Principal component analysis, as it reduces the dimensionality of the dataset, making it easier to visualize and understand the distribution.

Central limit theorem, because it allows the approximation of the distribution of sample means to a normal distribution, facilitating inference about the population from sample data.

Linear regression, for modeling the relationship between transaction amounts and the likelihood of fraud.

K-means clustering, to group similar transactions together based on their features.