Determining targeted incentives based on consumer transaction history

Systems, apparatus, and methods for determining incentives based on consumer history. When, how, and to whom incentives are sent can be determined. For example, an incentive can be sent to a consumer to encourage a transaction at a time when the particular consumer is predisposed to initiate the transaction. Also, an incentive for a transaction can be sent to a consumer when that transaction has a high likelihood of leading to other transactions. An incentive can also be sent after a consumer initiates a transaction that is known to not have many subsequent transaction correlated to it.

Frequency-based transaction prediction and processing

Methods, apparatus, and systems are provided for tracking and analyzing data of consumer activity. The tracked data can be organized (e.g. as stored in cache, RAM, hard drives) in certain types of tables, where the tables can be associated with certain tags (keys) for efficiently accessing the data. The organization and associations of the data can also provide simple mechanisms for manipulating the data to obtain results specifically relevant for a task, such as detection of fraud or prediction of consumer behavior to provide better customer service or new services. For example, the tables may contain counters that store the number of times that two correlated consumer events occur within specific time intervals of each other. Such time data can provide efficient determination of patterns of consumer activity.

Pre-authorization of a transaction using predictive modeling

Systems, apparatus, and methods are provided for efficiently authorizing a transaction initiated by a consumer. An authorization can be generated before the consumer actually initiates the transaction. For example, a future transaction can be predicted, and an authorization can be generated for the predicted transaction. In this manner, the authorization can be ready and quickly used when the consumer does initiate the transaction. Previous transactions made by the consumer can be used to predict when the future transaction is likely. In various examples, the authorization can be sent to a specific merchant or to the consumer for use when the consumer initiates the predicted transaction, or saved by an authorization server for use in response to an authorization request from the merchant.

SOCIAL NETWORK PAYMENT AUTHENTICATION APPARATUSES, METHODS AND SYSTEMS

The SOCIAL NETWORK PAYMENT AUTHENTICATION APPARATUSES, METHODS AND SYSTEMS (“SNPA”) transform social networking user login inputs via SNPA components into notifications of authenticated purchase transactions. In one embodiment, the SNPA obtains an authentication request for a purchase transaction. The SNPA extracts card account data related to the authentication request, and provides the card account data related to the authentication request to a social networking server. The SNPA also provides an indication for a user to authenticate via logging into a social networking service associated with the social networking server. The SNPA obtains an indication from the social networking server that the user is authenticated. In response to obtaining the indication that the user is authenticated, the SNPA initiates completion of the purchase transaction. 1. A computer-implemented method for a graduated security protocol authentication network optimization , using social network , comprising:receiving, via a user interface implemented on a computing device, an authentication request for a purchase transaction;extracting, via a processor, card account data related to the authentication request;providing an indication for a user to authenticate via logging into a social networking service associated with a social networking server;providing the card account data related to the authentication request to the social networking server;calculating a quantitative transaction risk level associated with the authentication request.obtaining an indication from the social networking server that the user is authenticated; andinitiating completion of the purchase transaction in response to obtaining the indication that the user is authenticated.2. A computer-implemented method for a graduated security protocol authentication infrastructure optimization , using social network , comprising:obtaining an authentication request for a purchase transaction;extracting, via a processor, card ...

DYNAMIC NETWORK ANALYTICS SYSTEM

Embodiments of the invention is directed to a dynamic network analytics system capable of receiving and analyzing queries sent in data messages from data requesters. The queries contain a request from the data requester as to a risk level associated with an interaction conducted by a user. The dynamic network analytics system can determine an optimized process for determining the risk level of the interaction, based on an analysis of past interactions by the user and past interactions by users similar to the user. The dynamic network analytics system can retrieve data from internal and external data sources to generate a response to the query. The dynamic network analytics system conducts the optimized process and uses the retrieved data to generate risk assessments and risk scores in response to the query from the data requester. 1. A method comprising:receiving, at a data analyzer module, a first data message including a query from a data requester;determining, by the data analyzer module, a current risk level associated with the query;receiving, at the data analyzer module, risk data relevant to the query from one or more fixed risk data tables and one or more dynamic risk data tables;receiving, at the data analyzer module, external data relevant to the query from a first external data source;determining, at the data analyzer module, whether there is sufficient data to form a response to the query or one or more cut-off values have been reached, wherein if there is insufficient data or one or more cut-off values have not been reached, retrieving external data relevant to the query from a second external data source until there is sufficient data to form a response to the query or one or more cut-off values have been reached;generating, at the data analyzer module, a second data message containing the response to the query; andproviding the second data message to the data requester.2. The method of wherein the query is based on an interaction by a user with the ...

