公开(公告)号：US20210142126A1

公开(公告)日：2021-05-13

申请号：US16682147

申请日：2019-11-13

Applicant: Oracle International Corporation

Inventor： William M. WARRICK, II ,  Su-Ming WU ,  Stephen CLEGG ,  Randall FERNANDES

IPC: G06K9/62 ,  G06N20/20 ,  G06F16/28 ,  G06N20/10

Abstract: Embodiments detect fraud of risk targets that include both customer accounts and cashiers. Embodiments receive historical point of sale (“POS”) data and divide the POS data into store groupings. Embodiments create a first aggregation of the POS data corresponding to the customer accounts and a second aggregation of the POS data corresponding to the cashiers. Embodiments calculate first features corresponding to the customer accounts and second features corresponding to the cashiers. Embodiments filter the risk targets based on rules and separate the filtered risk targets into a plurality of data ranges. For each combination of store groupings and data ranges, embodiments train an unsupervised machine learning model. Embodiments then apply the unsupervised machine learning models after the training to generate first anomaly scores for each of the customer accounts and cashiers.