US 12,130,785 B2
Data quality control in an enterprise data management platform
Prashant Jamkhedkar, Fremont, CA (US); Nalini Johnas, Newark, CA (US); Ravinder Dhamija, Dublin, CA (US); Daniel Oing, Scotts Valley, CA (US); Senthil Vellaichamy, Chennai (IN); Durga Rathinasamy, Chennai (IN); Rajagopal Ramakrishnan, Chennai (IN); Jose Smithesh Joseph, Kollam (IN); Tariq Akhtar Shaikh, San Jose, CA (US); Venkateshan Sundaram, San Ramon, CA (US); and Viswanathan Varadarajan, Pleasanton, CA (US)
Assigned to PAYPAL, INC., San Jose, CA (US)
Filed by PAYPAL, INC., San Jose, CA (US)
Filed on Mar. 3, 2022, as Appl. No. 17/686,272.
Claims priority of application No. 202141060586 (IN), filed on Dec. 24, 2021.
Prior Publication US 2023/0205742 A1, Jun. 29, 2023
Int. Cl. G06F 16/21 (2019.01); G06F 16/215 (2019.01); G06F 16/23 (2019.01); G06F 16/25 (2019.01)
CPC G06F 16/215 (2019.01) [G06F 16/2393 (2019.01); G06F 16/258 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A system, comprising:
a non-transitory memory; and
one or more hardware processors coupled with the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations comprising:
detecting a transaction request submitted by a user device of a user, wherein the transaction request is directed to a particular server associated with a particular organization from a plurality of organizations, wherein the plurality of organizations is associated with a plurality of respective servers storing data sets according to a plurality of respective data model schemas;
accessing a plurality of enterprise data model instances stored in a local data storage of the system, wherein each of the plurality of enterprise data model instances stores a portion of the data sets associated with a corresponding organization from the plurality of organizations according to an enterprise data model schema, wherein the enterprise data model schema is different from the plurality of respective data model schemas, and wherein portions of the data sets were retrieved from the plurality of respective servers and mapped from the plurality of respective data model schemas to the enterprise data model schema;
generating a consolidated data view that combines transaction data from two or more enterprise data model instances from the plurality of enterprise data model instances, wherein the transaction data represents transactions conducted with two or more organizations from the plurality of organizations;
obtaining a portion of the transaction data associated with the user based on the consolidated data view and a common identifier representing the user across the two or more enterprise data model instances;
determining, using a machine learning model, a risk associated with the transaction request based on the portion of the transaction data from the consolidated data view;
determining to authorize or deny the transaction request based on the risk; and
transmitting an indication to the particular server based on whether to authorize or deny the transaction request.