GRADUATED SECURITY SEASONING APPARATUSES, METHODS AND SYSTEMS

The GRADUATED SECURITY SEASONING APPARATUSES, METHODS AND SYSTEMS (“GSS”) transform user virtual wallet activity and historical fraud reports via GSS components into transaction authorization triggers generated pursuant to graduated, transaction risk-appropriate, escalated security protocols. In one implementation, the GSS obtains a current transaction request, the request utilizing a user virtual wallet account for payment. The GSS identifies a transaction risk type associated with the request, and calculates a transaction risk level associated with the transaction risk type. Based on the transaction risk level, the GSS selects a security protocol for processing the current transaction request; and provide a security data request in accordance with the selected security protocol. The GSS also generates an offer, for an entity involved in processing the current transaction request, of a financial incentive in exchange for assuming the transaction risk level associated with the transaction risk type; and provides the offer for the entity. 1. A graduated security protocol escalation processor-implemented method , comprising:obtaining a current transaction request, the current transaction request utilizing a user virtual wallet account for purchase payment;identifying a transaction risk type associated with the current transaction request;calculating, via a processor, a transaction risk level associated with the transaction risk type;selecting a security protocol, based on the calculated transaction risk level, for processing the current transaction request; andproviding a security data request in accordance with the selected security protocol.2. The method of claim 1 , further comprising:obtaining data on prior user wallet activity associated with the user virtual wallet account from a database; andwherein the transaction risk type is identified using the data on the prior user wallet activity.3. The method of claim 2 , wherein the prior user wallet activity includes ...

PRE-AUTHORIZATION OF A TRANSACTION USING PREDICTIVE MODELING

Systems, apparatus, and methods are provided for efficiently authorizing a transaction initiated by a consumer. An authorization can be generated before the consumer actually initiates the transaction. For example, a future transaction can be predicted, and an authorization can be generated for the predicted transaction. In this manner, the authorization can be ready and quickly used when the consumer does initiate the transaction. Previous transactions made by the consumer can be used to predict when the future transaction is likely. In various examples, the authorization can be sent to a specific merchant or to the consumer for use when the consumer initiates the predicted transaction, or saved by an authorization server for use in response to an authorization request from the merchant. 1. A method of using an authorization for a specific future transaction by a consumer , the method comprising:performing a plurality of previous transactions;receiving, with an electronic device, a token for authorization of a specific future transaction, wherein the authorization is for a time when the specific future transaction is predicted to be likely based on the previous transactions, and wherein the specific future transaction is identifiable by one or more keys that characterize the specific future transaction; andproviding, with the electronic device, the authorization to a merchant at the time when the specific future transaction is predicted to be likely, wherein the generated authorization token is operable to be used during a process for authorizing a transaction that is initiated by the consumer and that matches the one or more keys, the authorization token authorizing payment from one or more accounts of the consumer.2. The method of claim 1 , wherein the time when the specific future transaction is predicted to be likely is a time window.3. The method of claim 2 , wherein the authorization token is valid only within the time window.4. The method of claim 1 , ...

DETERMINING TARGETED INCENTIVES BASED ON CONSUMER TRANSACTION HISTORY

Systems, apparatus, and methods for determining incentives based on consumer history. When, how, and to whom incentives are sent can be determined. For example, an incentive can be sent to a consumer to encourage a transaction at a time when the particular consumer is predisposed to initiate the transaction. Also, an incentive for a transaction can be sent to a consumer when that transaction has a high likelihood of leading to other transactions. An incentive can also be sent after a consumer initiates a transaction that is known to not have many subsequent transaction correlated to it. 134-. (canceled)35. A method of providing an incentive to a consumer , the method comprising:receiving data corresponding to previous transactions associated with the consumer, each of the previous transactions associated with one or more keys, and each of the one or more keys corresponding to a particular type of transaction;storing, in a table corresponding to an initial key and a final key, counts of a plurality of elapsed times between previous transactions associated with the initial key and previous transactions associated with the final key, each elapsed time of the plurality of elapsed times corresponding to an elapsed time between one transaction associated with the initial key and one transaction associated with the final key;determining, by a computer system, a likelihood for a future transaction at each future time of a plurality of future times, the future transaction being associated with the final key, the likelihood for the future transaction at each particular future time being determined based on a count of elapsed times in the table corresponding to the particular future time;predicting, by the computer system, a time window when the consumer is likely to initiate the future transaction using the likelihoods at the plurality of future times; andsending an incentive associated with the future transaction to the consumer such that the consumer receives the incentive ...

PRE-AUTHORIZATION OF A TRANSACTION USING PREDICTIVE MODELING

Systems, apparatus, and methods are provided for efficiently authorizing a transaction initiated by a consumer. An authorization can be generated before the consumer actually initiates the transaction. For example, a future transaction can be predicted, and an authorization can be generated for the predicted transaction. In this manner, the authorization can be ready and quickly used when the consumer does initiate the transaction. Previous transactions made by the consumer can be used to predict when the future transaction is likely. In various examples, the authorization can be sent to a specific merchant or to the consumer for use when the consumer initiates the predicted transaction, or saved by an authorization server for use in response to an authorization request from the merchant. 1. A method of using an authorization for a specific future transaction by a merchant , the method comprising:receiving, by a merchant computer of the merchant, an authorization token for the authorization of the specific future transaction between a consumer and the merchant, the authorization token authorizing payment from one or more accounts of the consumer for the specific future transaction, the specific future transaction being identifiable by a first set of one or more keys that characterize the specific future transaction, the specific future transaction being at a predicted time when the specific future transaction is predicted to be likely occur based on a plurality of previous transactions conducted by the consumer;determining, by the merchant computer, a second set of one or more keys that characterize a current transaction initiated by the consumer at a current time;determining, by the merchant computer, that the current time of the current transaction matches the predicted time when the specific future transaction is predicted to be likely;determining, by the merchant computer, that the first set of one or more keys that characterize the specific future transaction ...

