This paper proposes Mass Repulsing Optimal Transport (MROT), a new method for outlier detection, by utilizing the existing Optimal Transport (OT) theory. MROT is a method that moves the mass of data with minimal effort, while forcing samples in low-density areas suspected of being outliers to move their mass farther away, incurring high transport costs. We utilize this transport cost as an outlier score to detect outliers, and demonstrate through experiments that it outperforms existing outlier detection methods.