A Self-Supervised Graph-Temporal Model for Fraud Detection in Digital Bank Account Opening: A Systematic Review
Keywords:
Self-supervised learning, Graph neural networks, Temporal modelling, Account opening fraud, Label latencyAbstract
Digital bank account opening has become the latest trend in bank application and account opening. With its growth it also becomes one of the focuses of financial fraud. 2024 alone saw the potential for an estimated US$3.3 billion in losses at United States financial institutions from synthetic identity fraud. Most detection methods are based on supervised machine learning methods which are limited by the distance from the label source to the target server due to the properties of labels, extreme label-class imbalance and adversarial concept drift, and which can degrade in their detection capabilities by 15–30% within months of deployment. In this systematic review conducted as per PRISMA 2020 guidelines, the evidence related to fraud detection through supervised, graph-based approach, temporal approach, and self-supervised approach has been synthesised to determine if an integrated framework can cater to all the above three constraints. Selection of relevant literature was based on a search in six bibliographic databases, plus the proceedings of seven major machine-learning conferences, from January 2014 to April 2025. After deduplication and two-stage screening (with κs of 0.81 and 0.87 respectively), 34 studies were included from 1,847 which were later appraised using a Mixed Methods Appraisal Tool adapted for computational research. We identify the literature as going through three stages (2014-2017 n = 8; 2018-2022 n = 15; 2023-2025 n = 9) with two foundational pre-2014 works and six themes: supervised learning limitations, graph relational modelling, temporal sequence modelling, self-supervised contrastive learning, and integrated hybrid architectures. Seven research gaps were found across the studies, most importantly none of the studies included assessed an integrated graph-temporal self-supervised model for simulated label delay use or for account-opening data. Based on the above evidence, a conceptual yet still unvalidated architecture Self-Supervised Graph-Temporal (SSL-GT) is proposed that combines heterophily-aware graph learning, transformer temporal encoding and graph contrastive pre-training. The major findings indicate persistent limitations in supervised learning under delayed labels and drift, while graph, temporal and contrastive methods offer complementary but incompletely validated capabilities for digital account opening. The review concludes that an integrated SSL-GT framework remains a research proposal rather than a demonstrated solution. It recommends evaluation under realistic label delays, fraud-preserving graph augmentation, drift testing, operational-latency benchmarking and fairness audits.
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