The migration of enterprise banking toward instant payments and virtual-account structures has changed anomaly detection from retrospective transaction review into a real-time analytical problem. Virtual accounts can generate high-frequency payment streams across corporate collections, reconciliation processes, customer accounts, and API-connected banking services, creating transaction patterns that static rules may inadequately distinguish from operational or behavioral abnormalities. This study develops a predictive anomaly-detection framework for real-time virtual-account transactions and API-driven payment processing. Rather than evaluating transactions independently, the framework models temporal and contextual behavior using transaction velocity, amount deviation, beneficiary patterns, account activity, payment sequences, processing status, API latency, response codes, retry frequency, and failed-payment clusters. Historical and rolling behavioral baselines establish expected activity at account, transaction, and API levels, while machine-learning and anomaly-detection models generate dynamic anomaly scores for incoming events. Temporal validation, threshold calibration, and false-positive analysis evaluate detection reliability under changing transaction conditions. The resulting intelligence differentiates account-level anomalies from payment-processing irregularities, supporting prioritized investigation, adaptive controls, faster exception management, and resilient real-time banking operations.