Methods and systems for authorization rate gradual degradation detection

Abstract

Described herein are systems and methods to use modeling techniques to identify gradual changes in various metrics identified as a result of analyzing an aggregated transaction dataset. In one method, a computer model dynamically slice the data using an attribute, calculates an entropy value for using a rolling time window, and uses the entropy value to identify anomalous behavior. The model may use information gain to determine whether to further segmented the data slice into smaller data slices. The model may iteratively slice and analyze the data until a data slice corresponding to the root cause is determined. The model may then traverse the hierarchy of data slices and combine the data slices until an optimized combined data slice. The model may train a machine learning component, such as a booted tree algorithm, to optimize its traversal of the hierarchy of data slices.