4. 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 from the plurality of transactions from the plurality of transactions, 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 linked to the entity of the transaction based on at least one or the entity identifier and the other high categorical data; and applying, by at least one data processor, the determined entity cluster in conjunction with data derived from the transaction to a predictive model, the predictive model outputting a level of risk of the transaction to detect if the transaction is fraudulent.
4. 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 from the plurality of transactions from the plurality of transactions, 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 linked to the entity of the transaction based on at least one or the entity identifier and the other high categorical data; and applying, by at least one data processor, the determined entity cluster in conjunction with data derived from the transaction to a predictive model, the predictive model outputting a level of risk of the transaction to detect if the transaction is fraudulent. 13. The method of claim 4 , further comprising the steps of: storing, by at least one data processor, for each entity cluster at least one summarized transaction statistic, descriptive of transactions occurring at entities in the entity cluster; and applying, by at least one data processor, the at least one summarized transaction statistic of the entity cluster to the predictive model.