Patent Publication Number: US-2013232018-A1

Title: Method and system for engaging in a transaction between a consumer and a merchant

Description:
RELATED APPLICATIONS 
     This continuation patent application claims priority to and the benefit of U.S. Patent Application No. 11/710,883, filed on Feb. 26, 2007, the contents of which are incorporated herein by reference in their entirety. 
    
    
     BACKGROUND OF THE INVENTION 
     1. Field of Invention 
     The present invention relates generally to credit systems and credit issuer/consumer relationships, and in particular, to a method and system for engaging in a transaction between a consumer and a merchant, offering credit at a point-of-sale and identifying at least one optimal credit product of a credit issuer for a consumer and offering the optimal product to the consumer for consideration. 
     2. Description of the Related Art 
     According to the prior art, when a consumer wishes to obtain a credit product, such as a credit card or credit account, from a credit issuer, such as a bank, the consumer fills out an application, whether in hard copy of electronic form, and submits this application to the credit issuer. Once the appropriate information is received from the consumer, the credit issuer will make a decision regarding whether the applicant is eligible for credit product. If the person is, indeed, eligible, and meets the necessary requirements, the credit issuer establishes and account and provides the consumer with either the appropriate account information, or in most cases, a physical credit card for use in engaging in transactions. In addition, in order to successfully consummate the transaction, the consumer must have some preexisting relationship with some credit provider in order to facilitate any non-cash transaction, e.g., an online transaction, a telephone transaction, etc. Therefore, in order to engage in some non-cash purchase, the consumer must obtain credit, initiate the transaction with the merchant, and utilize the obtained credit product to consummate the, transaction and receive the goods and/or services. Therefore, there is a need for some payment option in a non-cash transaction that does not require a preexisting credit relationship. 
     In some situations, the consumer applies for a credit level or credit product type for which he or she is not eligible. For example, the consumer may apply for a “gold” credit card from the credit issuer, but is only eligible for the “silver” credit card. Accordingly, the credit issuer will issue a notification to the applicant that he or she is unfortunately not eligible for the “gold” card, but is for the “silver” card. Such an offer from the credit issuer is often referred to as a “down sell”. However, presently, this “down sell” is only used in connection with a single type of card and/or in connection with a single and discrete credit issuer. Therefore, the “down sell” represents only a downward qualification, which would be preferable to outright denial of the applicant, since the issuer is attempting to maximize sales and profit. 
     The “down sell” is fairly common, but extremely limited, i.e., limited to one issuer, limited to one type of card, etc. However, the credit issuer may have a number of different goals and objectives with respect to new or upgrading applicants. For example, the credit issuer may wish to control transactional costs, maximize sales, control risk, maximize long-term sales, maximize short-term sales, encourage co-branded products, etc. There are presently no methods and systems that take specific credit issuer (or affiliated merchant) objectives into account when offering credit products to the consumer, whether in a “down sell” or “up sell” situation. 
     There are many available credit products offerings to which a consumer may respond. However, the consumer may also have certain goals and objectives when searching for the optimal credit product, e.g., low interest rate, high loan level, minimal penalties, special advantages and perks, etc. Further, the consumer often would like to consider a variety of credit issuers, as well as a variety of credit products for each credit issuer. Therefore, there is a need for a method and system that would take the consumer&#39;s objectives into account, and provide the consumer with the optimal credit product (or appropriate products). 
     SUMMARY OF THE INVENTION 
     Therefore, it is an object of the present invention to provide a method and system for providing instant credit to a consumer engaged in a non-cash relationship with a merchant. It is a further object of the present invention to provide a method and system for facilitating a credit transaction between a consumer and a merchant. It a still further object of the present invention to provide a method and system for identifying optimal credit products for use in a transaction between a credit issuer and a consumer. It is another object of the present invention to provide a method and system for identifying optimal credit products that take into account consumer selection data and/or credit issuer selection data. It is a further object of the present invention to provide a method and system for identifying optimal credit products that offer at least one optimal credit product to a consumer based upon a credit issuer&#39;s goals and objectives. It is a still further object of the present invention to provide a method and system for identifying optimal credit products that offer at least one optimal credit product to a consumer based upon a consumer&#39;s goals and objectives. It is yet another object of the present invention to provide a method and system for identifying optimal credit products that operates effectively in both a “down sell” and an “up sell” situation. It is a further object of the present invention to provide a method and system of identifying optimal credit products that a optimized and presented to the consumer for selection. 
