Abstract: A system may generate a first transaction history from a plurality of records of charge. The system may select a plurality of merchants based on the plurality of records of charge, and generate a first user profile in response to the first transaction history and the plurality of merchants. The first user profile may comprise a merchant score for a merchant appearing in the first transaction history. The first transaction history may include transaction account issuer data. The system may receive a request for content that identifies a user account associated with the first user profile. The system may further generate the content based on the merchant score in response to the request for the content. The request may be automatically generated by the application or may be in response to an action by the first user.

Abstract: A system may generate a first transaction history from a plurality of records of charge. The system may select a plurality of merchants based on the plurality of records of charge, and generate a first user profile in response to the first transaction history and the plurality of merchants. The first user profile may comprise a merchant score for a merchant appearing in the first transaction history. The first transaction history may include transaction account issuer data. The system may receive a request for content that identifies a user account associated with the first user profile. The system may further generate the content based on the merchant score in response to the request for the content. The request may be automatically generated by the application or may be in response to an action by the first user.

Abstract: Improved systems and methods are provided for identifying financial relationships. In particular, financial relationships may be identified by associating tradelines with one or more people who sign or co-sign on the tradeline. In various embodiments a method is provided comprising, receiving, at a computer-based system for credit data analysis comprising a processor and a tangible, non-transitory memory, credit reporting data relating to a tradeline, parsing, by the computer-based system, the credit reporting data to yield primary debtor data and secondary debtor data, and linking, by the computer-based system, the tradeline with the primary debtor data and the secondary debtor data.

Abstract: Improved systems and methods are provided for identifying financial relationships. In particular, financial relationships may be identified by associating tradelines with one or more people who sign or co-sign on the tradeline. In various embodiments a method is provided comprising, receiving, at a computer-based system for credit data analysis comprising a processor and a tangible, non-transitory memory, credit reporting data relating to a tradeline, parsing, by the computer-based system, the credit reporting data to yield primary debtor data and secondary debtor data and linking, by the computer-based system, the tradeline with the primary debtor data and the secondary debtor data.

Abstract: The systems and methods may be used to recommend an item to a consumer. The methods may comprise determining, based on a collaborative filtering algorithm, a consumer relevance value associated with an item, and transmitting, based on the consumer relevance value, information associated with the item to a consumer. A collaborative filtering algorithm may receive as an input a transaction history associated with the consumer, a demographic of the consumer, a consumer profile, a type of transaction account, a transaction account associated with the consumer, a period of time that the consumer has held a transaction account, a size of wallet, and/or a share of wallet. The method may further comprise generating a ranked list of items based upon consumer relevance values, transmitting a ranked list of items to a consumer, and/or re-ranking a ranked list of items based upon a merchant goal.

Abstract: The systems and methods may be used to recommend an item to a consumer. The methods may comprise determining, based on a collaborative filtering algorithm, a consumer relevance value associated with an item, and transmitting, based on the consumer relevance value, information associated with the item to a consumer. A collaborative filtering algorithm may receive as an input a transaction history associated with the consumer, a demographic of the consumer, a consumer profile, a type of transaction account, a transaction account associated with the consumer, a period of time that the consumer has held a transaction account, a size of wallet, and/or a share of wallet. The method may further comprise generating a ranked list of items based upon consumer relevance values, transmitting a ranked list of items to a consumer, and/or re-ranking a ranked list of items based upon a merchant goal.

Abstract: The systems and methods may be used to recommend an item to a consumer. The methods may comprise determining, based on a collaborative filtering algorithm, a consumer relevance value associated with an item, and transmitting, based on the consumer relevance value, information associated with the item to a consumer. A collaborative filtering algorithm may receive as an input a transaction history associated with the consumer, a demographic of the consumer, a consumer profile, a type of transaction account, a transaction account associated with the consumer, a period of time that the consumer has held a transaction account, a size of wallet, and/or a share of wallet. The method may further comprise generating a ranked list of items based upon consumer relevance values, transmitting a ranked list of items to a consumer, and/or re-ranking a ranked list of items based upon a merchant goal.

Abstract: The systems and methods may be used to recommend an item to a consumer. The methods may comprise determining, based on a collaborative filtering algorithm, a consumer relevance value associated with an item, and transmitting, based on the consumer relevance value, information associated with the item to a consumer. A collaborative filtering algorithm may receive as an input a transaction history associated with the consumer, a demographic of the consumer, a consumer profile, a type of transaction account, a transaction account associated with the consumer, a period of time that the consumer has held a transaction account, a size of wallet, and/or a share of wallet. The method may further comprise generating a ranked list of items based upon consumer relevance values, transmitting a ranked list of items to a consumer, and/or re-ranking a ranked list of items based upon a merchant goal.

Abstract: In an embodiment of the invention, historical data related to multiple members of a customer loyalty program is gathered. A set of loyalty behavior models is developed for an individual member of the loyalty program is developed based on the historical data. For each campaign in a plurality of marketing campaigns, at least one combination of offers is inserted into each loyalty behavior model to output a plurality of net profit scores for the individual member, wherein each combination of offers outputs a separate net profit score. For each campaign in the plurality of campaigns, a combination of offers having the highest net profit score for the campaign is selected. The campaign having the highest net profit score of the plurality of campaigns is selected, and marketing materials for the selected campaign and combination of offers is transmitted to the individual member.

