We propose a novel data refinement (DR) scheme that relies on neural autoregressive flows (NAF) for self-supervised anomaly detection. Flow-based models allow us to explicitly learn the probability density and thus can assign accurate likelihoods to normal data which makes it usable to detect anomalies. The proposed NAF-DR method is achieved by efficiently generating random samples from latent space and transforming them into feature space along with likelihoods via invertible mapping. The augmented samples incorporated with normal samples are used to train a better detector to approach decision boundaries. Compared with random transformations, NAF-DR can be interpreted as a likelihood-oriented data augmentation that is more efficient and robust. Extensive experiments show that our approach outperforms existing baselines on multiple tabular and time series datasets, and one real-world application in accelerator physics, significantly improving accuracy and robustness over the state-of-the-art baselines.