| CPC H04L 63/1433 (2013.01) [G06F 16/219 (2019.01); G06F 21/64 (2013.01); G06F 40/40 (2020.01); G06N 20/00 (2019.01); G06V 30/414 (2022.01); H04L 67/01 (2022.05); G06V 30/10 (2022.01)] | 20 Claims |
|
1. A system comprising a server computer comprising:
one or more processors;
one or more non-transitory memories coupled to the one or more processors, the one or more memories storing:
a database comprising event data for a plurality of historical actions that were allegedly performed by a respective plurality of target individuals, wherein the historical actions comprise one or more of credit instruments applied for, purchases made, or fraudulent transactions conducted; and
a plurality of instructions executable by the one or more processors, the plurality of instructions comprising instructions that when executed by the one or more processors cause the one or more processors to perform processing comprising:
receiving a request from a target individual to modify event data for a historical action, the request comprising information about the target individual and information about the historical action, wherein the request corresponds to a contention that the target individual did not perform the action;
generating a first security assessment score by, at least, applying a first set of machine learning models to the information about the target individual obtained from the request, the information about the historical action, and information about the target individual obtained from the database;
retrieving, from the database, event data associated with prior event data modification requests made by the target individual, wherein the prior event data modification requests correspond to contentions that additional event data is inaccurate;
computing a second security assessment score by, at least, applying a second machine learning model to the retrieved event data, wherein the second machine learning model has been trained using labeled training data of the event data, and wherein the second machine learning model is augmented with an optimization model that has been trained using unlabeled training data of the event data;
generating an overall security assessment score for the request based on the first security assessment score and the second security assessment score, wherein the overall security assessment score indicates whether the request to modify the event data corresponds to credit washing; and
providing the overall security assessment score for the request to a client computer; and
the client computer, wherein the client computer is configured for preventing, based on the overall security assessment score, the target individual from accessing a resource.
|