Intelligent Credit Fraud Detection with Meta-Learning: Addressing Sample Scarcity and Evolving Patterns

This paper addresses key challenges in credit fraud detection, including small sample sizes, imbalanced data distribution, and rapidly changing fraud patterns. A meta-learning-based detection method is proposed to enhance the model's adaptability and detection efficiency in complex scenarios. The method adopts a two-level architecture consisting of a meta-learner and a task-learner. By extracting shared knowledge across multiple fraud detection tasks, the model can rapidly update its parameters. This allows it to maintain high detection performance when facing new types of fraud. In the method design, a task-level gradient update mechanism and a weighted loss strategy are introduced. These components help address the challenges caused by rare fraud samples and class imbalance. The proposed model is systematically evaluated through multiple experiments. These include different task quantities, varying degrees of data imbalance, and injected abnormal fraud patterns. The results confirm the model's effectiveness and robustness under diverse conditions. Experimental findings show that the proposed method outperforms traditional models in accuracy, precision, and recall. It also demonstrates better stability and generalization when facing dynamically changing fraud scenarios. This study highlights the application potential of meta-learning in credit fraud detection. It provides technical support for building more intelligent financial risk control models.