6. A method for implementation by one or more data processors and comprising: receiving, by at least one data processor, data characterizing a plurality of health care transactions, each transaction including at least one service provided to a client by a provider; retrieving, by at least one data processor, co-occurrence data, the co-occurrence data indicating an historical likelihood that a client will receive both a first service and a second service, the co-occurrence data being based on a vector space modeling of historical service co-occurrence in which performed service codes are represented in a vector space derived from the statistical distribution of service codes in reimbursement claims; retrieving, by at least one data processor, client historical data, the historical data including a plurality of historical services received by the client; for each of the plurality of historical services in the client historical data, determining, by at least one data processor, from the co-occurrence data a likelihood of the provided service co-occurring with the historical service in order to determine an overall inconsistency metric; and responsive to the overall inconsistency metric exceeding a threshold, indicating, by at least one data processor, that the transaction is potentially fraudulent.
7. The method of claim 6 , wherein determining the likelihood of the provided service co-occurring with the historical service in order to determine the overall inconsistency metric further comprises: determining, by at least one data processor for each combination of the provided service and a historical service received by the client, an inconsistency value; and determining, by at least one data processor, a metric of the inconsistency values.