Systems and methods for generating behavior profiles for new entities

A system comprising:

• at least one processor configured to:
• in response to and immediately after a new entity being added to a fraud detection system, wherein the new entity does not have a behavior history for generating a behavior profile from which to detect anomalous behavior for the new entity:
• generate a doppelganger behavior profile for the new entity, wherein, when generating the doppelganger behavior profile, the at least one processor is configured to:
• generate a plurality of similarity scores for each entity of a plurality of pre-existing entities based on inputting a portion of feature data of the new entity and a portion of feature data of the plurality of pre-existing entities into a plurality of machine-learning models, wherein each similarity score of the plurality of similarity scores defines a similarity between the new entity and an entity of the plurality of pre-existing entities based on an output of a machine-learning model of the plurality of machine-learning models;
• rank the plurality of pre-existing entities to produce a ranking;
• select two or more pre-existing entities of the plurality of pre-existing based on the ranking;
• and
• combine behavior profiles of the two or more preexisting entities to produce the doppelganger behavior profile for the new entity;
• assign the doppelganger behavior profile to the new entity;
• detect at least one anomaly in an activity of the new entity based on the doppelganger behavior profile assigned to the new entity;
• and
• in response to detection of the at least one anomaly, automatically cancel, at a transaction processing server, a current transaction being attempted by the new entity.

A computer program product comprising at least one non-transitory computer-readable medium including program instructions that, when executed by at least one processor, cause the at least one processor to:

• in response to and immediately after a new entity being added to a fraud detection system, wherein the new entity does not have a behavior history for generating a behavior profile from which to detect anomalous behavior for the new entity:
• generate a doppelganger behavior profile for the new entity, wherein the program instructions that cause the at least one processor to generate the doppelganger behavior profile cause the at least one processor to:
• generate a plurality of similarity scores for each entity of a plurality of pre-existing entities based on inputting a portion of feature data of the new entity and a portion of feature data of the plurality of pre-existing entities into a plurality of machine-learning models, wherein each similarity score of the plurality of similarity scores defines a similarity between the new entity and an entity of the plurality of pre-existing entities based on an output of a machine-learning model of the plurality of machine-learning models;
• rank the plurality of pre-existing entities to produce a ranking;
• select two or more pre-existing entities of the plurality of pre-existing based on the ranking;
• and
• combine behavior profiles of the two or more preexisting entities to produce the doppelganger behavior profile for the new entity;
• assign the doppelganger behavior profile to the new entity;
• detect at least one anomaly in an activity of the new entity based on the doppelganger behavior profile assigned to the new entity;
• and
• in response to detection of the at least one anomaly, automatically cancel, at a transaction processing server, a current transaction being attempted by the new entity.