Abstract: Techniques are disclosed relating to a graphical user interface (GUI) for editing classification rules. The graphical user interface may include a display of a modified threshold value for a classification rule, where values on one side (e.g. above) the threshold value indicate a first type of classification, and values on the other side (e.g. below) indicate a second type of classification. Machine learning techniques may be used to suggest a modified threshold value to the user, who may accept the modified value, or may provide, via the GUI, their own modified value, which can be different from a suggested value. Graphical indications of accuracy for the classification rule may be displayed.

Abstract: Techniques are disclosed relating to tuning fraud-detection rules using machine learning. In some embodiments, a server system may maintain rule information indicative of a plurality of fraud-detection rules for a transaction system. For example, in some embodiments, the server system may implement a fraud-detection service for the transaction system. In some embodiments, the server system may select a first rule to update, where the first rule includes one or more evaluation criteria and one or more corresponding user-defined threshold values. The server system may apply a machine learning algorithm to training data associated with the transaction system to generate an updated version of the first rule. The server system may then compare a performance of the first rule and the updated version of the first rule and, based on that performance, determine whether to suggest the updated version of the first rule to the transaction system.