1. A computer-implemented method comprising: acquiring, by a computing system, historical data including a plurality of features associated with known legitimate activities and with known illegitimate activities, wherein at least some of the known legitimate activities and the known illegitimate activities include one or more financial transactions; applying, by the computing system, a machine learning technique to the historical data to gain information about the plurality of features associated with the known legitimate activities and with the known illegitimate activities; generating, by the computing system, a decision tree based on at least a portion of the information about the plurality of features; identifying, by the computing system, a node in the decision tree that satisfies specified precision criteria; creating, by the computing system, a rule based on the node identified in the decision tree, wherein the rule corresponds to a conditional rule which indicates that a particular activity is illegitimate when one or more features associated with the particular activity respectively meet one or more feature values specified by the rule; and identifying, by the computing system, one or more illegitimate activities based on the rule, wherein applying the machine learning technique to the acquired historical data to gain the information about the plurality of features further comprises: determining a respective information gain for each feature in the plurality of features; identifying a feature having a highest information gain; and selecting a feature value, for the feature, that partitions the historical data into a first data subset and a second data subset, wherein the feature value is selected such that a largest possible amount of known legitimate activities is in the first data subset and a largest possible amount of known illegitimate activities is in the second data subset.