Yingnan Yi

Submitted: 16 October 2025 You are already at the latest version This paper addresses the complexity and high-risk nature of fraud detection in financial transaction data by proposing a discrimination method based on an improved Mamba architecture. The study first performs data cleaning, normalization, and embedding to reduce scale differences across multimodal features, and employs temporal encoding and positional embedding to strengthen sequence representation.

Submitted: 16 October 2025 You are already at the latest version 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.