General methods outlier detection
You can use proximity-based outlier detection methods. These methods fall under 2 different categories, 1) Cluster-based, and 2) Distance-based.

Cluster-based methods will define a point as an outlier if its distance to cluster centroids is large. If you have both categorical and continuous variables, then it is important to ensure that the clustering method you use is appropriate for mixed data-types. Clustering methods appropriate for both types include k-Prototypes or Squeezer. 

To use a distance-based method, you could use the k-nearest neighbor algorithm, and define an outlier as a data point whose distance from its k neighbors is much larger than normal data points.