System and method for transaction volume determination

Systems and methods of improving the operation of a transaction network and transaction network devices is disclosed. A business size arbitration network host may comprise various modules and engines as discussed herein wherein the size of the business (e.g., aggregate value of sales) may be identified as being relatively large, whereby the transaction network may tailor the handling of a transaction of the business, such as by adjusting handling speed, fees, etc., whereby the transaction network may actively encourage use of transaction products and services by relatively large businesses, and/or such as by delivering value-added services, such as electronically provided advertisements and/or offers, and/or other credit and/or lending products, whereby the transaction network more properly functions according to approved parameters.

FIELD

The present disclosure relates to data analytics for transaction data.

BACKGROUND

Large data sets may exist in various sizes and with various levels of organization. With big data comprising data sets as large as ever, the volume of data collected incident to the increased popularity of online and electronic transactions continues to grow. Billions of rows and hundreds of thousands of columns worth of data may populate a single table, for example. An example of the use of big data is in identifying and categorizing the relative standing (e.g., relative size) of a business relative to other businesses in its industry, which is frequently a key priority for transaction account issuers. Transactions processed by the transaction account issuer are massive in volume and comprise tremendously large data sets.

Due to significant informational deficiencies, private business opacity, and relative variations in transaction volumes among different industries, data establishing both objective and relative transaction volume and relative business size are incomplete and inaccurate. This data gap confuses and frustrates the identification and categorization of transaction data and related businesses, and obscures the identity and categorization of real-world entities and individuals behind transactions, while also hampering data analytics.

SUMMARY

In accordance with various embodiments, a business size arbitration network host and an industry indexed factor analysis module may be in communication with a processor and configured to predict a sales value of a business in response to data element source data. In various embodiments, the industry indexed factor analysis module predicts the sales value in response to evaluating and ranking of the business according to industry-relevant factors assigned industry-dependent weights.

A business size arbitration network host is disclosed. The business size arbitration network host may include a processor, a tangible, non-transitory memory configured to communicate with the processor, the tangible, non-transitory memory having instructions stored thereon that, in response to execution by the processor, cause the processor to perform operations. The business size arbitration network host may include an industry indexed factor analysis module in communication with the processor and configured to evaluate and rank of the business according to industry-relevant factors assigned industry-dependent weights, and a GBM model empirically-determined variable set analysis module in communication with the processor, the industry indexed factor analysis module, and a master size arbitration module and configured to predict the sales value of the business in response to the data element source data and in response to the industry indexed factor analysis module.

In various embodiments, the GBM model empirically-determined variable set analysis module predicts the sales value in response to evaluating and ranking of non-pre-determined factors ascertained by retrospective machine learning analysis in response to ingesting at least one of the data element source data and the evaluation and ranking of the business by the industry indexed factor analysis module according to industry-relevant factors assigned industry-dependent weights, crafting a prediction of the relative ranking of the business in response to at least one of the data element source data, and the evaluation and ranking of the business by the industry indexed factor analysis module according to industry-relevant factors assigned industry-dependent weights, and comparing the prediction that to a third party data source.

In various embodiments, the industry indexed factor analysis module further includes an industry categorization engine configured to ingest the data element source data and generate an industry categorization array comprising industry categories and a variable array comprising variables, an industry-linked variable comparator configured to compare a relative value of the variable array of the business within the industry categorization array and configured to provide at least one of an industry-linked variable ranking or an industry-linked variable threshold to a value decisioner. In various embodiments the value decisioner is configured to correlate the relative value of the variable array and the industry categorization array and rank the business within an industry category according to the sales value, wherein the industry categorization engine delivers the industry categorization array to the value decisioner on an industry categorization array bus, and wherein the industry categorization engine delivers the variable array to the industry-linked variable comparator on a variable array bus.

In various embodiments, the industry categorization array includes a plurality of industry categories. In various embodiments, the plurality of industry categories includes at least two of a group including depository institutions/insurance carriers, retail/general merchandise, manufacturing, wholesale trade durable, and legal services. In various embodiments, the variable array includes a plurality of variables that depict high sales value in one or more of the plurality of industry categories.

