Anomaly Ranking for Enterprise Finance Using Latent Structural Deviations and Reconstruction Consistency

This paper targets key challenges in enterprise financial anomaly monitoring, including coupled multi-source heterogeneous features, concealed anomaly signals, and cost-sensitive alerting. It proposes a unified monitoring framework based on representation-driven data modeling. The approach first normalizes and encodes transaction and identity-related fields within a unified feature space. A learnable representation mapping function is then used to obtain low-dimensional latent representations. A reconstruction consistency mechanism is introduced to suppress noise while preserving essential behavioral information. In the latent space, normal behavior structures are statistically characterized. Structural anomaly signals are derived by measuring the deviation between representation centers and sample representations. These signals are combined with reconstruction errors from the original inputs to form a unified anomaly score. This enables continuous ranking of samples and threshold-based anomaly decisions. To ensure controllability in training and deployment, the framework is systematically analyzed from the perspectives of hyperparameter sensitivity, including learning rate, optimizer choice, and network depth, as well as environmental sensitivity under input noise injection. The analysis delineates the impact boundaries of key configurations on overall detection performance and stability. The results demonstrate that the framework maintains consistent risk identification characteristics under different configurations and perturbations. It provides sortable and configurable anomaly signals for enterprise risk alerts, audit sampling, and compliance inspection.