US 11,989,165 B2
Server systems and methods for merchant data cleansing in payment network
Shashank Dubey, Madhya Pradesh (IN); Gaurav Dhama, Haryana (IN); Ankur Arora, Delhi (IN); Vikas Bishnoi, Rajasthan (IN); Ankur Saraswat, Haryana (IN); Hardik Wadhwa, Bangalore (IN); Yatin Katyal, Haryana (IN); and Debasmita Das, West Bengal (IN)
Assigned to MASTERCARD INTERNATIONAL INCORPORATED, Purchase, NY (US)
Filed by MASTERCARD INTERNATIONAL INCORPORATED, Purchase, NY (US)
Filed on Aug. 2, 2022, as Appl. No. 17/879,680.
Claims priority of application No. 202141035129 (IN), filed on Aug. 4, 2021.
Prior Publication US 2023/0047717 A1, Feb. 16, 2023
Int. Cl. G06F 16/215 (2019.01); G06F 16/23 (2019.01); G06F 16/25 (2019.01)
CPC G06F 16/215 (2019.01) [G06F 16/2365 (2019.01); G06F 16/258 (2019.01)] 15 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
accessing, by a server system, a plurality of electronic payment transaction records associated with a plurality of merchants from a transaction database, each of the plurality of electronic payment transaction records comprising merchant data fields associated with a merchant of the plurality of merchants;
determining, by the server system, a set of electronic payment transaction records with ambiguous merchant data fields, each of the set of electronic payment transaction records from the plurality of electronic payment transaction records having a matching probability score less than a predetermined threshold value, wherein the matching probability score is computed by a probabilistic matching model;
identifying, by the server system, at least one issue for non-matching of each of the set of electronic payment transaction records;
determining, by the server system, at least one data model based, at least in part, on the at least one issue of each of the set of electronic payment transaction records, wherein the at least one data model is one of: phone-to-city model, payment aggregator model, and merchant name normalization model;
updating, by the server system, the set of electronic payment transaction records having ambiguous merchant data fields with corresponding unambiguous merchant data fields by applying the at least one data model to each of the set of electronic payment transaction records;
applying, by the server system, the merchant name normalization model over third electronic payment transaction records to determine aggregated merchant names, wherein merchant name fields of the third electronic payment transaction records are populated with ambiguous merchant names, and wherein the merchant name normalization model is based on a transformer neural network model with character level encoding; and
updating, by the server system, the merchant name fields of the third electronic payment transaction records based on the application of the merchant name normalization model over the third electronic payment transaction records.