In various embodiments, the plurality of variables includes at least two of a group including employees, square footage, UCC filings, assets, payment experiences, net worth, Argus CV, and available credit.

In various embodiments, the GBM model empirically-determined variable set analysis module includes a GBM model variable determiner configured to ingest data and generate a GBM model variable array including a plurality of GBM variables. In various embodiments, the GBM model empirically-determined variable set analysis module includes a GBM model variable ranker configured to ingest the GBM model variable array and sequence the plurality of GBM variables, wherein the GBM model variable ranker associates an importance factor with each GBM variable, whereby a parallel GBM model variable importance array comprising a plurality of GBM model variable importance values is assembled.

In various embodiments, the GBM model empirically-determined variable set analysis module includes a GBM model variable evaluator configured to assess the GBM model variable array and GBM model variable importance array and configured to calculate a business size prediction in response to a magnitude of at least one GBM variable weighted by at least one GBM model variable importance value, and a value modeler configured to assign the sales value to the business indicative of a size of the business in response to the GBM model variable evaluator.

In various embodiments, the business size arbitration network host further includes an aggregate transaction value analysis module includes a third party data source sales determiner configured to add up aggregate transaction values from third-party data sources that are indicated to be transactions associated with the business and determine whether the transactions sum to a sales value floor, and wherein the business size arbitration network host selects between the GBM model empirically-determined variable set analysis module and the aggregate transaction value analysis module.

A business size arbitration network is disclosed. The business size arbitration network may include a business size arbitration network host configured to predict a sales value of a business in response to evaluating and ranking of the business according to industry-relevant factors assigned industry-dependent weights, wherein the business size arbitration network host directs data to be stored. The business size arbitration network may include a distributed storage system having a plurality of nodes, the distributed storage system configured to direct data to the business size arbitration network host, in response to the evaluating and ranking of the business by the business size arbitration network host. The business size arbitration network may include a telecommunications transfer channel including a network logically connecting the business size arbitration network host to the distributed storage system.

A method of industry-indexed factor analysis may include ingesting, by an industry categorization engine of a business size arbitration network host, data element source data. The method may include generating, by the industry categorization engine, an industry categorization array including industry categories. The method may include generating, by the industry categorization engine, a variable array including variables. The method may include comparing, by an industry-linked variable comparator, a relative value of the variable array of a business within the industry categorization array. The method may include providing, by the industry-linked variable comparator, at least one of an industry-linked variable ranking and/or an industry-linked variable threshold, to a value decisioner in response to the comparing. The method may include correlating, by the value decisioner, the relative value of the variable array and the industry categorization array. The method may include ranking, by the value decisioner, the business within an industry category according to the sales value, and in response to the correlating.

DETAILED DESCRIPTION

With reference toFIG. 1, system100for distributed data storage and processing is shown, in accordance with various embodiments. System100may comprise a business size arbitration network host102. Business size arbitration network host102may comprise any device capable of receiving and/or processing an electronic message via telecommunications transfer channel104. Telecommunications transfer channel104may comprise a network. Business size arbitration network host102may take the form of a computer or processor, or a set of computers/processors, although other types of computing units or systems may be used, including laptops, notebooks, hand held computers, personal digital assistants, cellular phones, smart phones (e.g., iPhone®, BlackBerry®, Android®, etc.) tablets, wearables (e.g., smart watches and smart glasses), or any other device capable of receiving data over telecommunications transfer channel104.

A network may be unsecure. Thus, communication over the network may utilize data encryption. Encryption may be performed by way of any of the techniques now available in the art or which may become available—e.g., Twofish, RSA, El Gamal, Schorr signature, DSA, PGP, PM, GPG (GnuPG), and symmetric and asymmetric cryptography systems.

In various embodiments, business size arbitration network host102may interact with distributed storage system106for storage and/or processing of big data sets. As used herein, big data may refer to partially or fully structured, semi-structured, or unstructured data sets including millions of rows and hundreds of thousands of columns. A big data set may be compiled, for example, from a history of purchase transactions over time, from web registrations, from social media, from records of charge (ROC), from summaries of charges (SOC), from internal data, or from other suitable sources. Big data sets may be compiled without descriptive metadata such as column types, counts, percentiles, or other interpretive-aid data points.

