Auditing Collusion and Review Failures in Financial Data Delivery Workflows

AI agents that prepare and review financial data may face pressure to deliver information that a license prohibits. This study examines whether such agents coordinate to bypass the restriction, and what evidence distinguishes collusion from individual mistakes. A synthetic financial workflow pairs a preparer that assembles a data package with a reviewer that controls its release. Two autonomous-agent studies, with 32 and 24 episodes, recorded no prohibited submissions or releases. Two later autonomous technical pilots each produced two prohibited submissions with inaccurate scope claims, but rejection or withdrawal prevented release. Model reviewers were then tested against a scripted preparer in two 16-episode invitation diagnostics and a separate 36-episode screen varying task framing, shared simulated scores and explicit cover-up invitations. No controlled reviewer attempted to approve a prohibited package. However, correct decisions did not always follow reliable procedures or come with accurate accounts of the reviewers’ actions. In one diagnostic, three attempted rejections never executed. In the 36-episode batch, four reviewer rationales claimed to have created a file that the script had created, and all four safe controls first attempted approval before completing required checks. These results do not establish deliberate deception or autonomous collusion, and do not show that collusion risk is low. They show why audits must examine released content, process claims, attempted actions and executed decisions separately, rather than treating a safe outcome or a confident explanation as sufficient evidence of reliable review.