A Comprehensive Survey of Tabular Anomaly Detection

Tabular anomaly detection (TAD) assigns anomaly scores to unusual samples in tables and supports applications in finance, healthcare, cybersecurity, industrial monitoring, and data-quality assurance. Its central challenge stems from the heterogeneous and weakly structured nature of tabular data: unlike images, sequences, and graphs, tables lack native spatial, temporal, or relational structure while mixing heterogeneous feature types without a common metric, so the notions of distance, density, and dependency that detectors rely on must be induced through representation and encoding choices. Part of what defines an anomaly may also reside outside processed values, in column semantics, domain rules, or schema information that standard pipelines discard. Recent TAD research spans non-deep detectors, deep task-specific models, large language model-based methods, and foundation model-based methods. These directions differ in supervision, information access, evaluation protocols, and downstream use, yet existing surveys examine neighboring areas largely in isolation. This survey provides a unified account of TAD by organizing existing detectors according to how they form anomaly scores. Beyond method taxonomy, it reviews enhancement and adaptation for deployment, benchmarks and evaluation protocols, and downstream tasks built on TAD outputs. It aims to clarify method assumptions and provide a more comparable basis for future TAD research.