I recently completed an ideation contest on CrowdANALYTIX where the participants had to build an approach towards warranty pricing and fraud detection.
Ideation contests are quite different from the usual data mining contests where the objective is solely to minimize the error (or maximize the accuracy). They are centered more around defining the problem and conceptualizing it with a framework.
In the contest, we had to structure the problem first with respect to the business and then extend it to provide possible analytical solutions for optimizing warranty prices and detecting fraud, including potential data that we would require. Having no experience in either of these areas (warranty and fraud), I tried to draw parallels between insurance pricing and warranty pricing keeping the fundamental differences between them in mind.
Luckily, my approach was chosen to be one of the finalists and was posted on their website.
Ideation contests are quite different from the usual data mining contests where the objective is solely to minimize the error (or maximize the accuracy). They are centered more around defining the problem and conceptualizing it with a framework.
In the contest, we had to structure the problem first with respect to the business and then extend it to provide possible analytical solutions for optimizing warranty prices and detecting fraud, including potential data that we would require. Having no experience in either of these areas (warranty and fraud), I tried to draw parallels between insurance pricing and warranty pricing keeping the fundamental differences between them in mind.
Luckily, my approach was chosen to be one of the finalists and was posted on their website.
