As financial ecosystems migrate toward an architecture composed of autonomous software agents representing banks, telecom operators, payment processors, and end users. The communication channels between these agents become high-value targets for adversarial exploitation. Conventional fraud detectors operate on transaction-level attributes such as amount and type, and are blind to the behavioural signatures embedded in inter-agent message exchange. This paper proposes a detection framework that extracts twelve communication behavioral features (response time, message length, retry count, session anomaly score, authentication failure rate, interaction frequency, and others) from agentto-agent (A2A) traffic and feeds them through a two stage pipeline: (i) an autoencoder trained exclusively on legitimate interactions to produce an eight dimensional latent embedding and a scalar reconstruction error anomaly score, and (ii) an XGBoost classifier that operates on the 21 dimensional fusion of raw and autoencoder derived features. We evaluate the system on PaySim, a widely used financial dataset containing 6.36 million records, under a deliberately balanced protocol (50/50 fraudlegitimate split) that avoids the metric inflation common in imbalanced evaluations. Across six distinct attack types : (i) impersonation (ii) prompt injection (iii) collusion (iv) trust-chain exploitation (v) data exfiltration, and (vi) transaction manipulation. The proposed framework attains an F1-score of 0.933, AUC-ROC of 0.983, and per-attack F1 ranging from 0.928 (collusion, the stealthiest category) to 1.000 (transaction manipulation). A complementary four-tier graduated mitigation strategy (Monitor/Throttle/Quarantine/Block) achieves 93.52% fraud prevention, marginally exceeding binary blocking (93.27%) while reducing friction for legitimate users by 30%. We discuss the implications for emerging agent-to-agent (A2A) ecosystems expected in 2028 and beyond, in which autonomous AI agents will negotiate, transact, and settle on behalf of human principals without direct human oversight.