In various embodiments, distributed storage system106may comprise one or more nodes108. Nodes108may comprise computers or processors the same as or similar to business size arbitration network host102. Nodes108may be distributed geographically in different locations, housed in the same building, and/or housed in the same rack. Nodes108may also be configured to function in concert to provide storage space and/or processing power greater than one of a node108might provide alone. As a result, distributed storage system106may collect and/or store the data110. Data110may be collected by nodes108individually and compiled or in concert and collated. Data110may further be compiled into a data set and formatted for use.

In various embodiments, data110may comprise a collection of data including and/or originating from cardholder information, transaction information, account information, record of sales, account history, customer history, sensor data, machine log data, data storage system, public web data, and/or social media. Data110may be collected from multiple sources and amalgamated into a big data structure such as a file, for example. In that regard, the data may be used as an input to generate metadata describing the big data structure itself, as well as the data stored in the structure.

The distributed storage system106may comprise a transaction network. A business size arbitration network host102may comprise various modules and engines as discussed herein wherein the probability that a transaction is executed by an individual or entity comprising a relatively large business as compared to other businesses within that businesses industry. The meaning of “relatively large” is relative to the typical norms of an industry and is flexibly determinable by the host102, as discussed herein. For instance, relatively large may mean within the top 25 percent of sales volume or value within an industry, or may mean within the top 10 percent of sales volume or value within an industry, or any other sales metric as desired. Relatively large may mean within the top 25 percent of employees in an industry, or may mean within the top 10 percent number of employees within an industry, or any other employment metric as desired. Relatively large may mean within the top 25 percent of revenue within an industry or within the top 10 percent of revenue within an industry, or any other revenue metric as desired. Moreover, and as will be apparent herein, various industries may ascribe various weights to various other metrics whereby a business may be said to be relatively large within that industry. Thus, relatively large may mean within the top 25 percent of all businesses within an industry, according to an industry-valuable metric and/or a weighted average of industry-valuable metrics, or similarly within the top 10 percent, or any other metric as applicable. A business may be identified as being relatively large, whereby the transaction network may tailor the handling of a transaction of the business, such as by adjusting handling speed, fees, etc., whereby the transaction network may actively encourage use of transaction products and services by relatively large businesses, and/or such as by delivering value-added services, such as electronically provided advertisements and/or offers, and/or other credit and/or lending products, whereby the transaction network more properly functions according to approved parameters.

Moreover, such identifications enhance credit risk discrimination, identification of businesses and consumers associated with a business organization who may presently be consumer cardholders, whereby business-oriented transactions cards may be provided to them. Such identifications enable the promotion of relevant merchants to relevant cardholders such as to promote business-to-business relationship building and/or potential business-to-business relationships.

In various embodiments, such determinations involve multiple complex and interactive machine steps. For instance, by evaluating the data110at a transaction level, assessing the nature of a transaction at the individual transaction level provides sufficient granularity. Data may be evaluated at the transaction level and/or aggregated such as to determine if a merchant or any other transaction party may be identified as a large business. Moreover, such identification may be combined with or enhance the identification of such aspects as card product type, merchant industry codes, transaction amounts, number of transactions by an individual or a business in an industry, or at a particular merchant in an industry, determination of gross sales, removal of noise inducing transactions, and controlling for transactions having similar profiles, such as to facilitate further data processing. Determinations may further leverage text mining on names and addresses to identify large businesses and/or to model business sales volume and machine learning methodologies, such as gradient boosting decision trees, to identify the non-linear patterns of behavior exhibited by parties.

In various embodiments, and with reference toFIG. 1B, a business size arbitration network host102is described in more particular detail. For instance, a business size arbitration network host102may comprise various logical modules configured to perform various operations and processes in accordance with methods disclosed herein.

A business size arbitration network host102may comprise a data element source set comprised within the data110from nodes108of distributed storage system106. In further embodiments, the business size arbitration network host102may not comprise a data element source set, but may receive a data element source set from nodes108of the distributed storage system106, the data element source set comprised within the data110form nodes108.

