Illusion of Control: Exploring the Limits of Human-in-the-Loop Oversight in Generative Finance

Integration of generative AI into financial services has created efficiency and innovation opportunities but also introduced systemic vulnerabilities. Regulators and firms have relied on the Human-in-the-Loop (HITL) paradigm, assuming human oversight ensures accountability over complex automated systems. However, the cognitive and temporal limits of humans are mismatched with the speed and autonomy of AI-driven markets, rendering oversight fragile and often illusory. Drawing on cognitive science, human factors research, and historical case studies such as the Knight Capital collapse and the 2010 Flash Crash, this paper evaluates why tactical human interventions fail in high-frequency trading. We propose an alternative framework of embedded, automated governance, combining dynamic circuit breakers, pre-approval model constraints, and agentic self-monitoring that escalates only genuinely ambiguous cases to humans. This approach reconceptualizes safety as an intrinsic system property rather than a human add-on, offering a practical blueprint for resilient AI oversight. Our findings indicate that automated governance layers can more effectively mitigate high-speed risks than human-dependent models. We conclude that regulators and firms must shift from aspirational HITL guidance to operationally grounded, machine-native safeguards, with implications for both financial infrastructure design and AI regulation.