Abstract: A computer-implemented method for detecting potentially fraudulent cardholder transactions is implemented by a fraud detection computer system in communication with a memory. The method includes identifying a first home location for a first cardholder, identifying an expected range of travel relative to the first home location for the first cardholder when making a typical financial transaction in at least one merchant category, receiving transaction data associated with a first financial transaction, retrieving the first home location based on the cardholder data and a merchant location for the first merchant based on the merchant data, calculating a distance of travel between the first merchant location and the cardholder home location, comparing the calculated distance of travel to the expected range of travel, and flagging the first financial transaction as potentially fraudulent upon determining that the calculated distance of travel exceeds the expected range of travel.

Abstract: Disclosed herein are systems and methods of individual level learning that include receiving purchase event data from a merchant device that indicates that a purchase event occurred by a user on a user device, and transmitting the purchase event data to an analytics server. The methods may also include processing the purchase event data. The processing may include calculating a time gap for each of two sequential purchase events in a list of purchase events, and calculating an average duration of consecutive events by averaging all of the purchase events in the list of purchase events. The method may determine a purchase hazard probability that a purchase event will occur on a given day, when the average duration of consecutive events is larger than a standard deviation of the event occurring. When the purchase hazard probability is above a threshold, the system may push a message to the user device.

Abstract: A computer-implemented method for enhancing fraud detection based on transactions at potentially compromised locations is provided. The method includes determining a list of potentially compromised transaction locations, storing the list of potentially compromised transaction locations, and receiving from a first transaction location a first authorization request message for a first transaction. The first authorization request message is associated with a first payment account.

Abstract: Embodiments of the disclosure enable a computing system to enhance one or more financial transactions. The computing system identifies a cardholder account used to enter into a financial transaction, identifies a merchant and/or a primary product associated with the financial transaction, analyzes cardholder data associated with the cardholder account and account data associated with the merchant and/or the primary product to determine a secondary product, generate presentation data for presenting product information associated with the secondary product, and receive selection data associated with the secondary product such that the financial transaction is enhanced with the secondary product. Aspects of the disclosure provide for enhancing the financial transaction with a secondary product tailored to the cardholder, provided or promoted by the merchant, and/or useable with the primary product in a constructive, complementary, and/or effective manner.

Abstract: Disclosed herein are systems and methods of individual level learning that include receiving purchase event data from a merchant device that indicates that a purchase event occurred by a user on a user device, and transmitting the purchase event data to an analytics server. The methods may also include processing the purchase event data. The processing may include calculating a time gap for each of two sequential purchase events in a list of purchase events, and calculating an average duration of consecutive events by averaging all of the purchase events in the list of purchase events. The method may determine a purchase hazard probability that a purchase event will occur on a given day, when the average duration of consecutive events is larger than a standard deviation of the event occurring. When the purchase hazard probability is above a threshold, the system may push a message to the user device.

Abstract: Disclosed herein are systems and methods of individual level learning that include receiving purchase event data from a merchant device that indicates that a purchase event occurred by a user on a user device, and transmitting the purchase event data to an analytics server. The methods may also include processing the purchase event data. The processing may include calculating a time gap for each of two sequential purchase events in a list of purchase events, and calculating an average duration of consecutive events by averaging all of the purchase events in the list of purchase events. The method may determine a purchase hazard probability that a purchase event will occur on a given day, when the average duration of consecutive events is larger than a standard deviation of the event occurring. When the purchase hazard probability is above a threshold, the system may push a message to the user device.

Abstract: Embodiments of the disclosure enable a computing system to enhance one or more financial transactions. The computing system identifies a cardholder account used to enter into a financial transaction, identifies a merchant and/or a primary product associated with the financial transaction, analyzes cardholder data associated with the cardholder account and account data associated with the merchant and/or the primary product to determine a secondary product, generate presentation data for presenting product information associated with the secondary product, and receive selection data associated with the secondary product such that the financial transaction is enhanced with the secondary product. Aspects of the disclosure provide for enhancing the financial transaction with a secondary product tailored to the cardholder, provided or promoted by the merchant, and/or useable with the primary product in a constructive, complementary, and/or effective manner.

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: Embodiments of the disclosure enable a computing system to enhance one or more financial transactions. The computing system identifies a cardholder account used to enter into a financial transaction, identifies a merchant and/or a primary product associated with the financial transaction, analyzes cardholder data associated with the cardholder account and account data associated with the merchant and/or the primary product to determine a secondary product, generate presentation data for presenting product information associated with the secondary product, and receive selection data associated with the secondary product such that the financial transaction is enhanced with the secondary product. Aspects of the disclosure provide for enhancing the financial transaction with a secondary product tailored to the cardholder, provided or promoted by the merchant, and/or useable with the primary product in a constructive, complementary, and/or effective manner.

Abstract: At an intermediate node in a payment card processing network, clearing records are obtained for payment card transactions with a merchant which sells eligible wares and ineligible wares. Said clearing records specify, for each of said transactions, a total transaction amount and information representative of an amount associated with said eligible wares. At least a portion of said clearing records are stored in a first database accessible to said intermediate node in said payment card processing network, by transaction. Said first database is queried on payment card account number and, for said payment card account number, at least said information representative of an amount associated with said eligible wares is collected. Said collected information representative of an amount associated with said eligible wares is made available to at least one interested party.

Abstract: A method for automated payment of tax for payment transactions includes: receiving transaction messages related to payment transactions involving a merchant including a transaction amount and a merchant identifier; identifying a tax authority having jurisdiction over each payment transaction; identifying a tax amount for each of the plurality of transaction messages based on the transaction amount stored in the respective transaction message; identifying a tax identification number for the merchant based on the transaction messages; and initiating settlement of a settlement amount to a transaction account associated with the merchant, wherein the settlement amount is based on a combination of the transaction amounts included in each of the plurality of transaction messages less a combination of the identified tax amounts for the at least one tax authorities for each of the plurality of transaction messages.

