AI-driven transaction management system

A computer-implemented method for processing transaction records, comprising:

• receiving transaction data comprising a plurality of logs, the plurality of logs having associated text;
• clustering at least a subset of the plurality of logs based on the associated text and a similarity threshold, wherein the clustering results in a set of clusters;
• identifying a particular subset of the set of clusters having one or more of the plurality of logs to be categorized with different transaction types, based at least in part on one or more metrics of the set of clusters;
• determining a representative log to represent a centroid of individual clusters of the particular subset based at least in part on one or more metrics of the plurality of logs in the individual clusters of the particular subset;
• receiving a user determination of an individual transaction type of the different transaction types for the representative log in the individual clusters of the particular subset;
• assigning, in the individual clusters, the individual transaction type of the representative log individual logs, based, at least in part, on a context between the individual logs and the representative log that allows the individual logs to be joined together across the particular subset;
• training a prediction model using the individual logs from the particular subset and their associated transaction types;
• and
• predicting one of the different transaction types for additional logs of the plurality of using the prediction model.

A non-transitory computer-readable storage medium comprising instructions for processing transaction records, the instructions when executed by a processor causing the processor to:

• receive transaction data comprising a plurality of logs, the plurality of logs having associated text;
• cluster at least a subset of the plurality of logs based on the associated text and a similarity threshold to result in a set of clusters;
• identify a particular subset of the set of clusters having one or more of the plurality of logs to be categorized with different transaction types, based at least in part on one or more metrics of the set of clusters;
• determine a representative log to represent a centroid of individual clusters of the particular subset based at least in part on one or more metrics of the plurality of logs in the individual clusters of the particular subset;
• receive a user determination of an individual transaction type of the different transaction types for the representative log in the individual clusters of the particular subset;
• assign, in the individual clusters, the individual transaction type of the representative log individual logs, based, at least in part, on a context between the individual logs and the representative log that allows the individual logs to be joined together across the particular subset;
• train a prediction model using the individual logs from the particular subset and their associated transaction types;
• and
• predict one of the different transaction types for additional logs of the plurality of using the prediction model.