16. A computer implemented method for determining a level of risk of a transaction between a consumer and an entity, the method comprising: storing, by at least one data processor, a plurality of entity clusters, the plurality of entity clusters determined from statistical co-occurrences of the entity identified in a plurality of transactions, the statistical co-occurrences identified using entity identifiers or other high categorical data, the entity comprising a merchant associated with the transaction, the plurality of entity clusters being generated by: selecting a plurality of high categorical information elements, linking each high categorical information element with a context vector in a vector space such that high categorical information elements that co-occur in the plurality of transactions have context vectors that are similarly oriented in the vector space, the co-occurrence representing that context vectors corresponding to the co-occurring high categorical information elements are less than a predetermined distance apart in the vector space for more than a predetermined number of transactions, and clustering the context vectors of the high categorical information elements into a number of entity clusters that is less than number of high categorical information elements, each entity cluster being a low categorical information cluster; receiving, by at least one data processor, data from said transaction between said consumer and said entity; determining, by at least one data processor, an entity cluster of the plurality of entity clusters associated with the entity of the transaction based on at least one or the entity identifier and the other high categorical data; determining, by at least one data processor, an affinity measure indicative of an affinity of a consumer to the entity cluster; and applying, by at least one data processor, the affinity measure in conjunction with data derived from the transaction to a predictive model, the predictive model outputting the level of risk of the transaction to characterize whether the transaction is fraudulent, wherein the affinity measure comprises at least one of transaction frequency, total currency volume, and average transaction amount, which are associated with transactions between the consumer and one or more entities that are included in the entity cluster.
16. A computer implemented method for determining a level of risk of a transaction between a consumer and an entity, the method comprising: storing, by at least one data processor, a plurality of entity clusters, the plurality of entity clusters determined from statistical co-occurrences of the entity identified in a plurality of transactions, the statistical co-occurrences identified using entity identifiers or other high categorical data, the entity comprising a merchant associated with the transaction, the plurality of entity clusters being generated by: selecting a plurality of high categorical information elements, linking each high categorical information element with a context vector in a vector space such that high categorical information elements that co-occur in the plurality of transactions have context vectors that are similarly oriented in the vector space, the co-occurrence representing that context vectors corresponding to the co-occurring high categorical information elements are less than a predetermined distance apart in the vector space for more than a predetermined number of transactions, and clustering the context vectors of the high categorical information elements into a number of entity clusters that is less than number of high categorical information elements, each entity cluster being a low categorical information cluster; receiving, by at least one data processor, data from said transaction between said consumer and said entity; determining, by at least one data processor, an entity cluster of the plurality of entity clusters associated with the entity of the transaction based on at least one or the entity identifier and the other high categorical data; determining, by at least one data processor, an affinity measure indicative of an affinity of a consumer to the entity cluster; and applying, by at least one data processor, the affinity measure in conjunction with data derived from the transaction to a predictive model, the predictive model outputting the level of risk of the transaction to characterize whether the transaction is fraudulent, wherein the affinity measure comprises at least one of transaction frequency, total currency volume, and average transaction amount, which are associated with transactions between the consumer and one or more entities that are included in the entity cluster. 19. The method of claim 16 , wherein the step of determining the affinity measure of an affinity of the consumer to the entity cluster further comprises the steps of: determining, by at least one data processor, a consumer cluster associated with the consumer; and determining, by at least one data processor, an affinity vector that characterizes at least one affinity measures for the consumer cluster in relation to each of a plurality of entity clusters, including the entity cluster of the entity of the transaction.