A business size arbitration network host102may predict the aggregate transaction value of all transactions at a business based on data element source data110(“sales value”). In various embodiments, the business size arbitration network host102may further adapt these predictions over time in response to machine learning, such as may be useful as the business size arbitration network host102learns what data is more or less important to accurate predictions, based on iterative prediction, and evaluation of the outcome of predictions.

A business size arbitration network host102may determine predictions according to a variety of mechanisms, for instance, an industry-indexed factor analysis module201, a GBM model empirically-determined variable set analysis module203, and an aggregate transaction value analysis module205. One or more such module201,203, and205may provide a sales value prediction for a business. The business size arbitration network host102may further comprise a master size arbitration module207configured to select the one such sales value prediction, and/or blend multiple such sales value predictions, and provide an output to an electronic network.

As mentioned, a business size arbitration network host102may comprise an industry-indexed factor analysis module201. An industry-indexed factor analysis module201may complete operations and processes, such as in accordance with machine learning methods, whereby a sales value is predicted in response to evaluation and ranking of the business according to industry-relevant factors, each factor being assigned an industry-dependent weight. More specifically, an industry-indexed factor analysis module201may determine industry-relevant factors and assign industry-dependent weights to the industry-relevant factors, and communicate this determination and assignment to a GBM model empirically-determined variable set analysis module203, discussed herein. For instance, some factors may be more relevant to some industries than others (e.g., employee number may be more relevant to a staffing agency than to an automated manufacturing industry) and some factors may be more heavily weighted in some industries than others (e.g., employee number may weigh more heavily in favor of law firm revenues and less heavily or even negatively against software-as-a-service provider revenues).

A business size arbitration network host102may comprise a GBM model empirically-determined variable set analysis module203. A GBM model empirically-determined variable set analysis module203may complete operations and processes, such as in accordance with machine learning methods, whereby a sales value is predicted in response to evaluation and ranking of non-pre-determined factors ascertained by retrospective machine learning analysis, such as by ingesting data, making predictions as to the relative ranking of businesses, then comparing that to third party data sources. For instance, the GBM model empirically-determined variable set analysis module203may receive non-pre-determined factors ascertained by retrospective machine learning analysis from the industry-indexed factor analysis module201and/or third party data sources. The module203may itself identify non-pre-determined factors ascertained by retrospective machine learning analysis and may compare to factors derived in response to third party data sources, for instance, derived by the industry-indexed factor analysis module201in response to third party data. Thus, the GBM model empirically-determined variable set analysis module203may determine its own factors and weights according to machine learning, and/or may enjoy awareness of pre-determined factors and weights provided by the industry-indexed factor analysis module201. In this manner, the sales value prediction may be improved.

Finally, a business size arbitration network host102may comprise an aggregate transaction value analysis module205. An aggregate transaction value analysis module205may add up aggregate transaction values from third-party data sources that are indicated to be transactions associated with the business and/or may predict expected aggregate transaction values, and may determine whether the transactions sum to a sales value floor. Upon summing to a sales value floor, the business may be tagged with an indicator of “high sales value” or upon failing to sum to a sales value floor, with an indicator of “low sales value.” Moreover, aggregate transaction values may be predicted based on retrospective machine learning analysis, such as by ingesting data, making predictions as to the relative ranking of businesses, then comparing that to third party data (or later consolidated internal data).

With specific reference now toFIG. 3A-B, an industry-indexed factor analysis module201is discussed in additional detail. An industry-indexed factor analysis module201may comprise an industry categorization engine301. The industry categorization engine301may ingest data110and may generate an industry categorization array314comprising a plurality of categorical industries315, for instance a first industry category315-1, a second industry category315-2, a third industry category315-3, a fourth industry category315-4, a fifth industry category315-5, and any number n of industry category315-n. In various embodiments, such industry categories may comprise depository institutions/insurance carriers, retail/general merchandise, manufacturing, wholesale trade durable, legal services, and/or the like. The industry categorization array314may be delivered by the industry categorization engine301on an industry categorization array bus313to a value decisioner307, as will be discussed further herein.

