1. A computer implemented method of predicting financial behavior of a target consumer with respect to an offer or merchant, comprising: for a reference set of consumers, obtaining consumer vectors and data describing financial behavior; obtaining a consumer vector for the target consumer; identifying at least one nearest neighbor to the target consumer vector among the reference set of consumers; and generating a financial behavior prediction for the target consumer by aggregating the financial behavior data of the consumers corresponding to the identified consumer vectors; wherein generating a behavior prediction comprises: training a predictive model using a plurality of consumer vectors, corresponding financial behavior data, and merchant vectors; using an unexpected deviation learning approach to determine values of the merchant vectors; wherein said unexpected deviation learning approach comprises comparing co-occurences of merchant descriptions in said financial behavior data to determine if a pair of merchants are either positively or negatively concurrent wherein either the positive or negative concurrency is used to determine values for the merchant vectors; and applying the predictive model to the consumer vector of the target consumer to output for said target consumer a predicted spending amount; and wherein identifying at least one nearest neighbor comprises identifying consumer vectors having a dot product between the consumer vector and the target consumer vector that exceeds a predetermined threshold.