New Machine Learning Model

By Kristin Hoyne Gomes, Director Decision Sciences May 22, 2015

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Want to boost your decisioning effectiveness? Accertify is pleased to announce that we can now offer clients the ability to implement a new statistical model type built with machine learning to help reduce your review rate and improve your fraud capture rate.

This new machine learning model type leverages a proprietary algorithm from the Gradient Boosting (GBM) technique. Gradient boosting is a machine learning technique for regression problems, which produces a prediction model in the form of an ensemble of models. In the Accertify algorithm, the model ensemble is made of a series of decision trees. Each subsequent decision tree helps to optimize the decision of the prior tree, with the final result having a review by all trees.

In a recent client test run, GBM models have enabled improved decisioning over and above that of logistic regression models.

In addition, Accertify has implemented a new model reason capability that gives client fraud analysts clarity into the probability calculation from the model for each transaction.

This service is available through a consulting engagement with Accertify's Decision Science team.

Want to learn more, please contact Kristin Hoyne Gomes at khgomes@accertify.com or your Accertify Account Manager.

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About the Author

Kristin has over 17 years experience working in risk management and payments. At Accertify she leads a team responsible for improving risk decisioning.