Abstract: The method of processing an analysis cycle to determine interest merchants may include selecting a seed merchant relevant to a topic interest, identifying consumers that have completed a transaction with the seed merchant to generate a list of identified consumers, determining merchants visited by the identified consumers, scoring all the merchants based on network connectivity, activity, and merchant over-index, updating the seed merchant in response to the list of scored merchants relative to a scoring threshold, and scoring the list of identified consumers based on the number of distinct merchants in transaction and over-indexing. Additionally, the method may further comprise producing a list of updated interest merchants and a list of updated identified consumers, where the updated interest merchants and the updated identified consumers are relevant to the topic interest.

Abstract: The present disclosure includes a system, method, and article of manufacture for generating an entity graph. The method may comprise determining a relationship between a first entity and a second entity based upon internal data, external data, and/or online data associated with the first entity, and generating the entity graph comprising at least two nodes and an edge connecting the at least two nodes. The method may further comprise, in various embodiments, tailoring marketing to the first entity based upon the entity graph, detecting fraud against the first entity based upon the entity graph, periodically updating the entity graph based upon new internal data and new online data, and/or adjusting the edge based upon a change in the relationship between the first entity and the second entity.

Abstract: The present disclosure includes a system, method, and article of manufacture for generating an entity graph. The method may comprise determining a relationship between a first entity and a second entity based upon internal data, external data, and/or online data associated with the first entity, and generating the entity graph comprising at least two nodes and an edge connecting the at least two nodes. The method may further comprise, in various embodiments, tailoring marketing to the first entity based upon the entity graph, detecting fraud against the first entity based upon the entity graph, periodically updating the entity graph based upon new internal data and new online data, and/or adjusting the edge based upon a change in the relationship between the first entity and the second entity.

Abstract: Improved systems and methods are provided for identifying financial relationships. In particular, financial relationships may be identified by associating tradelines with one or more people who sign or co-sign on the tradeline. In various embodiments a method is provided comprising, receiving, at a computer-based system for credit data analysis comprising a processor and a tangible, non-transitory memory, credit reporting data relating to a tradeline, parsing, by the computer-based system, the credit reporting data to yield primary debtor data and secondary debtor data, and linking, by the computer-based system, the tradeline with the primary debtor data and the secondary debtor data.

Abstract: The systems are generally configured to provide rewards. The systems are capable of syncing transaction accounts with one or more reward programs and one or more channels. Moreover, the systems are capable dynamically provisioning rewards in one or more subsets to a rewards program to distribute high, medium and low value rewards to a reward program throughout the duration of the program.

Abstract: The method of processing an analysis cycle to determine interest merchants may include selecting a seed merchant relevant to a topic interest, identifying consumers that have completed a transaction with the seed merchant to generate a list of identified consumers, determining merchants visited by the identified consumers, scoring all the merchants based on network connectivity, activity, and merchant over-index, updating the seed merchant in response to the list of scored merchants relative to a scoring threshold, and scoring the list of identified consumers based on the number of distinct merchants in transaction and over-indexing. Additionally, the method may further comprise producing a list of updated interest merchants and a list of updated identified consumers, where the updated interest merchants and the updated identified consumers are relevant to the topic interest.

Abstract: The systems and methods described herein may be used to recommend an item to a consumer. The methods may comprise determining, based on a collaborative filtering algorithm, a consumer relevance value associated with an item, and transmitting, based on the consumer relevance value, information associated with the item to a consumer. A collaborative filtering algorithm may receive as an input at least one of: a transaction history associated with the consumer, a demographic of the consumer, a consumer profile, a type of transaction account, a transaction account associated with the consumer, a period of time that the consumer has held a transaction account, a size of wallet, a share of wallet, and/or the like. The method may further comprise generating a ranked list of items based upon a plurality of consumer relevance values, transmitting a ranked list of items to a consumer, and/or re-ranking a ranked list of items based upon a merchant goal.

Abstract: Improved systems and methods are provided for identifying financial relationships. In particular, financial relationships may be identified by associating tradelines with one or more people who sign or co-sign on the tradeline. In various embodiments a method is provided comprising, receiving, at a computer-based system for credit data analysis comprising a processor and a tangible, non-transitory memory, credit reporting data relating to a tradeline, parsing, by the computer-based system, the credit reporting data to yield primary debtor data and secondary debtor data, and linking, by the computer-based system, the tradeline with the primary debtor data and the secondary debtor data.

Abstract: Improved systems and methods are provided for identifying financial relationships. In particular, financial relationships may be identified by associating tradelines with one or more people who sign or co-sign on the tradeline. In various embodiments a method is provided comprising, receiving, at a computer-based system for credit data analysis comprising a processor and a tangible, non-transitory memory, credit reporting data relating to a tradeline, parsing, by the computer-based system, the credit reporting data to yield primary debtor data and secondary debtor data, and linking, by the computer-based system, the tradeline with the primary debtor data and the secondary debtor data.

Abstract: Improved systems and methods are provided for identifying financial relationships. In particular, financial relationships may be identified by associating tradelines with one or more people who sign or co-sign on the tradeline. In various embodiments a method is provided comprising, receiving, at a computer-based system for credit data analysis comprising a processor and a tangible, non-transitory memory, credit reporting data relating to a tradeline, parsing, by the computer-based system, the credit reporting data to yield primary debtor data and secondary debtor data, and linking, by the computer-based system, the tradeline with the primary debtor data and the secondary debtor data.

Abstract: Improved systems and methods are provided for identifying financial relationships. In particular, financial relationships may be identified by associating tradelines with one or more people who sign or co-sign on the tradeline. In various embodiments a method is provided comprising, receiving, at a computer-based system for credit data analysis comprising a processor and a tangible, non-transitory memory, credit reporting data relating to a tradeline, parsing, by the computer-based system, the credit reporting data to yield primary debtor data and secondary debtor data, and linking, by the computer-based system, the tradeline with the primary debtor data and the secondary debtor data.