     Accordingly, in one embodiment, the present invention is directed to a method for providing instant credit by a credit issuer to a consumer at a point-of-sale of a merchant. The method includes the steps of: initiating or engaging in a transaction between the consumer and the merchant at the point-of-sale; obtaining a consumer/transaction data set including a plurality of data fields populated with data reflecting the consumer, the merchant, the transaction, the credit issuer or any combination thereof; analyzing at least a portion of the consumer/transaction data set; based upon the results of the analysis: (i) offering instant credit by the credit issuer to the consumer at the point-of-sale; or (ii) preventing an offer of instant credit by the credit issuer to the consumer at the point-of-sale; and if instant credit is offered to the consumer: (i) accepting, by the consumer, the offer of instant credit; and (ii) consummating the transaction between the consumer and the merchant using the provided instant credit. 
     The present invention is further directed to a method for identifying at least one optimal credit product from a plurality of credit products of at least one credit issuer to a consumer. The method includes the steps of: providing a credit issuer selection data set including a plurality of data fields to a central optimization database; providing a consumer selection data set including a plurality of data fields to the central optimization database; determining at least one optimal credit product to be offered by the at least one credit issuer to the consumer based upon: (i) at least one data field in the credit issuer selection data set; (ii) at least one data field in the consumer selection data set, or any combination thereof; and offering at least one optimal credit product, by the at least one credit issuer, to the consumer. 
     In a further embodiment, the present invention is directed to an apparatus for providing instant credit by a credit issuer to a consumer at a point-of-sale of a merchant. The apparatus includes a storage mechanism including an central database, and at least one input mechanism for transmitting a consumer/transaction data set including a plurality of data fields populated with data reflecting the consumer, the merchant, the transaction, the credit issuer or any combination thereof, to the central database. Further, a processor mechanism is configured to: (i) analyze at least a portion of the consumer/transaction data set; and (ii) based upon the results of the analysis: (a) offer instant credit by the credit issuer to the consumer at the point-of-sale; or (ii) prevent an offer of instant credit by the credit issuer to the consumer at the point-of-sale. The apparatus also includes an output mechanism for offering the instant credit to the consumer at the point-of-sale. 
     In another embodiment, the present invention is directed to an apparatus for identifying at least one optimal credit product from a plurality of credit products of at least one credit issuer to a consumer. The apparatus includes a storage mechanism with a central optimization database and at least one input mechanism for transmitting a credit issuer selection data set including a plurality of data fields to a central optimization database and for transmitting a consumer selection data set including a plurality of data fields to the central optimization database. The apparatus further includes a processor mechanism configured to determine at least one optimal credit product to be offered by the at least one credit issuer to the consumer based upon: (i) at least one data field in the credit issuer selection data set; (ii) at least one data field in the consumer selection data set, or any combination thereof. In addition, an output mechanism is provided for offering at least one optimal credit product, by the at least one credit issuer, to the consumer. 