The industry categorization engine301may further ingest data110and may generate a variable array209comprising a plurality of variables that depict high sales value in one or more industry category of the industry categorization array314. For instance, a variable array209may comprise a first variable310-1through an eighth variable310-8, and/or any number n of variables310-n. In various embodiments, such variables may comprise employees, square footage, UCC filings, assets, payment experiences, net worth, Argus CV (e.g., an external data source that provides Visa and MasterCard charge volumes for businesses in the United States and which may be positively correlated to sales volume), available credit, and/or the like. The variable array209may be delivered by the industry categorization engine301on variable array bus303to an industry-linked variable comparator305, as will be discussed further herein.

The industry-indexed factor analysis module201further comprises industry-linked variable comparator305configured to compare the relative value of the variables310of the variable array309among multiple businesses within the industry category315and at least one of provide a relative ranking, or assess relative to a floor or ceiling limit value thus establishing an industry-linked variable ranking and/or threshold, respectively. The industry-linked variable comparator305provides such industry-linked variable ranking and/or threshold to the value decisioner307, as will be further discussed herein.

The industry-indexed factor analysis module201further comprises a value decisioner307. As mentioned, the value decisioner307is in communication with both the industry categorization engine301and the industry-linked variable comparator305. The value decisioner307correlates the relative value of the variables310of the variable array309with the industry categories315of the industry categorization array314and selects only those variables310for each industry category315that are relevant for that industry category315(e.g., “filter” each array). In this manner, the businesses within each industry category315may be ranked by the value decisioner307based on only those variables310important for that industry category315. As used herein, ranking may mean a hierarchical assembling in relative value order, or may mean a prediction of the individual businesses sales value. For instance, the value decisioner307may provide an output of the industry-indexed factor analysis module201that is received into the GBM module empirically-determined variable set analysis module203.

With additional reference now toFIG. 2, the value decisioner307, industry categorization engine301, and industry-indexed factor analysis module201may interoperable implement a machine learning methodology whereby the predictions of sales values may be improved. For instance a method of machine learning enhanced value decisioning2000may include establishment of a dependent variable (step2002), for instance, the selection of a variable310believed to be relevant (e.g., dependent on) an industry category315(e.g., an independent variable). The method may comprise computing the dependent variable value from data sources (step2004), for instance, data110may be ingested to ascertain the relative and/or individual value of the variable for a given business. The value may be tested by comparison over time to other outcomes, and/or an analogous or related value from another source (step2006). Moreover, the value may be output, such as from the industry-indexed factor analysis module201to a GBM module empirically-determined variable set analysis module203(step2008). Finally, an iteration timing delay (step2010) may ensure a passage of time before a return to step2002, whereby the method of machine learning enhanced value decisioning2000repeats, thus iteratively improving the output of step2008, through the iterative step2006. In further embodiments, various aspects of machine learning enhanced value decisioning2000are performed by the GBM module empirically-determined variable set analysis module203in cooperation with the industry-indexed factor analysis module201.

With reference now toFIGS. 1A, 1B, 4A, and 4B, a GBM model empirically-determined variable set analysis module203is discussed in greater detail. A GBM model empirically-determined variable set analysis module203may comprise a GBM model variable determiner409. A GBM model variable determiner409may ingest data110, which may include outputs from the industry-indexed factor analysis module201, and generate a GBM model variable array405comprising a plurality of GBM variables407, for instance a first GBM variable407-1through a fifteenth GBM variable407-15, and any number n of GBM variables407-n. The GBM variables may include employees, payment experiences, industry index, sales amount factor, maximum available credit, average available credit, satisfactory payment experiences, square footage, Argus CV, IXI Assets (e.g., The total assets at zipcode level in US. This is a neighborhood variable that may be used as independent variable in a model), UCC filings, payment references, high sales presence indicators, legal status, and Enigma Match indicators (Enigma Match indicators may indicate presence of the business in an Enigma database wherein in various embodiments such presence indicates higher sales and may include data on businesses available on public/online sources, for instance, name of the business registering for marine vessels, health care data bank, import-export, hotel occupancies, green card filing, etc.), and/or the like. The GBM model variable determiner409determines which variables indicate high sales value and creates the GBM model variable array405. The GBM model variable determiner409provides the GBM model variable array405via a GBM model variable array bus403to a GBM model variable evaluator408, as will be further discussed herein.

