A Unified Meta Learning and Domain Adaptation Framework for Credit Fraud Detection in Dynamic Environments

This work targets open-world credit risk identification where few labeled samples and emerging fraud scenarios coexist. It proposes a unified detection framework that integrates meta learning and domain adaptation. The goal is to improve rapid adaptation and cross-scenario robustness in dynamic environments. The method uses a shared encoder for representation learning over multiple source transaction features. At the task level, it introduces a meta learning training paradigm. A support set-driven fast update mechanism is used to learn transferable initialization and efficient parameter adjustment. This enables the model to form an effective decision boundary quickly under limited target domain labels. To mitigate distribution shift caused by channel switching, temporal drift, and changes in business strategies, the framework adds domain alignment constraints at the representation level. It learns domain-invariant risk features by minimizing the discrepancy between source and target latent representations. This systematically presents stable ranges and behavioral patterns under different risk bias settings and scenario changes. The study provides a technical solution for credit fraud detection in open environments that supports unified modeling, fast adaptation, and cross-domain robustness.