     These and other features and characteristics of the present invention, as well as the methods of operation and functions of the related elements of structures and the combination of parts and economies of manufacture, will become more apparent upon consideration of the following description and the appended claims with reference to the accompanying drawings, all of which form a part of this specification, wherein like reference numerals designate corresponding parts in the various figures. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the invention. As used in the specification and the claims, the singular form of “a”, “an”, and “the” include plural referents unless the context clearly dictates otherwise. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a schematic view of one embodiment of a method and system for providing instant credit by a credit issuer to a consumer at a point-of-sale of a merchant according to the present invention; 
         FIG. 2  is a schematic view of one embodiment of a method and system for identifying at least one optimal credit product according to the present invention; 
         FIG. 3  is a schematic view of an example screenshot of a credit product offering to a consumer according to the present invention; 
         FIG. 4  is a schematic view of another embodiment of a method and system for identifying at least one optimal credit product according to the present invention; 
         FIG. 5  is a schematic view of a further embodiment of a method and system for identifying at least one optimal credit product according to the present invention; 
         FIG. 6  is a schematic view of a still further embodiment of a method and system for identifying at least one optimal credit product according to the present invention; and 
         FIG. 7  is a schematic view of yet another embodiment of a method and system for identifying at least one optimal credit product according to the present invention. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     It is to be understood that the invention may assume various alternative variations and step sequences, except where expressly specified to the contrary. It is also to be understood that the specific devices and processes illustrated in the attached drawings, and described in the following specification, are simply exemplary embodiments of the invention. 
     The present invention is directed to a method and system  100  for providing instant credit by a credit issuer CI to a consumer C at a point-of-sale POS of a merchant M. In particular, as shown in one preferred and non-limiting embodiment in  FIG. 1 , the present invention begins with the consumer C initiating, engaging in or otherwise interacting with a merchant M at the point-of-sale POS. While the system  100  is particularly useful in connection with online transactions, e.g., transactions over the Internet, at a merchant M portal, etc., it is equally useful in extending instant credit to consumers C at any type or variety of point-of-sale, such as over the telephone, in the mail, at an in-store location, etc. In the present embodiment, the point-of-sale POS is at an online location of the merchant M, such that the point-of-sale POS is a consumer&#39;s computer  102  (output mechanism). Accordingly, all of the consumer C, the credit issuer CI and the merchant M interact with or otherwise communicate with the computer  102  over a network, e.g., the Internet, a virtual private network, etc. 
     Next, a consumer/transaction data set  104  is obtained by or otherwise transmitted to a central system  106 , which includes a processor mechanism  108  and a storage mechanism  110 . This consumer/transaction data set  104  includes a plurality of fields  112  populated with data reflecting the consumer C, the merchant M, the transaction, the credit issuer CI, etc. Once this data set  104  is received at the central system  106 , it is analyzed or processed by the processor mechanism  108 . Based upon this analysis, the credit issuer CI and/or central system  106  offers (or authorizes the offer of) instant credit to the consumer C at the point-of-sale POS, in this embodiment, the computer  102 . In addition, and also based upon this analysis, the credit issuer CI and/or the central system  106  may prevent such an offer to the consumer C. If the instant credit is offered to the consumer C, the system  100  further enables the consumer C to accept the offer of instant credit, and consummates the transaction between the consumer C and the merchant M using the instant credit. 
     The consumer/transaction data set  104  may include a variety of data points and information. For example, the fields  112  may be populated with data reflecting a consumer&#39;s C name, a consumer C key, a consumer C identification, an account number, an address, a city, a state, a zip code, a country, a telephone number, an e-mail address, a social security number, a date of birth, the merchant&#39;s M name, an identification, a credit issuers&#39; CI name, credit issuer CI data, credit data, credit product data, credit rate data, credit terms data, credit product benefits data, a product identification, a service identification, a company identity, a merchant M identity, consumer credit account balance, merchant M history, private label entity data, affiliated private label entity, transaction data, transaction type, transaction amount, etc. 
     Accordingly, the presently-invented system  100  allows for the receipt of instant credit by a qualified consumer C at the point-of-sale POS. Further, this instant credit is extended to the consumer automatically, and used to consummate the transaction with the merchant M, such that the merchant M ships the goods or begins the services based upon this credit. In this manner, the consumer C only need provide certain basic information and data in the consumer/transaction data set  104  in order to obtain instantaneous credit from the credit issuer CI and/or central system  106 . 
     In a further embodiment, the system  100  includes a verification/authentication system  112 . This system  112  is in communication with or otherwise a part of the central system  106 , and is used to verify or authenticate the consumer C prior to offering the instant credit to him or her. This verification by the verification/authentication system  112  is based at least in part upon the data in the consumer/transaction data set  104 . Of course, if the system  100  requires additional information or data from the consumer C, the merchant M, the credit issuer CI or other parties, it either automatically requests such information, or otherwise instructs the user to obtain this data. Therefore, the verification/authentication system  112  ensures that the offer of instant credit to the consumer C is appropriate and directed to the correct entity or identified consumer C. 
