Anomaly detection in textual data remains an underexplored problem, hindered by fragmented benchmarks, scarce labeled data, and methods that fail to model the distribution of normal text in a continuous and generative manner. We propose Flocat, an unsupervised approach that reformulates this task through a source-reversed flow matching paradigm: rather than learning to generate, we learn a flow from inlier text embeddings toward a compact isotropic Gaussian target, whose radius is explicitly constrained to ensure that the distance to the centroid serves as a geometrically consistent anomaly score. Built on a Diffusion Transformer (DiT) backbone, Flocat operates at both token and sentence level through a level-agnostic interpolation mechanism, enabling fine-grained signal interpretability. We evaluate on 10 datasets spanning text classification, spam detection, sentiment analysis, human- vs. machine-generated text and depression screening, consistently achieving state-of-the-art AUC-ROC across all benchmarks and text representations.