comparison of t-SNE and PCA and truncate SVD
PCA and truncate SVD do not differ much, since they are based on
the same theory that the eigenvectors with the less eigenvalue are
discarded. As mentioned here the difference:


TruncatedSVD is very similar to PCA, but differs in that it works on
sample matrices  directly instead of their covariance matrices. When
the columnwise (per-feature) means of  are subtracted from the feature
values, truncated SVD on the resulting matrix is equivalent to PCA. In
practical terms, this means that the TruncatedSVD transformer accepts
scipy.sparse matrices without the need to densify them, as densifying
may fill up memory even for medium-sized document collections.


T-SNE attempts to find the underlying structure of the data by taking
into account the neighbors of a sample. By prioritizing the neighbor
points gives an advantage when data structure is a manifold.
A way to understand the meaning of your figure clusters is described here,
using radar charts. From my point of view, what i would say is that


The information, which predicts the labels Fraud/not Fraud, is not
on    a structure manifold
The samples seems to be linear separated and and that the main information is given by only two features. If you wanted, you could check how much of the eigenvalues energy is retained when keeping only two features