FraudDetection
Anomalies caught and acted on as transactions clear, not after the loss is booked.
Batch detection lets fraud run before alarms fire
Fraud detection depends on identifying anomalies across high-velocity data streams. Traditional systems rely on batch analysis or delayed processing.
Streaming, vectors, and rules in one execution loop
MonkDB processes transactions, behavioural signals, and historical patterns simultaneously. Block, hold, or escalate decisions land before the transaction settles.
Stop fraud at the moment of decision
Per-transaction p99 under 5 ms
Score every event against models and rules without leaving the data plane.
Behavioural vectors
Detect novel attack patterns by comparing live behaviour to learned baselines.
Federated investigation
Replay any transaction against full historical context for auditors and analysts.
Continuous learning
Outcomes feed back into models to harden detection without retraining downtime.
Detect, decide, and act in the same millisecond.
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