21. A method for implementation by one or more data processors comprising: receiving, by at least one data processor, data characterizing a transaction of transactions between a consumer and merchant; linking, by at least one data processor, the merchant with one of a plurality of pre-defined merchant clusters, the merchant clusters defined by a frequency at which merchants historically shared customers, the merchant clusters being generated by: selecting a plurality of high categorical information elements from the 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 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 merchant clusters that is less than number of high categorical information elements, each merchant cluster being a low categorical information cluster; determining, by at least one data processor, an affinity of the customer to an associated merchant cluster; generating, by at least one data processor, a risk score using at least one predictive model and at least the determined affinity; and initiating, by at least one data processor, provision of data characterizing the generated score.
21. A method for implementation by one or more data processors comprising: receiving, by at least one data processor, data characterizing a transaction of transactions between a consumer and merchant; linking, by at least one data processor, the merchant with one of a plurality of pre-defined merchant clusters, the merchant clusters defined by a frequency at which merchants historically shared customers, the merchant clusters being generated by: selecting a plurality of high categorical information elements from the 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 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 merchant clusters that is less than number of high categorical information elements, each merchant cluster being a low categorical information cluster; determining, by at least one data processor, an affinity of the customer to an associated merchant cluster; generating, by at least one data processor, a risk score using at least one predictive model and at least the determined affinity; and initiating, by at least one data processor, provision of data characterizing the generated score. 27. A method as in claim 21 , wherein the determined affinity is based on an affinity vector which is a combination of an affinity measure for the consumer in relation to each of the plurality of pre-defined merchant clusters.