A system for processing and monitoring trade data, the system comprising a computing device having at least one processor (1XX1101), the computing device configured to: receive data representing a set of trade activities; transform the data representing the set of trade activities to a musical piece representation; convert the musical piece representation to a metrical tree, wherein notes of the musical piece representation are recorded in labeled leaf nodes of the metrical tree, and the labeled leaf nodes are connected to unlabeled internal nodes; set an initial default value of a score representing a probability of trade irregularity; compare the metrical tree to a plurality of trigger trees stored in a lessons database (2TX0120), each trigger tree of the plurality of trigger trees representing a trade irregularity; identify at least one trigger tree of the plurality of trigger trees indicating at least a closest match to the metrical tree using a nearest-neighbors algorithm, wherein using the nearest-neighbors algorithm further comprises: computing distances between trees using match values, wherein the distance from a trigger tree of the plurality of trigger trees to the metrical tree is an average chance of finding a labeled leaf from the trigger tree in leaf nodes connected to unlabeled internal nodes of the metrical tree; and identifying at least a trigger tree having a minimal distance to the metrical tree; modify the score to represent an increased probability of trade irregularity as a function of the identification of the at least one trigger tree; compare the score to a preconfigured threshold; and execute a corresponding action by a review system (3XX0140) as a result of the comparison. receive data representing a set of trade activities; transform the data representing the set of trade activities to a musical piece representation; convert the musical piece representation to a metrical tree, wherein notes of the musical piece representation are recorded in labeled leaf nodes of the metrical tree, and the labeled leaf nodes are connected to unlabeled internal nodes; set an initial default value of a score representing a probability of trade irregularity; compare the metrical tree to a plurality of trigger trees stored in a lessons database (2TX0120), each trigger tree of the plurality of trigger trees representing a trade irregularity; identify at least one trigger tree of the plurality of trigger trees indicating at least a closest match to the metrical tree using a nearest-neighbors algorithm, wherein using the nearest-neighbors algorithm further comprises: computing distances between trees using match values, wherein the distance from a trigger tree of the plurality of trigger trees to the metrical tree is an average chance of finding a labeled leaf from the trigger tree in leaf nodes connected to unlabeled internal nodes of the metrical tree; and identifying at least a trigger tree having a minimal distance to the metrical tree; computing distances between trees using match values, wherein the distance from a trigger tree of the plurality of trigger trees to the metrical tree is an average chance of finding a labeled leaf from the trigger tree in leaf nodes connected to unlabeled internal nodes of the metrical tree; and identifying at least a trigger tree having a minimal distance to the metrical tree; modify the score to represent an increased probability of trade irregularity as a function of the identification of the at least one trigger tree; compare the score to a preconfigured threshold; and execute a corresponding action by a review system (3XX0140) as a result of the comparison.