How are you using Watson at Nedbank? Traditionally, Nedbank has reduced rates of online fraud by using rule-based decision systems. Every time a fraud was committed, new rules were added. Unfortunately, this created a large catch-all problem where responders would see many false alarms, which would encumber resources and divert attention away from actual fraud. By using machine learning, developed within the Watson Local development environment, we were able to drop the false positive rate from 60–80% to below 35%. Watson helped with easy collaboration for model development and simplified the deployment process by letting us build a production-ready API with ease.