Causal Representation Learning for Robust Anomaly Detection in Complex Environments

This paper proposes a deep discriminative model based on causal representation learning for the task of fraud detection in financial transactions. The method aims to enhance model stability and generalization in complex environments, such as distribution shifts and strategy changes, by modeling the underlying causal structures among transaction features. Specifically, the model first captures the generative process of transaction data using causal structural modeling. It introduces latent variables to represent causal dependencies among generative factors. Next, a causal encoder and reconstruction decoder are constructed within a variational autoencoder framework. This enables causally guided representation learning from the input data. Based on these representations, a classifier is designed to perform the fraud detection task. A domain discriminator is also introduced to enforce domain invariance in the latent space. This helps improve robustness under diverse fraud strategies. To validate the proposed method, experiments are conducted on a publicly available financial fraud dataset. The model is evaluated from multiple perspectives, including discrimination ability, stability, and cross-domain generalization. Results show that the proposed model outperforms representative existing methods across several metrics. It shows particularly strong performance in scenarios with noticeable strategy changes or significant feature perturbations. These findings further demonstrate the modeling advantage of causal mechanisms in fraud detection tasks. The overall framework enhances the extraction of key fraud-related features. It also provides theoretical and practical value for representation learning methods in high-risk environments.