Financial regulators confront a pressing dilemma: maintaining oversight in markets where algorithmic trading is prominent. The existing framework, which hinges on proving intent, struggles when machines—not humans—drive trading activity. Specialists insist that accountability should pivot from motivation to actual damage caused by algorithmic behavior.
Gina-Gail S. Fletcher, a law professor at Duke University and member of the Financial Industry Regulatory Authority’s National Adjudicatory Council, warns that current enforcement methods are outdated for an era dominated by high-speed algorithmic execution. “Intent has always been notoriously difficult to prove in manipulation cases, and with algorithms and AI creating a layer of separation between the trader and the alleged misdeeds, it is likely to be even more difficult to assign liability based on intent,” she states.
Her proposed fix centers on liability tied to measurable consequences rather than subjective intent. If an algorithm distorts pricing, spreads misleading signals, or exploits market vulnerabilities, penalties should apply, even if its designers lacked deceptive intent. The emphasis must remain on results: whether trades eroded market fairness, reduced efficiency, or increased instability. Penalties could then vary based on whether the conduct was intentional, reckless, or negligent.
Regulators lag behind in tech tools
This strategy reflects a broader regulatory shortfall. The SEC and CFTC, which oversee securities and derivatives respectively, operate in disconnected units that no longer match how modern markets function. Fletcher advocates for closer collaboration on algorithmic trading rules rather than a full merger, which might introduce unnecessary complexity. Coordination alone won’t suffice, however. Regulators currently lack the technical tools to monitor algorithmic activity effectively, trailing behind firms that use machine learning to spot irregularities or bypass oversight.
“Regulators’ technological capabilities tend to lag behind industry capabilities, novel technologies such as artificial intelligence have a role to play in identifying and regulating harmful behavior in financial markets,” Fletcher notes.
The debate over balancing innovation with protection is longstanding, but algorithmic trading has sharpened its urgency. Some policymakers dismiss regulation as an obstacle to progress, assuming stricter rules would hinder technological advancement. Fletcher dismisses this view, emphasizing that the goal is to prevent technology from harming investors or destabilizing markets.
Algorithmic manipulation persists, even if its methods have changed. Regulators risk overlooking familiar risks, like market manipulation, because of the novelty of AI. Fletcher’s solution is straightforward: recognize that old problems persist in new forms. Rules must adapt to technological shifts rather than treating algorithms as a distinct category demanding entirely new frameworks.
Currently, the SEC and CFTC face dual pressures: the need to address algorithmic risks swiftly and the reality that their resources and expertise cannot keep pace. Fletcher’s harm-focused approach offers a solution, prioritizing outcomes over intent and using AI to narrow the surveillance gap. However, success demands regulators shift from reactive enforcement to a system where rules adjust in real time, not years after market changes.
Old risks demand new regulatory approaches
Technology has already reshaped financial markets. The critical question is whether regulators can adapt their methods quickly enough to mitigate the resulting risks.
Fletcher’s research demonstrates how existing enforcement tools fail to account for algorithmic misconduct. She argues that a harm-based liability system could reduce manipulation by focusing on measurable market impact rather than unprovable intent.
Critics argue that harm-focused liability could incentivize firms to underreport algorithmic errors for fear of regulatory action. Fletcher counters that transparency incentives, such as reduced penalties for voluntary disclosures, could offset this risk. “The alternative is a system where firms hide problems until they escalate into crises,” she warns. “Prevention should be rewarded, not punished.”
Her most recent paper, co-authored with economists at the Bank for International Settlements, models how a harm-based liability system could reduce algorithmic manipulation.
SEC takes steps toward harm-based enforcement
As of recent data, the SEC has opened enforcement actions related to algorithmic trading, reflecting growing recognition of these risks. Fletcher attributes this to the need for updated liability standards that align with modern trading realities.
Her final recommendation: regulators should establish a framework for adaptive oversight, leveraging AI and continuous rule updates tied to market behavior. Without such changes, she warns, regulators will continue playing catch-up in an evolving financial system.
