance, economy, and financial services. Unlike prior editions that emphasized general AI capacity, Part 5 examines how AI functions when subjected to the constraints of financial regulation, systemic risk, data sensitivity, and capital-market discipline.

www.dkv.global/ai-index/part5

During the presentation, Dmitry highlighted that the index:

• Ranks countries and financial hubs based on deployment maturity, not theoretical capability

• Assesses AI as part of financial system architecture, rather than as standalone technology

• Uses multi-pillar scoring across governance readiness, market infrastructure, ecosystem depth, capital formation, and connectivity

Key findings discussed at the summit included:

• Clear differentiation between jurisdictions with strong AI research and those with finance-grade AI deployment

• The emergence of a small group of countries and cities capable of scaling AI across banking, capital markets, insurance, and compliance functions

• The increasing importance of market connectivity and cross-border operability as competitive variables

The report 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.

AI for Finance in Hong Kong: A System-Level Ecosystem View

Complementing the global index, Dmitry also discussed AI for Finance in Hong Kong, a project developed to provide a granular, system-level view of how AI is actually being deployed across one of the world’s most internationally connected financial centres.

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

Rather than focusing on isolated use cases, the project maps:

• Financial institutions

• AI solution providers

• Infrastructure operators

• Data and governance enablers

• Capital and ecosystem intermediaries

The report and platform document Hong Kong’s progression from pilot-stage experimentation toward production deployment across banking, capital markets, insurance, RegTech, and risk management.

At the summit, Dmitry emphasized that the value of this work lies in its operational grounding:

• It tracks real deployments rather than stated intentions

• It situates AI adoption within Hong Kong’s broader capital-market architecture

It highlights interoperability between global and Mainland Chinese technology ecosystems without advocacy

The platform was presented as a decision-support tool for regulators, institutions, and ecosystem leaders seeking to understand AI at scale, rather than as a promotional catalogue.

Japan-Tech and Asia-Tech: Comparative Ecosystem Lenses

In addition to the two flagship projects, Dmitry briefly discussed two Asia-focused industrial ecosystem mapping and analytics projects that it wil be lauching in 2026 following its selection of key regional stakeholder supporting and obverser organizations: Deep Knowledge Group’s Japan-Tech and Tech Ecosystem in Asia interactive platforms. These projects were introduced as comparative analytical lenses, illustrating how different regions structure technology ecosystems around finance, industry, and infrastructure.

The Japan-Tech platform was discussed in the context of:

• Long-term industrial planning

• Infrastructure-centric innovation models

• Deep integration between corporates, research institutions, and state-linked actors

Tech Ecosystem in Asia, by contrast, was referenced as a macro-regional view capturing:

• Cross-border capital and technology flows

• Heterogeneous regulatory environments

• Varying stages of AI deployment maturity across Asian economies

These references were used to underscore a broader analytical point: there is no single path to AI competitiveness, but deployment success consistently correlates with infrastructure depth, governance clarity, and capital-market integration.

NTT as a Key AI for Finance Infrastructure Enabler

Within this framework, NTT was discussed as a critical infrastructure context rather than the focal subject. As one of the world’s largest telecommunications and technology groups, and a major operator of enterprise-grade data centres and networks in Hong Kong and across Asia, NTT represents the type of foundational infrastructure layer upon which finance-grade AI systems depend.

At the summit, NTT’s relevance was framed in terms of:

• High-availability, low-latency environments required for regulated AI workloads

• Secure hosting of sensitive financial data and models

• Cross-border network connectivity enabling distributed AI deployment

This positioning aligned with the broader thesis of the presentation: AI competitiveness increasingly depends on infrastructure that sits beneath algorithms and applications, and actors like NTT form part of that invisible but decisive layer.

Hong Kong as a Forum for Serious AI–Finance Dialogue

While neither project was framed as a promotional exercise for Hong Kong, Dmitry noted that the city continues to function as a practical convening ground for discussions at the intersection of AI, finance, and deployment.

The city’s combination of:

• International capital markets

• Dense institutional finance presence

• Proximity to advanced technology ecosystems

• Mature regulatory and legal frameworks

makes it a location where global, regional, and Mainland perspectives can be examined simultaneously. This contextual observation was used to explain why Hong Kong serves as a useful venue for launching and debating analytically rigorous work — not as an endorsement, but as an empirical reality.

Looking Ahead: Analysis Before Advocacy

The closing portion of Dmitry Kaminskiy’s presentation emphasized that credible AI strategy must be grounded in analysis before advocacy. Indexes, ecosystem maps, and deployment studies are tools for understanding constraints, trade-offs, and systemic risks — not marketing instruments.

DKG’s continued participation in forums such as the NTT Future Visions Summit reflects this orientation: contributing structured, evidence-based perspectives to discussions where technology, finance, and infrastructure increasingly converge.

As AI continues its transition from innovation layer to economic infrastructure, such forums — and the analytical work underpinning them — will play a growing role in shaping informed, durable decision-making across regions and sectors.