China named world's second most competitive country in AI finance
China has moved into second place globally for AI competitiveness in finance, according to a new international index. It scored 83.41 out of 100, behind the U.S. at 98.84 and ahead of the U.K. at 78.26.
Switzerland ranked fourth, followed by Singapore, Germany, Saudi Arabia, and India. The index was released on Jan. 28 by Deep Knowledge Group.
Where China is strong
China led the world in financial-sector AI maturity with a score of 90, reflecting broad adoption across banking, insurance, fintech, and investment management. The country counts 2,065 AI-focused finance enterprises, supported by scaled mobile payments, AI-driven lending (including credit-scoring models), and digital banking.
Dmitry Kaminskiy of Deep Knowledge Group said China's edge shows up where scale, speed of adoption, and end-to-end deployment matter most. That plays well in consumer payments, credit decisioning, and back-office automation where throughput and distribution drive outcomes.
Where it lags (for now)
Infrastructure and data readiness came in at 62 for China, compared with 80 for the U.S. Capital availability scored 72 versus 86 for the U.S. and 80 for the U.K., pointing to less headroom for funding expansion.
That gap may close if policy-directed capital keeps flowing into technology and AI, as suggested by the recent pickup in tech IPOs. If that continues, expect faster build-out in compute, data pipelines, and vendor capacity.
Global picture
The fifth edition of the Global AI Competitiveness Index assessed 20 countries and 15 city-level financial hubs for capability and maturity. The U.S. led by combining performance across all measured pillars, not just scale.
At the city level, Hong Kong ranked third globally after New York and London. That positions it as a bridge for regional deployment and cross-border products.
Why this matters if you work in finance
- Pricing pressure and speed: AI-native competitors in China will push faster underwriting cycles, sharper pricing, and leaner ops. Expect spillover through partnerships and vendors.
- Vendor selection: Many AI finance vendors now originate from or scale in China. Due diligence on data lineage, model risk, and compliance will be non-negotiable.
- Data strategy: The gap in infrastructure and data readiness signals opportunity. Firms with cleaner, well-governed datasets will extract more value from similar models.
- Capital and partnerships: Watch policy-driven capital in China. Cheaper, faster innovation there can shape your partner map and M&A pipeline.
- Regional hubs: With Hong Kong in the top three city hubs, expect tighter links across New York-London-Hong Kong for issuance, liquidity, and AI-enabled market infrastructure.
Practical moves for CFOs, CROs, and CIOs
Leaders looking for governance and strategy frameworks can consult AI for Executives & Strategy for practical guidance.
- Run an AI maturity check across underwriting, fraud, collections, client service, and middle/back office. Prioritize use cases with measurable P&L lift within two quarters.
- Tighten model risk controls: document data sources, monitoring thresholds, and fallbacks. Treat vendor models like internal ones-same standards, same audits.
- Upgrade data plumbing before adding more models. Focus on access controls, feature stores, and lineage-this is where most ROI gets trapped.
- Pilot cross-border partnerships in Hong Kong for distribution or co-developing risk models, with clear compliance gates and exit clauses.
- Track policy and primary markets in China to time partnerships and allocate a small exploration budget to proven AI finance vendors.
Sources and further reading
See Deep Knowledge Group for index methodology and updates: Deep Knowledge Group.
If you're mapping tools by function (risk, payments, fraud, analytics), this curated collection can help shortlist options: AI for Finance.
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