Using Apache Ignite's Machine Learning for Fraud Detection at Scale - DZone AI

Our initial results look promising, but there is room for improvement. We made a number of choices and assumptions for our initial analysis. Our next steps would be to go back and evaluate these to determine what changes we can make to tune our classifier. If we plan to use this classifier for a real-time credit card fraud detection system, we want to ensure that we can catch all the fraudulent transactions and also keep our customers happy by correctly identifying non-fraudulent transactions. Once we have a good classifier, we can use it directly with transactions arriving into Ignite in real-time. Additionally, with Ignite's continuous learning capabilities, we can refine and tune our classifier further with new data, as the data arrive. Finally, using Ignite as the basis for a real-time fraud detection system enables us to obtain many advantages, such as the ability to scale ML processing beyond a single node, the storage and manipulation of massive quantities of data, and zero ETL.