Federated Risk Discrimination with Siamese Networks for Financial Transaction Anomaly Detection

This paper proposes a deep risk discrimination framework that integrates a Siamese network structure with a federated optimization mechanism to address challenges in financial transaction environments, including multi-source heterogeneity, data silos, and unclear decision boundaries. The method preserves data privacy by deploying structurally symmetric and parameter-sharing Siamese subnetworks across multiple clients. It performs contrastive encoding on transaction sample pairs and uses a contrastive loss function to pull normal samples closer and push anomalous ones apart in the embedding space. To improve the detection of low-frequency, high-impact behaviors, the model incorporates an entropy-aware regularization term and an embedding compression module. These components guide local training to suppress redundant representations and enhance embedding compactness. During the federated optimization process, the classic FedAvg strategy is used to aggregate models from different clients, improving global semantic alignment and generalization. Experiments on real transaction datasets demonstrate the superior performance of the proposed method across multiple metrics. The model shows particularly strong results in embedding consistency and anomaly boundary detection, confirming its modeling value and discriminative power in financial risk control.