US 12,007,980 B2
AI-driven transaction management system
Ronny Fehling, Munich (DE); Samantha Short, London (GB); Axel de Goursac, Paris (FR); Raphael Dubois, Paris (FR); Jörg Erlebach, Frankfurt (DE); and Karin Von Funck, Munich (DE)
Assigned to THE BOSTON CONSULTING GROUP, INC., Boston, MA (US)
Filed by THE BOSTON CONSULTING GROUP, INC., Boston, MA (US)
Filed on Jan. 17, 2019, as Appl. No. 16/251,051.
Prior Publication US 2020/0233857 A1, Jul. 23, 2020
Int. Cl. G06F 16/23 (2019.01); G06F 16/28 (2019.01); G06F 40/40 (2020.01); G06N 5/04 (2023.01); G06N 20/00 (2019.01); G06Q 10/0631 (2023.01); G06Q 10/067 (2023.01)
CPC G06F 16/2379 (2019.01) [G06F 16/2358 (2019.01); G06F 16/285 (2019.01); G06F 40/40 (2020.01); G06N 5/04 (2013.01); G06N 20/00 (2019.01); G06Q 10/06315 (2013.01); G06Q 10/067 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for processing transaction records, comprising:
receiving transaction data;
generating, based on the transaction data, a cleaned data set (CDS) comprising a plurality of logs for a plurality of transactions from the transaction data, wherein individual ones of the plurality of logs are associated with text;
clustering at least a subset of the plurality of logs from the CDS based at least in part 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 to 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;
determining, using the prediction model, one of the different transaction types, for the plurality of logs in the CDS not yet associated with a transaction type; and
generating a transaction report, the transaction report comprising a plurality of calculated parameters determined based at least in part on the plurality of logs and their associated transaction types that comprise the determined one of the different transaction types from the prediction model.