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Sprint projectOct 26, 2026United States of America

Insider Trading Risk Due to AI Agent Collusion: An Experimental Study of Information Barriers in Financial Institutions

Rachita Mehta · Team AI Collusion Risk in FinCrime Compliance

Submitted to AI Collusion Research Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.

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This study examines AI-agent information leakage risks in financial institutions through a synthetic simulation of communication between investment banking and trading agents. Across 500 scenarios, it compares a public-information baseline, unrestricted communication, and a probabilistic metadata-based firewall. Unauthorized use of material nonpublic information occurs in all 178 eligible scenarios under unrestricted communication, compared with 16 (8.99%) under the firewall condition. Although the difference in simulated profit and loss is not statistically significant, the findings highlight a potential control risk relevant to Financial Crime Compliance . This research can help compliance teams assess AI-related information-barrier vulnerabilities and inform preventive controls, monitoring, and governance as AI adoption expands.

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Cite this project

@misc{mehta2026insider,
  title = {{Insider Trading Risk Due to AI Agent Collusion: An Experimental Study of Information Barriers in Financial Institutions}},
  author = {Rachita Mehta},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/insider-trading-risk-due-to-ai-agent-collusion-an-experimental-study-of-information-barriers-in-financial-institutions-j0vf}},
  url = {https://apartresearch.com/sprints/projects/insider-trading-risk-due-to-ai-agent-collusion-an-experimental-study-of-information-barriers-in-financial-institutions-j0vf}
}

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