This "learn by example" tip walks you through a classic credit scoring data mining flow. Use this link to download the files you need from SAS Software's github. You can import the XML into SAS Enterprise Miner to recreate a flow diagram. Use the PDF file of each example to read more details on how to build your flow diagram step by step.
In a nutshell, you create the basic Credit Scoring flow for the CS_ACCEPTS data set using these nodes:
As a bonus, you create a Reverse Scorecard. With one quick property change, you change the order of the scale for your scorecard. Usually the higher the score, the less likely you expect to see an event (payment default). In a reverse scorecard it is the opposite, the higher the score the more likely you expect to see an event.
Another example in the same repository walks you through a flow diagram for Reject Inference. You can use use this diagram to account for sample bias, as long as you have a Rejects data set.
As part of the documentation of the repository for this example, the PDF document briefly describes the fuzzy method for reject inference. Find more information about other methods in the Credit Scoring section of the Reference Help, and a visual explanation of these methods in the video, Reject Inference in SAS Enterprise Miner:
Feel free to post questions or comments!
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