     In another embodiment, the system includes a credit analysis system  114 , which is supplied with a consumer data set  116  having a plurality of data fields  118  populated with data reflecting the consumer C, the merchant M, the transaction, etc. This system  114  is used to analyze the credit of the consumer C prior to extending the instant credit, and may also be used to analyze the type of transaction, the amount of the contemplated transaction, etc. The credit analysis system  114  analyzes one or more of the fields  118  in the consumer data set  116  and makes a credit decision. As discussed above, if the system  114  requires additional information or data to successfully complete the analysis, this data may be requested, as appropriate, from any of the parties to the transaction. 
     Based upon the analysis by the credit analysis system  114 , the system  106  and/or the credit issuer CI makes the decision to offer the instant credit to the consumer C, or prevent such an offer from being presented at the point-of-sale POS. Of course, it is also envisioned that the credit analysis system  114  obtains the required information or data in the consumer data set  116  prior to the consumer C engaging in the transaction with the merchant M. This means that the consumer C may be pre-screened and/or pre-approved for the instant credit prior to the actual transaction. During the transaction, once the consumer/transaction data set  104  is transmitted or communicated to the central system  106 , the consumer C may be verified by the verification/authentication system  112 , and the instant credit is automatically provided or offered to the consumer C at the point-of-sale POS based upon the pre-screening or pre-approval. Such a method could also be used to automatically deny or prevent the instant credit from being offered to the consumer C. 
     The credit analysis performed by the credit analysis system  114  may perform a variety of functions and drive a variety of responses or communications from the credit issuer CI and/or the central system  106  to the consumer C. For example, based upon the credit analysis, the system  100  may provide a communication  120  to the consumer C that he or she is only able to use a limited amount (partial amount of the transaction) of instant credit at the point-of-sale. A communication  122  may be used to inform the consumer C that he or she is, indeed, eligible for instant credit. Another communication  124  may be used to inform the consumer C that the transaction is not eligible for instant credit, based upon transaction data, transaction type, transaction amount, merchant data, consumer data, etc. For example, the transaction type may be too risky to provide instant credit to the consumer C, or alternatively, the amount of the transaction may be much too high for the provision of instant credit. However, the communication  124  may indicate that the consumer C is eligible for another type of transaction or more-limited transaction amount. 
     The consumer data set  116  may include a variety of data. For example, the fields  118  may be populated with data reflecting a consumer&#39;s C name, a consumer C key, a consumer C identification, an account number, an address, a city, a state, a zip code, a country, a telephone number, an e-mail address, a social security number, a date of birth, the merchant&#39;s M name, an identification, a credit issuers&#39; CI name, credit issuer CI data, credit data, credit product data, credit rate data, credit terms data, credit product benefits data, a product identification, a service identification, a company identity, a merchant M identity, consumer C credit account balance, merchant M history, private label entity data, affiliated private label entity, transaction data, transaction type, transaction amount historical interaction between the consumer C and the credit issuer CI, historical data, merchant M data, previous consumer/credit issuer transaction data, consumer C creditworthiness, consumer C credit quality, size of purchase, type of purchase, consumer C demographic data, consumer C age, consumer C location, consumer C income, consumer C credit data, consumer C purchasing behavior, consumer C purchasing behavior with a specified credit issuer CI, credit issuer CI sales objectives, credit issuer CI goals, consumer C purchasing history, consumer C status, consumer C lifetime value to credit issuer CI, credit issuer CI input data, consumer C input data, product credit rate, product credit terms, product benefit data, product relationships, product tie-ins, consumer C purchasing behavior at a specified merchant M, merchant M objectives, merchant M goals, consumer C lifetime value to merchant M, merchant M input data, a transaction amount, a consumer C purchase demographic, a product identification, a service identification, consumer C type, a company identity, a merchant M identity, a third-party risk score, risk data, authentication data, verification data, consumer C rating data, profitability data, credit risk data, fraud risk data, transaction risk data, denial data, processing data, a general credit risk score, a credit bureau risk score, a prior approval, prior report data, previous transaction data, a geographical risk factor, credit account data, bankcard balance data, delinquency data, credit segment data, previous transaction data, time between transactions data, previous transaction amount, previous transaction approval status, previous transaction time stamp data, a response code, active trades in database, public record data, trade line data, transaction medium, credit segment data, consumer payment type, consumer C payment method, consumer C payment history, consumer C account history, consumer C credit account balance, merchant M history, private label entity data, affiliated private label entity, consumer/merchant historical data, negative consumer/credit issuer data, positive consumer/credit issuer data, etc. 
