Observation similarity problem

Hi there,

I'm trying to use machine learning to determine if a new observation follows the pattern of the current observations.

To elaborate, the problem is the following:

I have ~2,000 observations that all belong to the same class, unfortunately I don't have any observations for a different type of class so I can't use a supervised approach.

Each observation has a set of attributes that all together make it a particular class. As an analogy, imagine that each observation is a person who had their visa approved, I have age, income, visit purpose, city, etc for each person and I know as a fact that all of them were approved, I do not have people who had their visa denied but I also know that they must have not followed the same pattern as the people who had their visa approved.

My question is, how can I frame this problem as a Machine Learning problem and what type of approach could I take?

Thanks