Hi all,
We are working on a fraud detection project for auto insurance. We have prepared out analytical base table and now we are working on generating derived variables from existing ones.
Do you have any suggestions to us about derived variables?
For example:
Estimated_claim_value / total_payments_amount
Many thanks,
Onur
Here was a response from SAS employee John Stultz:
In case you have not seen this, here is an old but good paper by SAS’ Terry Woodfield (Predictive Modeling in the Insurance Industry Using SAS Software-- http://www2.sas.com/proceedings/sugi26/p013-26.pdf ) that might help give you some ideas on how to create derived fields and use them as model inputs within an Enterprise Miner process flow.
You might also find more current information/examples in Global Forum papers by searching the Online Proceedings: http://supportprod.unx.sas.com/events/sasglobalforum/previous/online.html
And as always, you can usually find a bunch of stuff by searching the internet. For example, here is a list of some derived/binary variables that might be relevant: (https://www.researchgate.net/publication/227540405_Detection_of_Automobile_Insurance_Fraud_With_Disc...)
Characteristics of the Insured/Claimant/Policy:
Characteristics of the Vehicle:
Characteristics of the Accident:
Are you ready for the spotlight? We're accepting content ideas for SAS Innovate 2025 to be held May 6-9 in Orlando, FL. The call is open until September 25. Read more here about why you should contribute and what is in it for you!
Use this tutorial as a handy guide to weigh the pros and cons of these commonly used machine learning algorithms.
Find more tutorials on the SAS Users YouTube channel.