If you review a customer as genuine then Ravelin will always return ALLOW for that customer. They will always be able to transact, however high their machine learning score, however strongly connected to fraud networks, however many chargebacks they may have. Any rules that you configure will not change this. Any blacklists do not apply. A customer reviewed as genuine will always be allowed to transact.
So be very cautious reviewing people as genuine. Only do so when you are certain they are genuine. Don’t just say they’re genuine because their name matches their email or they live in a nice part of town. And only review them as genuine when necessary. If they are such an important customer that you cannot risk losing their business because of an incorrect fraud block then review them as genuine. If the system has blocked them, they have complained and you have made additional checks then review them as genuine. But beware of people who phone up and just seem like a nice person: some fraudsters are very willing to call up and demand to be allowed to transact. Always make additional checks to confirm who they are.
If you do review someone as genuine, then please do leave a comment on the customer explaining why. Comments can be very helpful to Ravelin’s machine learning engineers as they try to improve the system. And they’ll help the next person from your company who looks at that customer record.
Reviewing as fraudster carries somewhat less risk than reviewing as genuine. Potential future payments from that customer will be lost, and you may also block other customers connected to that customer in the network. You should still use caution and care when reviewing as fraudster. As well as any losses to your company, incorrect fraud reviews may eventually cause damage to your machine learning model.
Similar to a genine review, a fraudster review is all-powerful. That customer will never be able to transact again unless you remove the review. No rules you set will change this (Ravelin personnel are able to install rules that do change this - contact us if you have a need for this).
Beware of reviewing based on theories that haven’t been tested. One common misconception is that an email address with a lot of numbers is a sign of a customer that is likely to commit fraud. The data does not appear to support this conclusion. If you do have a theory about patterns that fraudsters follow then please let us know so we can evaluate it, and consider it for inclusion in our machine learning models.
As with genuine reviews, please do leave comments with your fraud reviews. These are invaluable to Ravelin, to other people at your company and potentially to you at a later date. Explain why you reviewed this customer as a fraudster and any additional evidence you might have found.