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: Systems and methods are provided for use in identifying an entity's location based on device-level location data. One example computer-implemented method includes, for an entity associated with an unconfirmed location, transmitting, to a mobile device, a request to identify a physical location of the entity and receiving, from the mobile device, a response including at least (i) an identifier associated with the entity and/or a user associated with the mobile device and (ii) a location of the mobile device at the physical location of the entity. The method also includes matching the location of the mobile device to a transaction record for a transaction at the entity based on the transaction record including the identifier from the response and, in response to a match, assigning the location to the entity and publishing the assigned location of the entity.

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: A method for linking de-identified data identifiers to traceable data identifiers in compliance with applicable data privacy rules and regulations includes: receiving a plurality of first data identifiers from a first computing system; applying a one-way hashing algorithm and salt to the first data identifiers to generate second data identifiers; storing an association between each of the first data identifiers and the respective second data identifier; receiving one or more specific second data identifiers from a second computing system; identifying, for each of the one or more specific second data identifiers, the associated first data identifier; and transmitting each identified associated first data identifier to the first computing system.

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 linking de-identified data identifiers to traceable data identifiers in compliance with applicable data privacy rules and regulations includes: receiving a plurality of first data identifiers from a first computing system; applying a one-way hashing algorithm and salt to the first data identifiers to generate second data identifiers; storing an association between each of the first data identifiers and the respective second data identifier; receiving one or more specific second data identifiers from a second computing system; identifying, for each of the one or more specific second data identifiers, the associated first data identifier; and transmitting each identified associated first data identifier to the first computing system.

Abstract: A method for determining an economic impact of a candidate event includes receiving an event type identifier representing an event type of a candidate event to occur, and building a reference data store including historical transaction data records of payment transactions occurring in reference geographic areas associated with a reference event having the event type identifier. The method also includes generating a reference model from the historical transaction data records the models a reference number of active accounts and active merchants within a reference geographic area during a period of time associated with the reference event occurring in the geographic area. The method further includes determining a predicted number of active accounts and active merchants within the target geographic area using the reference model, and determining a predicted consumer spend using a predicted spend model. The method also includes generating an economic impact report including the consumer spend.

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 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: 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 generating microsegments includes retrieving a first set of information including activities attributable to a first plurality of entities and first entity identifiers; retrieving a second set of information including characteristics attributable to the first plurality of entities and second entity identifiers; combining the first and second sets of information using the first and second entity identifiers to obtain a combined set of information, the combined set of information not including personally identifiable information; generating a plurality of microsegments based on selected activities and/or characteristics criteria from the combined set of information; receiving a third set of information including characteristics attributable to a second plurality of entities; and matching characteristics in respective microsegments to characteristics of entities in the second plurality of entities to identify an audience of entities that have a propensity to carry out certain activities based on the

Abstract: A novel solution to the vexing problem of how to secure a cashless transaction card and card account from unauthorized use. By adding enhanced security to a cashless transaction card, the present invention significantly reduces, and may in fact eliminate, unauthorized use of a cashless transaction card and card account. In accordance with embodiments of the present invention, a cashless transaction card and card account are linked with an authentication device that must be in proximity with the cashless transaction card when that card is being used for a cashless transaction. In this way, the present invention makes unauthorized use of the card significantly less likely. The authentication device is chosen by the owner and linked thereby with the cashless transaction card and card account. Only when the authentication device is in proximity with the cashless transaction card will a cashless transaction be authorized.

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 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 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: A method for automatic email generation and delivery based on transaction account linkage includes: storing a plurality of account profiles, each related to a transaction account and including a primary account number and an email address automatically generated for the transaction account upon issuance; receiving a transaction message for a payment transaction including a specific primary account number and transaction data; identifying a specific account profile that includes the specific primary account number; generating an email message, wherein a body of the email message includes content generated from the transaction data and a header of the email message includes the email address included in the identified specific account profile as a destination address; and transmitting the generated email message to a mail server for delivery to the destination address included in the header.

Abstract: A system, method, and computer-readable storage medium configured to validate data to ensure that a computing system operates on clean, correct and useful data.