The problem I was facing, from a machine learning perspective, it only had a supervised learning capability. You would have to provide your data live, but in fraud, the pattern of the fraudsters keeps changing and it's impossible to provide data labels. That's where the user unsupervised learning comes in handy — you don't have to tell them, "okay, this is fraud and this is not fraud." 

If unsupervised learning was also incorporated with Amazon SageMaker, that would be really cool. I am talking about anomaly detection algorithms, like isolation, forest, or anything on the neural network side for anomaly detection, including autoencoders. These are some things which companies would really like to use.

There was also a problem with latency. In fraud detection, everything needs to be happening in real-time, but some of the algorithms ran for three to four minutes, which is not a viable option.