Bank of America Corporation

John R Sampson

David L Yommer

Brad Romano

Shashi Thanikella

An adaptive machine learning system for predicting file controls includes a memory, an interface, and a processor. The memory stores a plurality of controls for incoming files and the interface receives a first file and a second file. The first file has a first property and the second file has a second property. The processor determines a type for each of the first property and the second property, wherein the type of each property is related to a first file control. The processor also determines that the first property and the second property each satisfy the first file control. If the value of the first property and the second property are above a first threshold, the processor changes a value of the first control for incoming files.

Machine learning method and system for predicting file controls

Methods, systems, and computer-readable media for detecting errors within a system by using behavior profiles are presented. At a first time, user requests may be received and serviced. The serviced user requests may be logged. Based on the logged user requests, profiles may be determined. At a second time, user requests may be received and serviced. The serviced user requests may be logged. The logged serviced user requests may be compared to the profiles determined at a first time. For example, the determined profiles may include an error rate for serviced user requests. At the second time, an error rate for the logged serviced user requests may be compared to an error rate included the determined profiles. Serviced users requests may be flagged based on the comparison.

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Charlotte, NC, United States of America

John R Sampson