Please note: Having accurate reviews is far more important than having a high number of reviews. Only do reviews you are very confident about even if that means doing less. If you aren't sure, think about using Potential Manual Reviews. Instead of using a genuine review, you could also consider forgiving a specific chargeback.
One of the ways that our model can receive feedback is via manual reviews (another way is through chargebacks). Manual reviews at Ravelin refer to an explicit intervention by an analyst or member of your team to mark a customer as genuine or fraudulent regardless of the Ravelin action.
Machine learning is based on the idea that an algorithm can teach itself to automatically identify patterns in significant volumes of data. It uses statistical techniques to make highly accurate predictions about events in the real world.
With support and feedback from human experts, such as fraud analysts, a machine learning model can be continuously enhanced and optimised to tackle big problems. Algorithms like the one we use at Ravelin can become even more accurate at making predictions as they consume more data and receive feedback on previous forecasts.
There are two main reasons why doing manual reviews can be beneficial.
Reviewing Customers as Genuine
If you review a customer as genuine then Ravelin will always return ALLOW for that customer, unless the customer goes on to receive an unforgiven chargeback (in which case we will return PREVENT). In all other cases 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.
We advise you to be very cautious when adding a genuine manual review - only do so when you are certain they are genuine and when it is necessary to do so. For example, if the customer is very important and you cannot risk losing their business because of an incorrect fraud block. Or if a customer has complained and you have made additional checks. But beware as some fraudsters call up and demand to be allowed to transact. Always make additional checks to confirm who they are.
Remember, genuine manual reviews will minimise similar customers being prevented by your machine learning model.
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 Customers as Fraudsters
If you mark someone as a fraudster, they will be marked as such for all future orders and prevented from transacting.
Reviewing as a fraudster carries somewhat less risk than reviewing as genuine. However, 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 a fraudster. As well as any losses to your company, incorrect fraud reviews may eventually cause damage to your machine learning model.
Remember, manual reviews stay active for customers and can only be overturned by another review. 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).
Please note, fraudster manual reviews will maximise similar customers being prevented by your machine learning model.
As with genuine reviews, please 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.