Dynamic network analytic system

Embodiments of the invention is directed to a dynamic network analytics system capable of receiving and analyzing queries sent in data messages from data requesters. The queries contain a request from the data requester as to a risk level associated with an interaction conducted by a user. The dynamic network analytics system can determine an optimized process for determining the risk level of the interaction, based on an analysis of past interactions by the user and past interactions by users similar to the user. The dynamic network analytics system can retrieve data from internal and external data sources to generate a response to the query. The dynamic network analytics system conducts the optimized process and uses the retrieved data to generate risk assessments and risk scores in response to the query from the data requester.

Method and apparatus for pattern generation

Methods and apparatus for transforming scoreable transaction data into financial data features are disclosed. In one aspect, a computer-implemented method transforms transaction data (104) into a financial data feature (114) for assessing credit risks. The financial data feature (114) is extracted from the transaction data (104). The method involves obtaining the transaction data from a data source, and performing a set of operations (202) on the transaction data to transform the transaction data into the financial data feature. The set of operations (202) is selected only from a predefined set of classes of operations which are interrelated by a predefined order of precedence (402). Each operation in the set of operations is performed in an order based on the predefined order of precedence of a class associated with each operator.

Analyzing local non-transactional data with transactional data in predictive models

Systems and methods are provided that empowers various parties to combine transactional data and local non-transactional data using the collective intelligence gathered from a variety of sources to help the parties make more intelligent decisions relating to consumers. For example, the system can help select consumers based on the probability that the consumers will take advantage of an offer, coupon, or other item. In some embodiments, the present invention can be deployed as a part of a system that processes transactions. In this system, information associated with the transactions is analyzed in conjunction with non-transactional data in order to probabilistically determine whether a further action should be taken with the consumer.

Distributed quantum encrypted pattern generation and scoring

Distributed quantum encrypted pattern generation and scoring

Transaction scoring is performed in a distributed manner across a client-server computing system. A computing system for processing a transaction includes a server system and a client system. The server system is arranged to process information associated with the transaction, while the client system communicates with the server system and includes a key engine which is arranged to generate keys. The client system and the server system are arranged to cooperate to assess risk associated with the transaction. The client is arranged to send the keys generated by the key engine as a transaction to the server system.

Determining targeted incentives based on consumer transaction history

DETERMINING TARGETED INCENTIVES BASED ON CONSUMER TRANSACTION Systems, apparatus, and methods for determining incentives based on consumer history. When, how, and to whom incentives are sent can be determined. For example, an incentive can be sent to a consumer to encourage a transaction at a time when the particular consumer is predisposed to initiate the transaction. Also, an incentive for a transaction can be sent to a consumer when that transaction has a high likelihood of leading to other transactions. An incentive can also be sent after a consumer initiates a transaction that is known to not have many subsequent transaction correlated to it. 11686107_1

Demographic analysis using time-based consumer transaction histories

Systems, apparatus, and methods for determining groups of similar consumers and for identifying a trend in consumer behavior are provided. Likelihoods of a transaction being initiated at various times by one consumer can be calculated based on previous transactions of the consumer. The likelihoods for different consumers can be used to determine a group of similar consumers as a demographic. The likelihoods of a transaction being initiated at various times by a consumer of a demographic (or other entity) can be used to forecast trends (such as a demand for a product) and make business decisions, such as for marketing campaigns, inventory levels (e.g. at particular stores or for all stores), pricing, and store locations. Such likelihoods when focused to a particular category of transactions can provide even greater accuracy.

Determining targeted incentives based on consumer transaction history

Systems, apparatus, and methods for determining incentives based on consumer history. When, how, and to whom incentives are sent can be determined. For example, an incentive can be sent to a consumer to encourage a transaction at a time when the particular consumer is predisposed to initiate the transaction. Also, an incentive for a transaction can be sent to a consumer when that transaction has a high likelihood of leading to other transactions. An incentive can also be sent after a consumer initiates a transaction that is known to not have many subsequent transaction correlated to it.

Dynamic network analytics system

Embodiments of the invention is directed to a dynamic network analytics system capable of receiving and analyzing queries sent in data messages from data requesters. The queries contain a request from the data requester as to a risk level associated with an interaction conducted by a user. The dynamic network analytics system can determine an optimized process for determining the risk level of the interaction, based on an analysis of past interactions by the user and past interactions by users similar to the user. The dynamic network analytics system can retrieve data from internal and external data sources to generate a response to the query. The dynamic network analytics system conducts the optimized process and uses the retrieved data to generate risk assessments and risk scores in response to the query from the data requester.