A. K-Nearest Neighbors (KNN), because it is simple to implement and doesn't require extensive training.

B. Support Vector Machine (SVM) with a radial basis function (RBF) kernel, as it can capture complex non-linear relationships without concern for interpretability.

C. Decision Tree or Random Forest, combined with techniques like oversampling the minority class (fraudulent transactions) within Snowflake using SQL or UDFs to balance the dataset before training. These models provide reasonable interpretability and can handle non-linear relationships effectively.

D. Linear Regression, because it's computationally efficient and easy to understand, even though fraud detection is a classification problem.

E. Naive Bayes, because it requires no hyperparameter tuning and works well on numerical data.