AI Uncertainty Favors Strong Homegrown Markets Over Rival Hubs

Domestic Markets + AI: From Rivalry to Resilience

Rivalry between global financial hubs made sense when capital, talent and market access were concentrated in a few cities. That era is fading. As AI rewires workflows and risk models, and as countries deepen their own capital markets, the edge shifts from "attracting flows" to "building domestic capacity."

For finance leaders, the question is simple: are you optimizing for offshore prestige, or for onshore depth that lowers funding costs, widens investor participation and absorbs shocks?

The old race is losing steam

For decades, New York, London, Hong Kong and Singapore defined global finance. Others tried to catch up. The assumption: you needed a dominant hub to access capital and expertise.

That assumption is weakening. At recent events in Tokyo hosted by FinCity Tokyo and the Tokyo Metropolitan Government, participants emphasized a broader playbook: use "financial center" strategy to build the home market-move households from deposits into diversified investments, scale local currency bond markets and connect corporate funding to domestic savers.

Signals to watch: Tokyo-Frankfurt link-up

Representatives from Tokyo and Frankfurt signed an agreement to accelerate mutual capital market development. This is notable because both Japan and Germany have long prioritized manufacturing over finance. Their pivot suggests a shared view: deeper home markets are now a strategic asset, not a side project.

If two industrial heavyweights are coordinating on market-building, expect peers to follow. The center of gravity is shifting from "where deals are booked" to "where savings meet investment at scale."

What AI actually changes in market structure

AI won't replace market plumbing, but it reduces the premium on physical proximity and imported expertise. More of the value chain can run domestically if the data, rules and rails exist.

• Pricing and underwriting: granular cash flow and alt-data models improve SME lending, muni finance and private credit-without outsourcing analytics overseas.
• Market-making and surveillance: local exchanges can run tighter spreads and stronger supervision with AI-driven liquidity management and anomaly detection.
• Advisory at scale: retail and mass-affluent investors get low-cost, compliant advice, boosting equity and bond participation over bank deposits.
• Compliance and reporting: automated KYC/AML, ESG assurance and disclosure mapping lower issuance frictions for domestic corporates.
• Risk and treasury: faster, AI-assisted ALM and hedging in local currency strengthens banks and insurers, encouraging longer-duration assets at home.

Why deeper domestic markets matter in an AI-uncertain cycle

• Shock absorption: more local-currency funding and broader investor bases reduce exposure to FX swings and fickle cross-border flows.
• Capital formation: turning high savings into productive investment lifts potential growth and funds tech diffusion-including AI infrastructure.
• Policy flexibility: authorities can use targeted liquidity tools, collateral frameworks and market backstops with fewer external constraints.
• Speed: AI-augmented issuance, settlement and disclosure shortens the loop from idea to capital-especially for SMEs and project finance.

Priority moves for policymakers

• Household participation: tax-efficient accounts, default enrollment in diversified funds, auto-escalation and digital onboarding.
• Institutional depth: scale pension and insurance AUM with clear mandates for long-term local assets; modernize solvency and valuation rules.
• Corporate access: streamline shelf registration, lower minimum lot sizes, standardize covenants and enable recurring issuance programs.
• Market plumbing: T+1 or faster settlement, interoperable CSDs, central clearing for key rates/credit products and reliable securities lending.
• Data standards: machine-readable disclosures (XBRL), loan-level data repositories and consistent ESG taxonomies to feed AI systems.
• Open finance: APIs for payments, identity and account data; strong privacy and consent frameworks to unlock analytics without leakage.
• Securitization and private markets: simple, transparent structures with reporting discipline to broaden funding beyond banks.
• Green and transition finance: credible frameworks and benchmarks to crowd in domestic savings for energy and industrial upgrades.

What exchanges and market operators can execute now

• AI-native surveillance and market quality dashboards; publish liquidity scores to attract both issuers and market makers.
• SME and muni corridors: templated documentation, digital data rooms and standardized covenants for repeat, small-ticket issuance.
• Tokenized market pilots where legal certainty exists-focus on settlement efficiency, not hype.
• Issuer analytics: pre- and post-trade data feeds, comparable-issuer stacks and investor targeting tools.

For banks and asset managers

• Build local factor libraries: granular credit, supply chain and consumer data to price risk better than offshore competitors.
• AI-assisted origination: automated memoing, covenants and monitoring to profitably serve SMEs and mid-cap issuers.
• Retail scale: model portfolios, goal-based planning and compliant advice flows to reallocate deposits into diversified funds.
• Treasury and risk: local currency liquidity ladders, options markets and collateral optimization to deepen duration.
• Operating discipline: model governance, scenario testing and human-in-the-loop reviews to prevent costly errors.

Metrics that show real progress

• Local-currency bond market/GDP and the share held by households and pensions.
• Household allocation to equities and funds vs. deposits.
• Domestic IPO and bond volumes, repeat issuance rates and time-to-market.
• Bid-ask spreads and turnover in priority segments (sovereign, SSA, IG credit, SME ABS).
• Share of corporate funding raised onshore vs. offshore.
• Coverage, accuracy and latency of machine-readable disclosures.

Risks to manage as AI scales

• Model concentration: diversify providers and keep critical models auditable and portable.
• Data leakage and privacy: strict access controls, synthetic data where feasible and red-teaming.
• Operational resilience: stress test core workflows for outages, vendor failures and cyber incidents.
• Market integrity: monitor feedback loops where many firms use similar signals; maintain human oversight.
• Consumer protection: disclosures that real people can understand; escalation paths to human advice.

What the Hong Kong-Singapore rivalry means now

Both will stay important, but the headline contest matters less if countries build credible domestic pipes. The practical move for CFOs and allocators is to price funding optionality: maintain access to these hubs while lowering reliance on them by building local issuance and investor channels.

CFOs can explore the AI Learning Path for CFOs to align capital strategy with AI-enabled market development.

In short: keep the doors to global pools open, but make home your default.

Bottom line for finance teams

AI reduces the need to import every capability; domestic market depth reduces the need to import every dollar. Together, they lower your cost of capital and increase your degrees of freedom in uncertain conditions.

If you influence policy, focus on plumbing, participation and data. If you run a desk, focus on models, liquidity and repeatable issuance. Finance managers can pursue the AI Learning Path for Finance Managers for practical training in budgeting, forecasting and AI-assisted origination.

Either way, the edge goes to those who build at home-and keep a clean line to the global grid.

Further reading
- Local currency markets overview: IMF
- Capital Markets Union policy background: European Commission

Upskill your team on AI in finance
Explore practical tools and workflows: AI tools for finance