     In another embodiment, prior to the offer of instant credit to the consumer C, the system  100  provides for the presentation of credit terms  126  to the consumer C. For example, the consumer C may be presented with basic credit terms, a Terms &amp; Conditions page, a basic credit contract or other simplified agreement. In general, the credit terms  126  would signify the consumer&#39;s willingness to eventually satisfy the debt incurred by using the instant credit of the credit issuer CI. 
     After the transaction is consummated, and the goods and/or services delivered or performed, the consumer C must then satisfy his or her debt. Therefore, the system  100  further includes some payment system  128 , which presents, to the consumer C, a variety and plurality of payment options PO. These payment options PO are then used to satisfy some or all of the instant credit provided by the credit issuer CI and/or central system  106  to the consumer C. As discussed hereinafter, the type, order, method and system for use in providing these payment options PO may be optimized. Further, these payment options PO may be in the form of an e-check, a check, cash, an ACH product, a credit card, a new credit account, an online credit account, an existing credit account, a minimum payment on account, a debit account, etc. 
     Prior to presenting the payment options PO to the consumer C, the credit analysis system  114  may be used to analyze the credit of the consumer C and provide appropriate payment options PO thereto. For example, and as discussed in greater detail hereinafter, the credit analysis system  114  may make the necessary credit decision to list and/or optimize the various payment options PO available to the consumer C based upon the consumer/transaction data set  104  and/or the consumer data set  116 . 
     Once presented, the consumer C selects one or more of the payment options PO to satisfy the incurred debt. In one embodiment, the consumer C selects a payment option PO that is a credit product, such as a credit card, an online credit account, etc. Therefore, if eligible, verified, authenticated, etc., the credit account is activated and used to pay off the instant credit involved in the transaction. Of course, the consumer C may select more than one payment option PO and segregate all or a portion of the total instant credit amount to be satisfied by one or more separate payment options PO. For example, one payment option PO may be a credit offer with certain benefits if the amount transferred is at a specified amount. Accordingly, the consumer C may wish to allocate that amount to that specific payment option PO, and satisfy the remaining instant credit debt using some other vehicle, e.g., cash, e-check, debit, etc. In this manner, the consumer C is provided with a seamless method and system  100  for obtaining instant credit at the point-of-sale POS, and satisfying the debt using a variety of payment options in the future. 
     The present invention is also directed to a method and system  10  for identifying one or more optimal credit products for use in connection with and between one or more consumers C and one or more credit issuers CI. The present method and system  10  is equally useful in connection with multiple consumers C, multiple credit issuers CI (whether affiliated or not), various types and levels of credit products, as well as co-brand situations and merchant affiliations. Accordingly, the present method and system  10  is an optimization process that, when provided the appropriate data, serves to identify at least one, and typically multiple, credit products for offering from one or more credit issuers CI to the consumer C. For example, the system  10  could be used in presenting the payment options PO to the consumer C for satisfying the debt incurred as instant credit in connection with system  100 . 