A GBM model empirically-determined variable set analysis module203further comprises a GBM model variable ranker401. A GBM model variable ranker401sequences the variables of the GBM model variable array405according to a first importance factor. Stated differently, the GBM model variable ranker401ingests data110(in various embodiments including an output of the industry-indexed factor analysis module201) and may determine the relative value of the variables to a depiction of high sales value. The variables may be ranked by an importance factor. An importance factor comprises a number between 0 and 100 with 100 being determinative and 0 being irrelevant. The GBM model variable ranker401ingests the GBM model variable array405from the GBM model variable determiner409, and associates an importance factor with each variable407of the GBM model variable array405. As such, the GBM model variable ranker401creates a parallel GBM model variable importance array415comprising a first importance value417-1through a fifteenth importance value417-15and/or any number n of importance values417-n, each being mapped to a corresponding GBM model variable407of the GBM model variable array405. The GBM model variable ranker401provides this array on a variable rank bus413to the GBM model variable evaluator408.

The GBM model variable evaluator408may assess the parallel arrays of GBM model variable array405and GBM model variable importance array415and calculates a business size prediction based on the magnitude of each variable weighted by the importance of each variable. The GBM model variable evaluator408acquires the magnitude of each variable from data110, performs such calculation, then interoperates with a value modeler411.

The value modeler411subsequently assigns a sales value to the business indicative of the size of the business. In this manner, a GBM model empirically determined variable set analysis module203determines a sales value for a business. The GBM model empirically determined variable set analysis module203can further implement the machine learning methodology ofFIG. 2, whereby the predictions of sales values may be improved. For instance a method of machine learning enhanced value decisioning2000may include establishment of a dependent variable (step2002), for instance, the selection of a variable407believed to be relevant to (e.g., dependent on) a business (e.g., an independent variable). The method may comprise computing the dependent variable value from data sources (step2004), for instance, data110may be ingested to ascertain the relative and/or individual value of the variable for a given business. The value may be tested by comparison over time to other outcomes, and/or an analogous or related value from another source (step2006). Moreover, the value may be output, such as from the GBM model empirically-determined variable set analysis module203to a master size arbitration module207(step2008). Finally, an iteration timing delay (step2010) may ensure a passage of time before a return to step2002, whereby the method of machine learning enhanced value decisioning2000repeats, thus iteratively improving the output of step2008, through the iterative step2006.

With reference now toFIGS. 1A-B,3A-B,4A-B and5, an aggregate transaction value analysis module205is discussed in further detail. An aggregate transaction value analysis module205may determine a sales value of a business. The aggregate transaction value analysis module may make such determinations in response to sales volume predictions derived from a determination of whether the sales value of the business is believed to exceed $1 million annually. As such, rather than ascertaining a variable sales value, the aggregate transaction value analysis module205determines a threshold value and provides a binary indication of whether the sales value of the business is or is not believed to exceed $1 million. As such, the various methods discussed herein with respect to the industry-indexed factor analysis module201, the GBM model empirically-determined variable set analysis module203and/or other mechanisms may be implemented therein.

For instance, an aggregate transaction value analysis module205may comprise a GBM sales determiner501. A GBM sales determiner501may comprise a GBM model empirically-determined variable set analysis module203to determine a sales value.

Moreover, an aggregate transaction value analysis module205may comprise a third party data base sales determiner503. A third party data base sales determiner503may ingest data110, that has already be reposed in third-party databases, such as data indicative of business sales volume (e.g., number of employees, aggregate transaction values, and/or the like) and sum the factors to determine whether sales value exceeds $1 million. As such, a third-party data base sales determiner503may comprise an industry-indexed factor analysis module201.

