AI reshapes the underlying logic of financial markets: from trading efficiency to risk defense

From Edge to Core: How AI Is Penetrating Every Layer of Financial Markets

While the market is still debating the trading speed of high-frequency algorithms, AI is already redefining the underlying architecture of financial markets. Frank La Salla, President of DTCC, pointed out at the Davos Forum that AI will be a super-cycle lasting over a decade, with impact not limited to trade execution but spanning the entire post-trade lifecycle—from clearing and settlement to regulatory reporting. This signals that AI is upgrading from a "optional feature" to a "standard infrastructure."

Traditionally, the financial market's adoption of technology has been cautious and gradual, but AI has changed that pace. The GenAI tool launched by DTCC has achieved 97% accuracy with zero hallucinations, compressing client meeting preparation time from one week to one day and boosting productivity nearly fourfold. These numbers are not isolated cases but reflect the industry's tipping point from "experimentation" to "scaled production."

The Data Moat: Why High-Quality Data Becomes a New Scarce Resource

AI's success in financial markets heavily depends on data availability, discoverability, and cleanliness. DTCC, being at the center of global transactions, naturally possesses massive structured data, providing a differentiated training foundation for AI models. La Salla emphasized that enterprises need to invest in modern technology stacks, including resilient data governance platforms and high-quality metadata, to unlock the transformative value of AI.

This means data holders—especially exchanges, clearing houses, and custodian banks—will have a natural advantage in the AI era. They can not only improve efficiency through internal AI but also offer data products and insight services to the market, creating new revenue streams. In contrast, small and medium-sized financial institutions lacking high-quality data may face an "AI divide," forced to rely on third-party platforms or merge.

Talent Transformation: AI Is a Multiplier, Not a Replacement

La Salla clearly stated that "AI is not a replacement for humans" but a "force multiplier." At DTCC, AI shifts employees from data collection to insight generation, increasing the time spent on high-value work by tenfold. This trend is reshaping the job structure in finance: repetitive, rule-based operational roles (e.g., manual reconciliation, report generation) will accelerate automation, while roles requiring judgment, creativity, and interpersonal communication (e.g., strategy development, client relations) become more important.

Accordingly, companies need to build an "AI literacy" culture, providing all employees with AI tools and training. This is not just about skills upgrading but also a competition in organizational efficiency. Those that first achieve company-wide AI empowerment will gain a generational advantage in response speed and service quality.

Regulation and Risk: The Double-Edged Sword of AI Must Not Be Viewed Only for Its BladeWhile AI improves efficiency, it also introduces new risks: model bias, algorithmic errors, and systemic vulnerabilities. La Salla emphasizes that robust AI governance and compliance frameworks must be established to prevent the technology dividend from being offset by risks. For the North American market, the U.S. SEC and CFTC have already begun to pay attention to the use of AI in trading and risk control, and it is expected that more specific requirements for data accountability and algorithmic transparency will be introduced in the future.

This means dual pressure for market participants: they must accelerate AI deployment to remain competitive while investing in compliance resources to ensure safety. Leading institutions may adopt a "regtech" strategy, AI-fying compliance itself, thereby turning passivity into initiative.

North American Regional Competition: Can New York Maintain Its Status as a Financial Center?

The application of AI is also reshaping competition among North American financial centers. New York still leads with Wall Street giants, major exchanges, and tech talent, but Chicago (derivatives trading), Toronto (banks and pension funds), and Silicon Valley (fintech) are all increasing their investment in AI financial infrastructure. In particular, Toronto, Canada, is leveraging its strong AI research ecosystem (Vector Institute, University of Toronto) to attract fintech companies, attempting to establish new advantages in intelligent risk control and quantitative trading.

• For Mexico and the broader Latin American market, AI finance started later, but the late-mover advantage lies in the ability to directly adopt next-generation technology architectures without having to overhaul legacy systems. This could enable emerging markets to leapfrog in AI-driven inclusive finance.- Combination of Smart Contracts and AI Agents: Agent-based AI systems will autonomously perform compliance checks, settlement matching, and cash flow management, reducing manual intervention.
• Paradigm Shift in Risk Management: From "post-hoc backtracking" to "real-time prediction," AI can identify systemic risks in advance and even trigger automatic circuit breakers.
• Rise of Financial Data Markets: Institutions share anonymized data through private APIs or consortium blockchains to train collaborative AI models, enhancing industry-wide risk control capabilities.
• Direction of New Capital Inflows: Private equity and venture capital funds will concentrate investments in AI-driven financial infrastructure companies, especially startups involved in data governance, model bias detection, and compliance automation.

In short, AI is making every aspect of financial markets smarter, faster, and safer. But the real winners are those enterprises that simultaneously build advantages in data, talent, and governance. They will define the capital markets of the next era.