DETECTING SYNTHETIC IDENTITY FRAUD USING BEHAVIOURAL AND NETWORK FEATURES: A REVIEW

Synthetic identity fraud (SIF) combines real and fabricated personal information to create new identities that pass onboarding checks, build credit over time, and disappear in a bust-out. Traditional rule-based models often miss these identities, and generative AI and deepfakes make them more realistic. This narrative review examines how researchers detect SIF using behavioral and network features. It covers generative-contrastive approaches to identity generation and detection, vulnerabilities in Know Your Customer (KYC) processes, temporal and transactional behavioral features, behavioral biometrics, and transaction network topology for finding fraud rings. The reviewed studies indicate that SIF rings leave coordinated temporal traces, that behavioral interaction data is difficult to forge, and that synthetic identities tend to form dense local clusters with few ties to the wider financial network. The review adds a summary table of feature families and notes that fraudsters can adapt to graph-based detection. Most evidence comes from single studies, common benchmarks are missing, and behavioral monitoring raises privacy and fairness questions. The article follows no formal search protocol. Future work should test detection methods under adversarial conditions and report results in a standard format.