Detecting Coordination Risk among AI Trading Agents: Integrating Communication and Behavioral Evidence for Financial Auditing

This study develops and evaluates an audit-oriented framework for detecting coordination risk among artificial-intelligence trading agents. The framework integrates non-semantic communication structure with behavioral alignment residualised against observable market-wide and common-shock conditions. The experiment uses a rule-based synthetic data-generating process rather than a reinforcement-learning model: coordinated sets, activity periods, and labels are specified for evaluation. Four scenarios represent independent activity, explicit coordination, weak and intermittent coordination, and a no-coordination common-shock counterfactual. Across 120 runs, five agents, and 500 periods per run, the integrated logistic model achieved a mean ROC AUC of 0.852 and an F1 score of 0.684 under repeated run-grouped holdouts. A common-shock ablation reduced ROC AUC to 0.737 and F1 to 0.584, showing that raw synchrony alone is not an adequate audit signal. A nested threshold procedure targeting a 10% G4 false-positive rate produced a mean out-of-sample G4 rate of 0.092. Semi-synthetic analysis using public daily Fama-French factor backgrounds yielded an ROC AUC of 0.844 and an F1 score of 0.697, but it does not constitute real-world validation. The proposed output is therefore a transparent referral score for human review rather than a finding of unlawful conduct.