Five-phase identity fraud detection sequence fuses behavioral, device, and contextual signals to classify authentication risk. User interaction flows through Identity Gateway to Feature Extractor, which isolates multimodal signals (typing cadence, device fingerprint, geolocation). Multimodal Fusion combines features for ML Classifier to detect anomalies, feeding Risk Engine for adaptive authentication decisions. This architecture demonstrates defense-in-depth for IAM teams combating credential stuffing and account takeover attacks. Fork this sequence diagram on Diagrams.so to model your own fraud detection pipeline with custom feature extractors or risk thresholds.