China climbs to No. 2 in AI-for-Finance, Hong Kong ranks third as a city hub
China is now the second most competitive country for AI in finance, according to the latest Global AI Competitiveness Index from Deep Knowledge Group. The US leads with a score of 98.84, followed by China at 83.41 and the UK at 78.26. Switzerland placed fourth, with Singapore, Germany, Saudi Arabia and India rounding out the top group.
The index's fifth edition compares 20 countries and 15 city hubs, measuring maturity across categories like production deployment, innovation ecosystem, talent and R&D, regulation, infrastructure and data readiness, and capital availability.
Where China stands out
China scored 90 in financial sector AI maturity - best-in-class for translating capability into live systems across banking, insurance, fintech and asset management. The innovation ecosystem scored 80, with talent, education and R&D at 76.
Government support and market demand are doing the heavy lifting. The report cites US$269 billion in total funding for AI start-ups in China, many focused on fintech and blockchain. The sector now includes 2,065 AI enterprises targeting finance, with strong traction in mobile payments, AI-driven lending (credit scoring), and digital banking.
As Dmitry Kaminskiy of Deep Knowledge Group put it, China is strongest at the "last mile" - turning capability into production-grade systems at speed and scale.
Where China still trails
Infrastructure and data readiness came in at 62 versus 80 for the US - a signal that the next leg of progress is about making deployment repeatable and resilient at institutional scale. Capital availability scored 72 (US 86, UK 80), suggesting tighter headroom for late-stage growth, cross-border ventures and sustained financing of finance-grade AI.
That gap may close. Policy-driven capital flows and recent tech IPO activity point to momentum building on the funding side.
City hubs: Hong Kong at No. 3
New York and London took the top two spots among city hubs, with Hong Kong ranked third. The city scores high on capital markets and regulated adoption, with strengths in cross-border connectivity and institutional depth.
The trade-off: a smaller ecosystem and lower funding intensity than the top two hubs. The report categorizes Hong Kong as a scaling and go-to-market hub rather than an early-stage experimentation center.
What finance leaders should do next
- Prioritize production use cases: credit underwriting, fraud, AML, risk analytics, treasury, client onboarding, and ops automation.
- Double down on data readiness: unify data pipelines, strengthen lineage and access controls, and standardize model monitoring for auditability.
- Tighten vendor diligence: require model governance artefacts, stress-testing results, data provenance and clear remediation paths.
- Align with compliance early: map each use case to model risk policies, privacy rules and sector-specific guidance.
- Build a cross-border plan: for Hong Kong, use the city's strengths in capital markets and regulated adoption to scale proven use cases regionally.
- Address funding bottlenecks: if you're building in China, plan earlier for late-stage and cross-border capital to avoid momentum stalls.
Quick numbers you can take to your board
- Country scores: US 98.84, China 83.41, UK 78.26; Switzerland fourth; then Singapore, Germany, Saudi Arabia, India.
- Financial sector AI maturity: China 90 (highest in the index).
- Innovation ecosystem: 80; Talent/Education/R&D: 76.
- Infrastructure & data readiness: China 62 vs US 80.
- Capital availability: China 72; US 86; UK 80.
- Total AI funding: China ~US$269B; US >US$310B; UK ~US$152B.
- Enterprises focused on AI in finance (China): 2,065.
Why this matters for your 12-24 month plan
The US leads on breadth and institutionalization. China wins on scale, adoption speed and turning pilots into production. Hong Kong offers a practical launchpad for regulated, cross-border finance use cases.
If you're allocating budget, bias toward deployment-readiness: data plumbing, governance, risk controls and integration with core systems. That's where the competitive gap shows up.
Source and further reading
Deep Knowledge Group's Global AI Competitiveness Index provides the full methodology and rankings. View the publisher's site.
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