The hands-on lab is about taking users through Oracle Machine Learning (SQL) Notebooks while using multiple use case scenarios for analysing insurance claims data and customer behavior data. Students were running the notebooks themselves to build and apply OML models inside Oracle Autonomous Database and then worked with Oracle APEX applications that leveraged OML’s insights and predictions...
So basically, what we did was: we took the Insurance Claims data set and ran 1-Class SVM algorithm, which is used for anomaly detection in order to identify suspicious claims.
For business (and any other) users we have an option to do the whole process from Oracle Analytics directly. In this case all complexity of machine learning models are hidden from the user and are literally ideal for business users.
Oracle Analytics supports Dynamic Anomaly Detection. In order to use this nice feature, we need to create the following data flow:
Actually, the list of Outputs can be longer. Beside Prediction which tells us if claim is fraudulent or not and Prediction Probability, algorithm can return several attributes which explains the prediction and exposes key attributes important for the prediction.