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Guidance and results for the data-rich, yet information-poor.

Case Study: Building Models Without Historic Data

DATASometimes you are in a position where you have a problem you need to solve, but you do not necessarily have all of the historic data you want or need to do so. You don’t have to allow this scenario to stop you in your tracks. There are always ways to back into a problem.

A large academic institution recently asked TMA to create a predictive modeling surveillance program to detect credit card fraud. The challenge here was that there were no known historical cases of fraud to work from. How, then, to train this model?

TMA solved this problem by using an unsupervised learning approach. The idea was to cluster behaviors based on both distance mapping and multidimensional space, along with pattern matching.

After building the cluster, TMA worked with the users to define the number of clusters that they could work with.

The model was then prepared to stand by for any known cases of fraud to come through the system. The users could then see which segment or cluster the behavior mapped to. They would then target any auditing efforts on that cluster. The model was designed to grow more effective as more cases came in.

Cluster analysis has many uses. It is the same sort of process that Netflix uses to predict your next movie. It’s also the same process Amazon uses to predict your next purchase.

It is, of course, just one tool out of many. If you want to learn how to match the right tool to the right task you need to seek the appropriate predictive analytics training program. Why not start with TMA’s free webinar? Or sign up for training courses today to learn how to solve sticky problems just like this one.

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