Continuous identity

A method for determining continuous identity in real-time comprising:

• receiving at a first time, via computer network, a request to verify the identity of an individual including a plurality of partial identifiers provided with the request;
• accessing a first database storing a plurality of identification credentials for each of a plurality of individuals,
• wherein different types of identification credentials may be stored and accessed within the first database for different individuals of the plurality of individuals,
• wherein a plurality of the different types of identification credentials for any individual of the plurality of individuals include various types of information that are not overlapping,
• wherein one or more of the different types of identification credentials for any individual of the plurality of individuals might be expired,
• wherein one or more of the different types of identification credentials for any individual of the plurality of individuals might not be expired;
• accessing a second database storing information regarding the strength of relationships between the plurality of identification credentials stored in the first database;
• applying a machine learning algorithm to the plurality of partial identifiers, the plurality of identification credentials, and the strengths of relationships, to determine whether the identity of the individual can be verified, refuted, or neither,
• wherein the machine learning algorithm establishes and applies weights to various of the relationships based upon training and feedback,
• wherein the machine learning algorithm applies a tunable risk tolerance to the determination;
• and
• transmitting at a second time, a response to the request to verify the identity,
• wherein the rate at which the machine learning algorithm processes a plurality of individuals scales substantially linearly over time with application of an equal number of resources and requests to verify.

A non-transitory computer-readable storage medium comprising:

• instructions that, when executed by a device comprising a processor, facilitate performance of operations comprising:
• receiving at a first time, via computer network, a request to verify the identity of an individual including a plurality of partial identifiers provided with the request;
• accessing a first database storing a plurality of identification credentials for each of a plurality of individuals,
• wherein different types of identification credentials may be stored and accessed within the first database for different individuals of the plurality of individuals,
• wherein a plurality of the different types of identification credentials for any individual of the plurality of individuals include various types of information that are not overlapping,
• wherein one or more of the different types of identification credentials for any individual of the plurality of individuals might be expired,
• wherein one or more of the different types of identification credentials for any individual of the plurality of individuals might not be expired;
• accessing a second database storing information regarding the strength of relationships between the plurality of identification credentials stored in the first database;
• applying a machine learning algorithm to the plurality of partial identifiers, the plurality of identification credentials, and the strengths of relationships, to determine whether the identity of the individual can be verified, refuted, or neither,
• wherein the machine learning algorithm establishes and applies weights to various of the relationships based upon training and feedback,
• wherein the machine learning algorithm applies a tunable risk tolerance to the determination;
• and
• transmitting at a second time, a response to the request to verify the identity,
• wherein when executing the instructions, the rate at which the machine learning algorithm processes a plurality of individuals scales substantially linearly over time with application of an equal number of resources and requests to verify.