Abstract: Systems and methods are provided for determining an environmental impact of one or more transactions. An example method generally includes accessing transaction data representative of a plurality of transactions, where each of the transactions involves a user and a merchant, and accessing at least one index indicative of environmental impact of a plurality of merchants. The method also includes generating a graph based on the users and merchants included in the transaction data, whereby the graph is representative of the plurality of transactions, and determining a mapping between ones of the merchants involved in the transaction data and the environmental impact indicated by the at least one index, based on at least one of: the graph, the accessed at least one index, a graph convolution network (GCN), and a graph neural network (GNN). The method then includes publishing the mapping between the ones of the merchants and the environmental impact.

Abstract: A gratuity analytics computing system, for generating gratuity analytics for a plurality of transactions of a customer at a service provider within a date range, is in communication with an electronic device of the service provider over an electronic network. The system includes a gratuity analytics computing device, a database including a memory in operable electronic communication with the gratuity analytics computing device, and a processor configured to: receive transaction data for the plurality of customer transactions occurring within the date range, match a plurality of authorization messages with a respective plurality of clearing messages, generate gratuity analytics for the plurality of customer transactions over the date range based on average tip data from the customer, and calculate a customer satisfaction score for one customer transaction of the plurality of customer transactions based on the generated gratuity analytics.

Abstract: A gratuity analytics computing system, for generating gratuity analytics for a plurality of transactions of a customer at a service provider within a date range, is in communication with an electronic device of the service provider over an electronic network. The system includes a gratuity analytics computing device, a database including a memory in operable electronic communication with the gratuity analytics computing device, and a processor configured to: receive transaction data for the plurality of customer transactions occurring within the date range, match a plurality of authorization messages with a respective plurality of clearing messages, generate gratuity analytics for the plurality of customer transactions over the date range based on average tip data from the customer, and calculate a customer satisfaction score for one customer transaction of the plurality of customer transactions based on the generated gratuity analytics.

Abstract: A method for fraud modeling based on shared computing device usage includes: storing transaction data entries, each including a transaction date and/or time, account identifier, and device identifier associated with a computing device; receiving a transaction message for a payment transaction, the transaction message including a specific device identifier, primary account number, and additional transaction data; identifying transaction data entries where the included device identifier corresponds to the specific device identifier; determining a fraud risk rating based on a number of unique account identifiers included in the identified transaction data entries over a predetermined period of time; and transmitting the transaction message and the determined fraud risk rating to a financial institution associated with the primary account number.

Abstract: A gratuity analytics computing system, for generating gratuity analytics for a plurality of transactions of a customer at a service provider within a date range, is in communication with an electronic device of the service provider over an electronic network. The system includes a gratuity analytics computing device, a database including a memory in operable electronic communication with the gratuity analytics computing device, and a processor configured to: receive transaction data for the plurality of customer transactions occurring within the date range, match a plurality of authorization messages with a respective plurality of clearing messages, generate gratuity analytics for the plurality of customer transactions over the date range based on average tip data from the customer, and calculate a customer satisfaction score for one customer transaction of the plurality of customer transactions based on the generated gratuity analytics.

Abstract: A method for fraud modeling based on shared computing device usage includes: storing transaction data entries, each including a transaction date and/or time, account identifier, and device identifier associated with a computing device; receiving a transaction message for a payment transaction, the transaction message including a specific device identifier, primary account number, and additional transaction data; identifying transaction data entries where the included device identifier corresponds to the specific device identifier; determining a fraud risk rating based on a number of unique account identifiers included in the identified transaction data entries over a predetermined period of time; and transmitting the transaction message and the determined fraud risk rating to a financial institution associated with the primary account number.

Abstract: A method for fraud modeling based on shared computing device usage includes: storing transaction data entries, each including a transaction date and/or time, account identifier, and device identifier associated with a computing device; receiving a transaction message for a payment transaction, the transaction message including a specific device identifier, primary account number, and additional transaction data; identifying transaction data entries where the included device identifier corresponds to the specific device identifier; determining a fraud risk rating based on a number of unique account identifiers included in the identified transaction data entries over a predetermined period of time; and transmitting the transaction message and the determined fraud risk rating to a financial institution associated with the primary account number.

