Abstract: Systems and methods for efficient presentation of payment options at point of sale devices are disclosed. A method may include: identifying a plurality of stored or provisioned payment cards in customer wallets for a merchant; (2) pre-qualifying each of the customers associated with the stored/provisioned payment cards for one or more installment payment options for a purchase from the merchant; and (3) communicating a list of token reference identifiers for stored/provisioned payment cards that have been pre-qualified and the one or more pre-qualified installment payment options to a merchant computer program executed by a merchant backend, wherein the merchant computer program receives a token reference identifier for a stored or provisioned payment card in one of the customer wallets, determines that the token reference identifier is on the list of pre-qualified token reference identifiers, and presents the one or more installment payment options in a checkout user interface.

Abstract: A method for real-time billpay using credit-based products may include a billpay computer program: receiving an electronic file identifying approved merchant billpay participants from a payment processor computer program; receiving an invoice from one of the approved billpay merchant participants, the invoice comprising an embedded statement and a merchant name, an amount due, and a due date; receiving access to the invoice via a first customer user interface, wherein the first customer user interface is configured to access the embedded statement in a second customer interface, wherein the embedded statement is inaccessible to the first customer interface; presenting, in the first customer interface, the merchant name, the amount due, and the due date; receiving, from the first customer interface, approval to pay the invoice; determining that the approved billpay merchant participant is identified in the electronic file; and executing payment for the invoice via the payment processor computer program.

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.