Systems and methods for call fraud analysis using a machine-learning architecture and maintaining caller ani privacy

Abstract

Disclosed are systems and methods including processes executed by a server that executes software routines for machine-learning architectures that receive call-invite messages containing data from a terminating carrier. The server a caller ANI and types of call data. The server further requests data from a telephony database. The server applies and executes the software programming of the machine-learning architecture on the call data (from the terminating carrier) and the portability data (from the telephony database) to generate risk scores. The server stores the data and the risk scores into a request database, until a provider server requests the risk scores in a threat assessment request. The server returns a threat assessment message to the provider server in response to the threat assessment request. The threat assessment message includes information about the caller or caller device, and the risk scores, but not the caller ANI.

Dependencies are inferred from the claim text.