Bank of America Corporation
SYSTEM AND METHODS FOR EXPLAINABILITY ENSEMBLES FOR NEURAL NETWORK ARCHITECTURES IN REGULATED APPLICATIONS

Abstract:

A system for analyzing machine learning-derived misappropriation types with an array of shadow models is provided. The system comprises: a controller configured for analyzing an output of a machine learning model, the controller being further configured to: input interaction data into a machine learning model, wherein the interaction data is analyzed using the machine learning model to determine a misappropriation type output associated with the interaction data; identify data features in the interaction data associated with the misappropriation type output; construct an array of shadow models based on the data features, wherein each individual model in the array of shadow models is configured to extract logical constructs from a portion of the data features; and consolidate the logical constructs output by the array of shadow models, wherein consolidating the logical constructs determines a final explanation output for the misappropriation type output determined by the machine learning model.