Jpmorgan Chase Bank, N.a.

Prashant K Dhingra

John F Buford

Somnath Choudhuri

Liang Zhou

Christopher J Leasor

Jose Gondin

Akshay Karnawat

Riti Singhal

James Kaczmarczyk

Hong Ji

Song Ting Ceng

Rakesh Pathak

Jiangling Wang

Shailender Singh

Miguel Villarreal-vasquez

Parambir S Mudhar

Fenglin Yin

Systems and methods for automated data quality semantic constraint identification using rich data type inferences are disclosed. In one embodiment, a method for automated data quality analysis may include: (1) receiving, by a data quality engine computer program, reference data from a data source, wherein the reference data comprises a plurality of columns; (2) inferring, by the data quality engine computer program, a rich data type for each of the plurality of columns, wherein the rich data type has a specific format, a content constraint, and/or a specific application; (3) applying, by the data quality engine computer program, a data quality constraint to each column based on the rich data type for the column; (4) updating, by the data quality engine computer program, the reference data with production data; and (5) identifying, by the data quality engine computer program, a data quality issue in the production data.

Systems and methods for automated data quality semantic constraint identification using rich data type inferences

Various methods, apparatuses/systems, and media for implementing a payment application module for searching billets are disclosed. A plurality of data sources include customer profile data, customer payment history data, biller information data, and biller directory data. A processor operatively coupled to the plurality of data sources via a communication network builds a customer profile service area popularity model based on the customer profile data, customer payment history data, biller information data, and biller directory data; receives search query data for searching a biller; builds a biller search machine learning model to optimize weights applied to the search query data; and ranks search results by utilizing the machine learning model in response to the search query data.

System and method for searching billers with service area popularity model and machine learning

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Redmond, WA, United States of America

Prashant K Dhingra