AI Reshapes Finance, but Regulatory and Industry Divide Persists on Governance
AI is revolutionizing the financial sector, enhancing efficiency in trading, risk management, and client services. However, its rapid adoption has created a rift between industry enthusiasm and regulatory caution, with debates over how to define, govern, and deploy AI responsibly. A Broadridge Financial Services survey indicates 86% of over 500 financial firms plan to boost AI investments by 2027, yet regulators struggle to keep pace.
The Broadridge survey underscores AI’s growing role, with firms prioritizing fraud detection, algorithmic trading, and portfolio optimization. The AI financial services market is projected to reach $130 billion by 2027, up from $8.3 billion in 2019. This investment surge reflects AI’s ability to process vast datasets, driving operational efficiency and competitive advantage.
At a March 2025 SEC roundtable, experts from financial institutions, academia, and tech firms highlighted that AI’s rapid evolution outstrips traditional regulatory frameworks. Discussions emphasized balancing AI’s benefits—improved decision-making and cost savings—with risks like conflicts of interest, data privacy, and algorithmic biases. Participants advocated for a principles-based, risk-focused regulatory approach to protect investors while fostering innovation.
SEC’s Predictive Data Analytics Proposal
In July 2023, the SEC proposed the “predictive data analytics” rule, requiring investment advisors and broker/dealers to mitigate AI-related conflicts in investor interactions. The proposal faced significant industry backlash for its broad scope, as noted in a LSTA comment letter. The SEC’s July 2024 agenda promised revisions, but as of May 2025, none have been released.
At the roundtable, SEC Commissioners Mark Uyeda and Hester Peirce criticized the proposal’s restrictive approach. Uyeda, interim SEC Chair until Paul Atkins’ April 21, 2025, confirmation, warned of “unnecessary barriers” to technology adoption, while Peirce called the rule “clumsy.” Commissioner Caroline Crenshaw acknowledged industry concerns without opposing the proposal directly.
The need for a formal AI definition sparked debate. Gregg Berman of Citadel Securities likened AI to high-frequency trading, which thrived without a rigid definition, questioning its necessity. Conversely, BlackRock’s Daniel Pateiro advocated for a flexible taxonomy to enhance transparency and guide regulation, emphasizing adaptability to AI’s evolving capabilities. This divide reflects broader uncertainty about regulating diverse AI applications.
Regulatory Landscape
Unlike the EU’s EU AI Act, effective July 2024, the U.S. lacks a comprehensive AI regulatory framework. The Federal Trade Commission addresses deceptive AI practices, and California’s Consumer Privacy Act governs data usage. The current administration favors a relaxed regulatory stance, with President Trump’s January 2025 executive order urging agencies to “remove regulatory barriers” and file an AI action plan by July. In April, the administration issued revised policies governing federal AI use, building on Trump’s directive.
Financial Firms Take Independent Action
Firms are proactively managing AI risks absent federal rules. Morgan Stanley’s Jeff McMillan described a tiered, risk-based approach (Morgan Stanley AI Governance), noting the challenge of responsible generative AI deployment. The Depository Trust Co.’s Johnna Powell emphasized lifecycle oversight (DTCC AI Policy). Vanguard’s Ryan Swan highlighted its “AI Academy” for literacy and data-sensitive governance (Vanguard AI Academy). American University law professor Hilary Allen urged regulators to hire technologists and cautioned against over-trusting AI outputs. “We tend to think anything spit out of a computer is better than what we come up with ourselves. It takes a lot to say, ‘No, the machine is wrong.’”
AI Applications in Finance
While regulatory concerns dominate headlines, AI adoption remains focused on internal productivity. Nasdaq’s Douglas Hamilton uses AI for productivity, index creation, and trading execution (Nasdaq AI Strategy). BlackRock’s Pateiro noted AI optimizes trading and streamlines reconciliations (BlackRock AI Tools), with non-AI solutions amplifying results. Firms measure AI’s ROI through efficiency, alpha generation, and risk reduction, with BlackRock using AI for algorithmic pricing to lower transaction costs.
Looking Ahead: Industry and Regulators Must Align
AI’s transformative impact on finance is tempered by regulatory lag and governance debates. The SEC’s pending rule revisions, calls for flexible definitions, and robust internal frameworks highlight a sector in transition. As AI investments surge, a principles-based regulatory approach will be crucial to balance innovation with investor protection in a volatile market environment.