Abstract: A fraud prevention system, method and medium analyzes a fraud prevention image, or a sequence of fraud prevention images, including a facial region of the user and a card associated with the user. The systems, methods and mediums may enable an image parser, a facial recognizer, a communication manager and an authenticator. The image parser may be adapted to parse the fraud prevention image into a facial image and a device image. The facial recognizer may be adapted to analyze the facial image to generate a current facial signature of the user. The authenticator may be adapted to authenticate the user based at least on comparison to the current facial signature and an enrolled facial signature. The communication manager may request a device verification based on a device signature and the authentication of the user. A liveness detector may verify liveness of the user within the fraud prevention image.

Abstract: Systems and methods are provided for use in scoring network data related to an account. An example method includes locating, by a node device, in response to a trigger event, a tree node for an account in a B-tree data structure where the tree node includes at least one network address in a data array, and retrieving, by the node device, network data associated with the at least one network address from the data array. The method also includes transmitting, by the node device, the network data to at least one computing device, and then generating and publishing, at the at least one computing device, at least one score based on a historical model and the retrieved network data in real time or near real time, thereby permitting services based on the at least one score to be offered to a consumer associated with the account.

Abstract: A computer-implemented method for detecting potentially fraudulent cardholder transactions is implemented by a fraud detection computer system in communication with a memory. The method includes identifying a first home location for a first cardholder, identifying an expected range of travel relative to the first home location for the first cardholder when making a typical financial transaction in at least one merchant category, receiving transaction data associated with a first financial transaction, retrieving the first home location based on the cardholder data and a merchant location for the first merchant based on the merchant data, calculating a distance of travel between the first merchant location and the cardholder home location, comparing the calculated distance of travel to the expected range of travel, and flagging the first financial transaction as potentially fraudulent upon determining that the calculated distance of travel exceeds the expected range of travel.

Abstract: A method for linking forensic data to transaction history includes: storing a plurality of consumer profiles, each profile including data related to a consumer including a consumer identifier associated with the related consumer and a plurality of transaction data entries, each entry including data related to a payment transaction involving the consumer including transaction data; receiving a forensic profile, the profile including forensic data obtained from a computing device; identifying a correspondence between the received forensic data and transaction data included in the stored transaction data entries to obtain a specific consumer identifier; identifying a specific consumer profile where the included consumer identifier corresponds to the specific consumer identifier; and transmitting at least the transaction data included in one or more transaction data entries included in the identified specific consumer profile.

Abstract: A computer implemented method for creating merchant profiles based on check transactions is provided. The method uses a computing device having a processor communicatively coupled to a memory. The method includes receiving a plurality of payment transactions including payment by a check where each payment transaction includes plurality of fields from the associated check that include at least a payee field, an address field, and a memo field, generating a list of unique merchants based in part on the payee fields of the plurality of payment transactions, generating two or more merchant profiles, and determining one or more connections between two or more merchant profiles in the plurality of merchant profiles. Each connection represents a relationship or association between two or more merchant profiles. The method also includes storing the one or more connections and the two or more merchant profiles.

Abstract: A method and a system are provided for identifying payment card holder hobbies or interests. The method includes retrieving from one or more databases a first set of information comprising payment card transaction information, payment card holder information and merchant information. The method further includes analyzing the first set of information to construct one or more groupings of merchants based on merchant line of business or merchant association with a hobby or interest; analyzing the first set of information to identify one or more payment card holder purchase behaviors; and associating the one or more payment card holder purchase behaviors with the one or more groupings of merchants to identify one or more payment card holder hobbies or interests. The method and system provide advantages in fraud prevention, and can also be used by merchants or businesses to better target customers or enhance existing customer relationships.

Abstract: A computer-implemented method for detecting potentially fraudulent cardholder transactions is implemented by a fraud detection computer system in communication with a memory. The method includes identifying a first home location for a first cardholder, identifying an expected range of travel relative to the first home location for the first cardholder when making a typical financial transaction in at least one merchant category, receiving transaction data associated with a first financial transaction, retrieving the first home location based on the cardholder data and a merchant location for the first merchant based on the merchant data, calculating a distance of travel between the first merchant location and the cardholder home location, comparing the calculated distance of travel to the expected range of travel, and flagging the first financial transaction as potentially fraudulent upon determining that the calculated distance of travel exceeds the expected range of travel.

Abstract: Disclosed herein are systems and methods of individual level learning that include receiving purchase event data from a merchant device that indicates that a purchase event occurred by a user on a user device, and transmitting the purchase event data to an analytics server. The methods may also include processing the purchase event data. The processing may include calculating a time gap for each of two sequential purchase events in a list of purchase events, and calculating an average duration of consecutive events by averaging all of the purchase events in the list of purchase events. The method may determine a purchase hazard probability that a purchase event will occur on a given day, when the average duration of consecutive events is larger than a standard deviation of the event occurring. When the purchase hazard probability is above a threshold, the system may push a message to the user device.

Abstract: Exemplary systems and methods are provided for directing offer content to consumers at communication devices, while the consumers are at merchants. An exemplary method includes capturing an image of a consumer when the consumer is in the vicinity of a product at a merchant, where the image depicts a facial expression of the consumer, and determining an intensity associated with the facial expression of the consumer, as captured in the image. The method also includes determining, by a computing device, a location at the merchant of a communication device associated with the consumer and selecting, by the computing device, an offer associated with the product for the consumer, based on the intensity of the facial expression and the determined location of the communication device, thereby relying at least in part on a consumer reaction to select the offer.