Abstract: 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.

Abstract: 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.

Abstract: Systems and methods for machine-assisted document input are disclosed. In one embodiment, a method may include a data extraction application executed by a computer processor: receiving an image of a document/email; generating a transcript of the document/email, wherein the transcript comprises a plurality of text groups from the document/email and a location for each text group in the document/email; identifying a vendor associated with the document/email based on contents of one of the text groups and/or one of the locations of the one of one of the text groups; retrieving a vendor-specific machine learning model for the vendor; associating each of the plurality of locations in the document/email with a billing field using the vendor-specific machine learning model; extracting each of the text groups into one of the billing fields based on the association; and transmitting the billing fields with the extracted data to a user electronic device.

Abstract: Embodiments of a Global Risk Administration (GRA) method and system include a GRA tool that assists an institution with various decision making processes by enabling the institution to customize the GRA tool to generate decisions based on information input by the institution or a customer of the institution. In an embodiment, the institution is a financial institution (FI), and customizing the GRA tool involves an FI user assigning attribution rules using, wherein attribution rules comprise characteristics of applicants for financial accounts. The user further creates one or more decision classes using the UI, wherein one or more attribution rules place an applicant in a decision class; and the user creates business rules, wherein a business rule determines a manner in which the GRA module interprets data from the third party data sources.

Abstract: Embodiments of a Global Risk Administration (GRA) method and system include a GRA tool that assists an institution with various decision making processes by enabling the institution to customize the GRA tool to generate decisions based on information input by the institution or a customer of the institution. The GRA tool further accesses third party data sources for the purpose of verifying information and gathering additional information to be used in generating decisions. The data sources are selectable by the institution as an aspect of the customization in an embodiment. In an embodiment, the institution is a financial institution (FI), and customizing the GRA tool involves an FI user assigning attribution rules using, wherein attribution rules comprise characteristics of applicants for financial accounts.

Abstract: Embodiments include a system, or platform, and method allowing financial institutions (FIs) to have access to an account opening platform that is capable of completing every aspect of the account opening approval process for the FI, and that is also capable of interfacing with their respective core systems (regardless of type or protocol) for receiving account opening requests from customers. Account opening requests may encounter permanent or temporary errors. When temporary errors are encountered, the account opening request is placed in a queue and automatically retried until the error is resolved. The account opening platform performs real-time integration with a variety of core systems such that approved new accounts are immediately “boarded” to the FI's core. In an embodiment, the platform is a plug-and-play” architecture such that adding the ability to communicate with new or different cores requires minimal effort.