A system that aggregates client data and cyber indicators to authenticate a client, the system comprising: a memory that stores and maintains a plurality of client profiles; and a computer server comprising at least one computer processor and coupled to the memory, the computer server configured to: receive, via an electronic input, an authorization request from a requester for access to an account and to perform one or more actions related to the account; identify a client identifier associated with the authorization request; using the client identifier, retrieve, from the memory, a client profile, wherein the client profile is based on an aggregation of client data, client device data, claims data and cyber data; the client data comprises client account data, client behavior data and transaction activity data; the client device data comprises device attributes associated with the authorization request; the claims data comprises pending and prior data relating to one or more reports of improper activity, wherein the claims data is financial fraud data; and the cyber data comprises an indication of whether the client's personally identifiable information has been compromised; generate a risk score based on an aggregated combination of the client data, client device data, claims data and cyber data to determine whether the requester is authenticated to access the account; automatically apply an authentication determination to the request to perform one or more actions related to the requested account; and apply machine learning to the authentication determination, wherein the machine learning analyzes the requestor's behavior and transaction history to determine if the requestor's behavior is outside of an expected behavior range and updates the risk score based on the determination, and applies feedback to reduce improper denials of authorization requests. a memory that stores and maintains a plurality of client profiles; and a computer server comprising at least one computer processor and coupled to the memory, the computer server configured to: receive, via an electronic input, an authorization request from a requester for access to an account and to perform one or more actions related to the account; identify a client identifier associated with the authorization request; using the client identifier, retrieve, from the memory, a client profile, wherein the client profile is based on an aggregation of client data, client device data, claims data and cyber data; the client data comprises client account data, client behavior data and transaction activity data; the client device data comprises device attributes associated with the authorization request; the claims data comprises pending and prior data relating to one or more reports of improper activity, wherein the claims data is financial fraud data; and the cyber data comprises an indication of whether the client's personally identifiable information has been compromised; generate a risk score based on an aggregated combination of the client data, client device data, claims data and cyber data to determine whether the requester is authenticated to access the account; automatically apply an authentication determination to the request to perform one or more actions related to the requested account; and apply machine learning to the authentication determination, wherein the machine learning analyzes the requestor's behavior and transaction history to determine if the requestor's behavior is outside of an expected behavior range and updates the risk score based on the determination, and applies feedback to reduce improper denials of authorization requests. receive, via an electronic input, an authorization request from a requester for access to an account and to perform one or more actions related to the account; identify a client identifier associated with the authorization request; using the client identifier, retrieve, from the memory, a client profile, wherein the client profile is based on an aggregation of client data, client device data, claims data and cyber data; the client data comprises client account data, client behavior data and transaction activity data; the client device data comprises device attributes associated with the authorization request; the claims data comprises pending and prior data relating to one or more reports of improper activity, wherein the claims data is financial fraud data; and the cyber data comprises an indication of whether the client's personally identifiable information has been compromised; generate a risk score based on an aggregated combination of the client data, client device data, claims data and cyber data to determine whether the requester is authenticated to access the account; automatically apply an authentication determination to the request to perform one or more actions related to the requested account; and apply machine learning to the authentication determination, wherein the machine learning analyzes the requestor's behavior and transaction history to determine if the requestor's behavior is outside of an expected behavior range and updates the risk score based on the determination, and applies feedback to reduce improper denials of authorization requests.