Thus, the aggregate transaction value analysis module205may add up aggregate transaction values from third-party data sources that are indicated to be transactions associated with the business (such as via an third party data base sales determiner503), and may determine whether the transactions sum to a sales value floor. Upon summing to a sales value floor, the business may be tagged with an indicator of “high sales value” or upon failing to sum to a sales value floor, with an indicator of “low sales value.” Moreover, aggregate transaction values may be predicted based on retrospective machine learning analysis, such as by ingesting data, making predictions as to the relative ranking of businesses, then comparing that to third party data (or later consolidated internal data) (such as by a GBM sales determiner501).

The GBM sales determiner may further comprises an arbitration engine505configured to select among the output of the GBM sales determiner501and the third-party database sales determiner503. For instance, in the event that third-party data is lacking, the arbitration engine505may deselect the third-party database sales determiner503.

Data, as discussed herein, may include “internal data.” Internal data may include any data a credit issuer possesses or acquires pertaining to a particular consumer. Internal data may be gathered before, during, or after a relationship between the credit issuer and the transaction account holder (e.g., the consumer or buyer). Such data may include consumer demographic data. Consumer demographic data includes any data pertaining to a consumer. Consumer demographic data may include consumer name, address, telephone number, email address, employer and social security number. Consumer transactional data is any data pertaining to the particular transactions in which a consumer engages during any given time period. Consumer transactional data may include, for example, transaction amount, transaction time, transaction vendor/merchant, and transaction vendor/merchant location. Transaction vendor/merchant location may contain a high degree of specificity to a vendor/merchant. For example, transaction vendor/merchant location may include a particular gasoline filing station in a particular postal code located at a particular cross section or address. Also, for example, transaction vendor/merchant location may include a particular web address, such as a Uniform Resource Locator (“URL”), an email address and/or an Internet Protocol (“IP”) address for a vendor/merchant. Transaction vendor/merchant and transaction vendor/merchant location may be associated with a particular consumer and further associated with sets of consumers. Consumer payment data includes any data pertaining to a consumer's history of paying debt obligations. Consumer payment data may include consumer payment dates, payment amounts, balance amount, and credit limit. Internal data may further comprise records of consumer service calls, complaints, requests for credit line increases, questions, and comments. A record of a consumer service call includes, for example, date of call, reason for call, and any transcript or summary of the actual call.

Any communication, transmission and/or channel discussed herein may include any system or method for delivering content (e.g. data, information, metadata, etc.), and/or the content itself. The content may be presented in any form or medium, and in various embodiments, the content may be delivered electronically and/or capable of being presented electronically. For example, a channel may comprise a website or device (e.g., Facebook, YouTube®, AppleTV®, Pandora®, xBox®, Sony® Playstation®), a uniform resource locator (“URL”), a document (e.g., a Microsoft Word® document, a Microsoft Excel® document, an Adobe .pdf document, etc.), an “ebook,” an “emagazine,” an application or microapplication (as described herein), an SMS or other type of text message, an email, Facebook, twitter, MMS and/or other type of communication technology. In various embodiments, a channel may be hosted or provided by a data partner. In various embodiments, the distribution channel may comprise at least one of a merchant website, a social media website, affiliate or partner websites, an external vendor, a mobile device communication, social media network and/or location based service. Distribution channels may include at least one of a merchant website, a social media site, affiliate or partner websites, an external vendor, or a mobile device communication. Examples of social media sites include Facebook®, Foursquare®, Twitter®, My Space®, LinkedIn®, and the like. Examples of affiliate or partner websites include American Express®, Groupon®, LivingSocial®, and the like. Moreover, examples of mobile device communications include texting, email, and mobile applications for smartphones.

A “consumer profile,” “customer data,” or “consumer profile data” may comprise any information or data about a consumer that describes an attribute associated with the consumer (e.g., a preference, an interest, demographic information, personally identifying information, and the like).

The present system or any part(s) or function(s) thereof may be implemented using hardware, software or a combination thereof and may be implemented in one or more computer systems or other processing systems. However, the manipulations performed by embodiments were often referred to in terms, such as matching or selecting, which are commonly associated with mental operations performed by a human operator. No such capability of a human operator is necessary, or desirable in most cases, in any of the operations described herein. Rather, the operations may be machine operations. Useful machines for performing the various embodiments include general purpose digital computers or similar devices.

