Multivariable real time system for fraud detection
I think it makes sense to stick with Classification here, since you already have examples of fraudulent and non-fraudulent calls that you can train on.

It might also be beneficial to train several models for different regions based on IP, as well as your 'global' model, and apply the region specific and global models to incoming calls. Just a few ideas.

From what I understand, real-time learning would require immediate feedback, which most fraud detection systems can't provide. For example, it may take a few days or weeks to have a case of fraud resolved (labelled fraud/not-fraud), and therefore take some time for the learning system to receive the feedback on it's prediction.