Example Scenario: SAP Predictive Analytics, HANA APL(Automated Predictive libraries):Classification

Fellow SCN Predictive Enthusiasts,

The attempt of this post is to make you familiar with the process of building predictive models using APL (Automated predictive libraries) through an example.

For those who have not heard of APL yet – SAP APL is a native C++ implementation of the automated predictive capabilities of SAP Predictive Analytics running directly in SAP HANA. The key differentiator for the SAP APL over other predictive components within SAP HANA is the “A” for “automated”.  Using APL you can run real time automated predictive algorithms on your data stored in SAP HANA without requiring a data extraction process.

Another advantage of APL based model is, it simply needs to be set up and be instructed what type of data mining function needs to be applied. APL then takes over from there by composing its own models, creating and selectively eliminating metadata as required, and ultimately come up with the most optimal model given the data we provided – in a mostly automated way.

I have put together a document which shows a step by step example, how an insurance company can analyze past insurance fraud data in order to create a predictive model in SAP HANA using the Automated Predictive Libraries (APL) to identify potential future fraudulent auto insurance claims.

You can also see this example in action in this recorded webinar in it, we cover an overview of the predictive analytics in SAP HANA and a live demonstration of SAP Predictive Analytics and the APL in action.