1. A method for identifying related transaction records from a database storing transaction records for multiple entities performed by one or more processors of a computer system, the method comprising: acquiring a plurality of transaction record sets, wherein each transaction record set includes one or more of the transaction records sharing a common attribute value and wherein the transaction records are credit or debit transactions processed by a financial institution for a merchant; receiving a selection of or selecting an exemplar record set of the plurality of transaction record sets, wherein the exemplar record set comprises a plurality of the transaction records associated with a first entity of the multiple entities; for at least one of the acquired plurality of transaction record sets: determining a probability that the transaction record set stores transaction records associated with the first entity based at least in part on a machine learning classifier, the machine learning classifier being trained using one or more first pairs of transaction record sets and one or more second pairs of transaction record sets, wherein a first pair of transaction record sets represents a common entity and a second pair of transaction record sets represents unrelated entities, and upon determining the probability exceeds a threshold, resolving the transaction record set as storing transaction records associated with the first entity, the resolving including merging the transaction records of the transaction record set into the exemplar record set that comprises the plurality of the transaction records associated with the first entity.