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.

Abstract: Disclosed herein are systems and methods for efficiently analyzing data using intelligent methods and systems. In an embodiment, a server identifies an aggregated data associated with an electronic payment system comprising data corresponding to a set of attributes for a set of transactions associated with the electronic payment system, each attribute having one or more corresponding values; generates a set of data slices from the aggregated data, the set of data slices including a first data slice containing data corresponding to a first value of a plurality of values associated with a first attribute of the set of attributes; executes a computer model that receives an input of the aggregated data and an input of at least the first data slice and outputs an identification of an anomaly associated with the electronic payment system.