Abstract: A method for double anonymization of data includes: receiving, by a first computing system, a plurality of first data sets, each including a set identifier and personally identifiable information; anonymizing, by the first computing system, each of the first data sets, by hashing the set identifier included in each first data set to obtain a hashed identifier and deidentifying the personally identifiable information; transmitting, by the first computing system, the plurality of anonymized first data sets to a second computing system, wherein the second computing system is distinct and separate from the first computing system; anonymizing, by the second computing system, each of the anonymized first data sets, by hashing the hashed identifier to obtain a double-hashed identifier; and storing, in the second computing system or a third separate and distinct computing system, the plurality of double anonymized first data.

Abstract: Systems and methods are provided for determining an environmental impact of one or more transactions. An example method generally includes accessing transaction data representative of a plurality of transactions, where each of the transactions involves a user and a merchant, and accessing at least one index indicative of environmental impact of a plurality of merchants. The method also includes generating a graph based on the users and merchants included in the transaction data, whereby the graph is representative of the plurality of transactions, and determining a mapping between ones of the merchants involved in the transaction data and the environmental impact indicated by the at least one index, based on at least one of: the graph, the accessed at least one index, a graph convolution network (GCN), and a graph neural network (GNN). The method then includes publishing the mapping between the ones of the merchants and the environmental impact.

Abstract: A method for real-time measurement of campaign effectiveness includes: storing device profiles, each including a hashed device identifier, geographic location, and transaction entries including a transaction date and transaction data; receiving a data request including a start date, end date, merchant identifier, a plurality of device identifiers, and, for each device identifier, a category identifier of a set of category identifiers; generating a hashed identifier for each of the device identifiers; identifying, a device profile for each hashed device identifier; calculating purchase behaviors for each category based on transaction data in transaction data entries that include a transaction date between the start date and end date that are included in each identified device profile that is associated with the respective category and includes a geographic location corresponding to the merchant identifier; and transmitting at least one of: the calculated purchase behaviors and one or more metrics based thereon

Abstract: A method for double anonymization of data includes: receiving, by a first computing system, a plurality of first data sets, each including a set identifier and personally identifiable information; anonymizing, by the first computing system, each of the first data sets, by hashing the set identifier included in each first data set to obtain a hashed identifier and deidentifying the personally identifiable information; transmitting, by the first computing system, the plurality of anonymized first data sets to a second computing system, wherein the second computing system is distinct and separate from the first computing system; anonymizing, by the second computing system, each of the anyonmized first data sets, by hashing the hashed identifier to obtain a double-hashed identifier; and storing, in the second computing system or a third separate and distinct computing system, the plurality of double anonymized first data.

Abstract: A method for generation and distribution of protected user-specific information includes encrypting, by a processing device of a computer system, using a first encryption key of an encryption key pair, predetermined digits of a payment account number, the encryption yielding an encrypted identifier. The processing device of the computer system then generates a machine-readable code that is encoded with the encrypted identifier and additional data. A transmitting device of the computer system transmits, via an electronic communication, the generated machine-readable code to an electronic device of a user and transmits a second encryption key, of the encryption key pair, to a specialized computer system, wherein the second encryption key is associated with the first encryption key used to yield the encrypted identifier.

Abstract: There is provided a system and a method for the generation and management of point-of-sale (POS) electronic payment transaction receipts. The system and method include a POS component that performs a transaction at a location at which a customer and the POS component are physically present together generates content relating to the transaction; and a payment card that identifies a contact address to which an electronic receipt is to be sent. The POS component generates the electronic receipt based on the content relating to the transaction and causes the electronic receipt to be sent to the contact address identified on the payment card. The electronic receipt management system and method are based on payment transaction receipt information across a plurality of merchants, providing customers with an efficient solution for managing their receipts, and allowing merchants to offer better and more targeted recommendations for customers.

