The features of IBM Safer Payments that I found most valuable are the "what if" scenarios. I find "what if" scenarios very valuable for my use cases because it's easier to simulate the rules that one would want to set up and see what the outcome would be beforehand; it doesn't take too much rework or effort. I have used the transaction scoring feature of IBM Safer Payments. IBM Safer Payments adaptive machine learning models are still under review for improving the accuracy of fraud detection over time; it's not yet been fully realized because the more data you have, the better it is. Currently, the data is being populated, and it's too early to make definitive conclusions. The ability to process large volumes of transactions in real-time for my organization is very important; that will become even more important once we are doing real-time monitoring and scoring. I have benefited from the comprehensive reporting and visualization tools offered by IBM Safer Payments; they are lovely. These tools have assisted in optimizing my fraud prevention strategies because we are still building on a few things, but once you have a 360 view of the customer's transactional history, it's easier to build in rules for flagging anything that comes out of the ordinary, whether it's in terms of the number of transactions, the volume of the transactions, the location of the transactions, or all of them together. It brings in a lot of visibility into the transactional history and patterns for each customer.
