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SAP HANA Academy – APL: Predicting Auto Insurance Claim Fraud [2.4]

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In this video, part of the SAP Automated Predictive Library (APL) for SAP HANA series, we will use the APL to predict auto insurance claim fraud.

This example shows how an insurance company assesses past insurance frauds in order to create a category of client characteristics that may be susceptible to make fraudulent claims.

The first step of the analysis is to prepare the main input tables, one containing data that has already been analyzed, that contains some known fraud cases. This table is used to train the model. The results are used to indicate which variable(s) to use as the target and describe the claims data.

After considering past data and past fraudulent claims, the customer uses the data to train the APL model on that date produce an updated model that will be applied to the new data in order to detect potential fraud risks.

After training the model, the APL function returns summary information regarding the model as well as indicators like the Predicitve Power (KI) of the model, or the Prediction Confidence of the results (KR).

At the end of the data mining process, the “Apply Model” function produces scores in the form of a table that can be queried.

This video was created using SAP Automated Predictive Library 2.4 and SAP HANA SPS 10.

Code samples on Github: https://github.com/saphanaacademy/APL

Video by the SAP HANA Academy

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