Multivariable real time system for fraud detection This is the scenario:

Client -&gt; Server


The client sends multiple voice calls to Server.
Call info:


calling number 
called number
call duration
source IP 
destination IP
SIP header information (User-Agent, Version, Proxy information)


Other patterns:


Numeric Patterns - Destination.
Time Patterns - Calls occurring too regularly, activity at unusual times or dates.
Geographic Patterns - Proximity relationships between apparently unrelated entities.


In case of Toll fraud and client being insecure, the attacker can send calls via original agent hence the Service Provider won't reject calls immediately at least based from IP information. (Toll fraud).
I'm exploring which approach is the best to implement an ML model, instead of static rules to be able to detect Toll fraud in a live system:


Classification (Fraud or not)
Reinforcement Learning (If fraud then reward)


I'm new to RL, does this scenario makes sense for live systems?
Can I use offline data to train an RL model? Offline data includes Fraud cases detected historically.

Reference.
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.