Adaptive and reusable processing of retroactive sequences for automated predictions

A memory having instructions stored thereon that, in response to execution by a processor, cause the processor to perform operations comprising:

• uploading digital input data for a person associated with a business entity from at least one data source;
• provisioning a plurality of feature synthesizers to synthesize feature data from the input data for each attribute of a set of attributes to form at least one dataset of character feature data associated with the person;
• provisioning a plurality of specialized model builder components, each specialized model builder component configured to build a prediction model for a corresponding one of the attributes;
• training at least some of the specialized model builder components based at least in part on the synthesized feature data to form respective prediction models for the attributes, respectively;
• separately applying each of the trained prediction models to at least a portion of the character feature data associated with the person to generate individual vectors corresponding to attributes, respectively, wherein the individual vectors form a feature vector;
• identifying a plurality of additional feature vectors, each of which includes individual vectors corresponding to the attributes, respectively, for a different person associated with the business entity;
• combine the individual vectors of the feature vector with the individual feature vectors of the additional feature vectors, respectively, to form an aggregate score vector;
• and
• reporting information about the aggregate score vector via a display interface.