Computer system also includes a main memory, such as for example random access memory (RAM), and may also include a secondary memory. The secondary memory may include, for example, a hard disk drive and/or a removable storage drive, representing a floppy disk drive, a magnetic tape drive, an optical disk drive, etc. The removable storage drive reads from and/or writes to a removable storage unit in a well-known manner. Removable storage unit represents a floppy disk, magnetic tape, optical disk, etc. which is read by and written to by removable storage drive. As will be appreciated, the removable storage unit includes a computer usable storage medium having stored therein computer software and/or data.

In various embodiments, secondary memory may include other similar devices for allowing computer programs or other instructions to be loaded into computer system. Such devices may include, for example, a removable storage unit and an interface. Examples of such may include a program cartridge and cartridge interface (such as that found in video game devices), a removable memory chip (such as an erasable programmable read only memory (EPROM), or programmable read only memory (PROM)) and associated socket, and other removable storage units and interfaces, which allow software and data to be transferred from the removable storage unit to computer system.

Computer system may also include a communications interface. Communications interface allows software and data to be transferred between computer system and external devices. Examples of communications interface may include a modem, a network interface (such as an Ethernet card), a communications port, a Personal Computer Memory Card International Association (PCMCIA) slot and card, etc. Software and data transferred via communications interface are in the form of signals which may be electronic, electromagnetic, and optical or other signals capable of being received by communications interface. These signals are provided to communications interface via a communications path (e.g., channel). This channel carries signals and may be implemented using wire, cable, fiber optics, a telephone line, a cellular link, a radio frequency (RF) link, wireless and other communications channels.

The terms “computer program medium” and “computer usable medium” and “computer readable medium” are used to generally refer to media such as removable storage drive and a hard disk installed in hard disk drive. These computer program products provide software to computer system.

In various embodiments, software may be stored in a computer program product and loaded into computer system using removable storage drive, hard disk drive or communications interface. The control logic (software), when executed by the processor, causes the processor to perform the functions of various embodiments as described herein. In various embodiments, hardware components such as application specific integrated circuits (ASICs). Implementation of the hardware state machine so as to perform the functions described herein will be apparent to persons skilled in the relevant art(s).

“Cloud” or “Cloud computing” includes a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. Cloud computing may include location-independent computing, whereby shared servers provide resources, software, and data to computers and other devices on demand. For more information regarding cloud computing, see the NIST's (National Institute of Standards and Technology) definition of cloud computing at http://csrc.nist.gov/publications/nistpubs/800-145/SP800-145.pdf (last visited June 2012), which is hereby incorporated by reference in its entirety.

The computers discussed herein may provide a suitable website or other Internet-based graphical user interface which is accessible by users. In one embodiment, the Microsoft Internet Information Server (IIS), Microsoft Transaction Server (MTS), and Microsoft SQL Server, are used in conjunction with the Microsoft operating system, Microsoft NT web server software, a Microsoft SQL Server database system, and a Microsoft Commerce Server. Additionally, components such as Access or Microsoft SQL Server, Oracle, Sybase, Informix MySQL, Interbase, etc., may be used to provide an Active Data Object (ADO) compliant database management system. In one embodiment, the Apache web server is used in conjunction with a Linux operating system, a MySQL database, and the Perl, PHP, and/or Python programming languages.

The system and method may be described herein in terms of functional block components, screen shots, optional selections and various processing steps. It should be appreciated that such functional blocks may be realized by any number of hardware and/or software components configured to perform the specified functions. For example, the system may employ various integrated circuit components, e.g., memory elements, processing elements, logic elements, look-up tables, and the like, which may carry out a variety of functions under the control of one or more microprocessors or other control devices. Similarly, the software elements of the system may be implemented with any programming or scripting language such as C, C++, C#, Java, JavaScript, VBScript, Macromedia Cold Fusion, COBOL, Microsoft Active Server Pages, assembly, PERL, PHP, awk, Python, Visual Basic, SQL