Abstract: A method for double anonymization of data includes: receiving, by a first computing system, a plurality of first data sets, each including a set identifier and personally identifiable information; anonymizing, by the first computing system, each of the first data sets, by hashing the set identifier included in each first data set to obtain a hashed identifier and deidentifying the personally identifiable information; transmitting, by the first computing system, the plurality of anonymized first data sets to a second computing system, wherein the second computing system is distinct and separate from the first computing system; anonymizing, by the second computing system, each of the anonymized first data sets, by hashing the hashed identifier to obtain a double-hashed identifier; and storing, in the second computing system or a third separate and distinct computing system, the plurality of double anonymized first data.

Abstract: Systems and methods are provided for rerouting funds, associated with transactions between consumers and merchants, to direct-pay accounts associated with creditors that provide loan and/or credit arrangements to the merchants. One exemplary method generally includes identifying, by a computing device, funds directed to a primary payment account associated with a merchant during settlement of at least one transaction for which funds are directed to the merchant. The computing device then routes a first portion of the funds to a direct-pay account associated with a creditor of the merchant, whereby the first portion of the funds is delivered to the direct-pay account in service of a debt between the creditor and the merchant, without the first portion of the funds being appended to the primary account associated with the merchant. The computing device then also routes a second portion of the funds to the primary account associated with the merchant.

Abstract: A method for fraud modeling based on shared computing device usage includes: storing transaction data entries, each including a transaction date and/or time, account identifier, and device identifier associated with a computing device; receiving a transaction message for a payment transaction, the transaction message including a specific device identifier, primary account number, and additional transaction data; identifying transaction data entries where the included device identifier corresponds to the specific device identifier; determining a fraud risk rating based on a number of unique account identifiers included in the identified transaction data entries over a predetermined period of time; and transmitting the transaction message and the determined fraud risk rating to a financial institution associated with the primary account number.

Abstract: A computer-implemented method for recommending items for future purchase by a consumer based on the consumer's historical actions is provided. The method is implemented using an adaptive recommendation (“AR”) computer device in communication with a memory. The method includes receiving from a recommender server computing device at least one preference vector, storing the at least one preference vector, receiving at least one consumer action including a time aspect, from a candidate consumer, and determining at least one personalized vector based on the at least one preference vector, the at least one consumer action, and the time aspect associated with each of the at least one consumer action. The at least one personalized vector represents at least one future purchase that the candidate consumer will likely conduct. The method also includes displaying at least one recommendation to the candidate consumer based on the at least one personalized vector.

Abstract: A method for fraud modeling based on shared computing device usage includes: storing transaction data entries, each including a transaction date and/or time, account identifier, and device identifier associated with a computing device; receiving a transaction message for a payment transaction, the transaction message including a specific device identifier, primary account number, and additional transaction data; identifying transaction data entries where the included device identifier corresponds to the specific device identifier; determining a fraud risk rating based on a number of unique account identifiers included in the identified transaction data entries over a predetermined period of time; and transmitting the transaction message and the determined fraud risk rating to a financial institution associated with the primary account number.

Abstract: A method of generating a more objective consumer confidence and more particularly to tracking consumer confidence objectively based on data regarding past transactions.

Abstract: A method for assessing a finder's fee for directing merchant traffic includes: transmitting, by a transmitting device, offer data related to an offer for the purchase of goods or services to a mobile communication device, wherein the related offer is associated with a merchant; receiving, by a receiving device, an indication that a user of the mobile communication device viewed the transmitted offer data; receiving, by the receiving device, an indication that the mobile communication device was located at a geographic location associated with the merchant within a predetermined period of time of the viewing of the transmitted offer data by the user of the mobile communication device; generating, by a processing device, an authorization request for a payment transaction for a finder's fee to be paid by the merchant; and transmitting, by the transmitting device, the authorization request.