     In one embodiment, a credit issuer selection data set  12 , which includes multiple data fields  14 , is provided to an optimization database  16 , such as a database resident on a central server or centralized data warehouse. In addition, a consumer selection data set  18 , which includes a plurality of data fields  20 , is also provided to the optimization database  16 . Of course, one or more fields  112 ,  118  of the consumer/transaction data set  104  and/or the consumer data set  116  may be used to complement or enhance the consumer selection data set  18 . Therefore, the optimization database  16  may be linked to or associated with the database on the storage mechanism  110 . Once the appropriate data fields  14 ,  20  have been obtained and stored in the optimization database  16 , a determination is made. Specifically, the method and system  10  determines and identifies at least one optimal credit product  22  to be offered by the credit CI to the consumer C. Further, this determination is made based upon at least one data field  14  in the credit issuer selection data set  12  and/or at least one data field  20  in the consumer selection data set  18 . In this manner, one or more optimal credit products  22  are identified. Next, this optimal credit product  22  is offered by the credit issuer CI to the consumer C or otherwise presented to the consumer C, such as through the payment system  128  discussed above in connection with system  100 . 
     As discussed above, a plurality of optimal credit products  22  may be offered or presented to the consumer C. Accordingly, these optimal credit products  22  can be presented or offered to the consumer in a variety of forms. For example, the products  22  can be offered in hard copy form, wireless form, electronic form, on a computing device, on a display device, over the Internet, as a web page, on a graphical user interface, at a point-of-sale, before a transaction, during a transaction, upon completion of a transaction, etc. In this manner, the optimal credit products  22  can be presented to the consumer in a form of a listing  24 , which the consumer C can peruse and make some eventual selection. 
     As seen in  FIG. 2 , the consumer C enters the consumer selection data set  18  through an interface device  26 , such as a personal computer  102  operating in a network (such as the Internet), and this data set  18  is transmitted to the database  16 . At least one, and preferably multiple, credit issuers CI have provided their individual credit issuer selection data sets  12  to the optimization database  16 , and the system  10  processes this data and provides the listing  24  to the consumer C. Of course, when the interface device  26  used by the consumer C is a personal computer  102 , it is envisioned that the listing  24  of optimal credit products  22  is provided in electronic form and transmitted to the same interface device  26 . Such communication would maximize the efficiency and speed of the method, and allow the system  10  to make virtually dynamic decisions and otherwise perform the optimization process for creating a listing  24 . It is also envisioned that the method and system  10  can be used to create a variety of credit issuer reports  28  that can be provided to the credit issuer CI. As discussed in detail hereinafter, these credit issuer reports  28  could provide summary data, consumer C selection data, consumer C demographics, competing credit issuer CI data, etc. 
     In another embodiment, and as illustrated in  FIG. 3 , the optimal credit products  22  can be ranked prior to presentation to the consumer C. For example, the optimal credit products  22  can be ranked according to at least one data field  14  in the credit issuer selection data set  12 , and/or at least one data field  20  in the consumer selection data set  18 . These ranked optimal credit products  22  can be organized to form the above-referenced listing  24 , and subsequently presented as an organized list to the consumer C. 
     As seen in  FIG. 3 , the consumer C may request a ranking based upon multiple ranking fields  30 . Furthermore, it may be the credit issuer CI that selects the ranking fields  30  for use in presenting multiple credit products  22  to the consumer C.  FIG. 3  illustrates the presentation of at least three credit products, namely credit product A, credit product B and credit product C. Of course, as indicated by the arrow at the bottom right corner, this listing  24  could include any number of credit products  22 . On the left is the credit product  22  offerings ranked according to field X, and on the right are credit product  22  offerings ranked according to field Y. Accordingly, it is envisioned that, based upon the differing data in the ranking fields  30 , the credit products A, B and C could achieve different rankings. 
     A variety of data and data fields could be presented to the consumer C, and these data or data fields could be specifically associated with a specific optimal credit product  22 . For example, and based upon the credit issuer selection data set  12  and consumer selection data set  18 , an information box  32  could be provided. This information box  32  may include the perceived benefits of the optimal credit product  22 , as well as the perceived concerns regarding the credit product  22 . Of course, these benefits and concerns could be based either upon the data fields  20  in the consumer selection data set  18  and/or the data fields  14  and the credit issuer selection dataset  12 . 
     In addition, these benefits and concerns could be used in connection with the ranking field  30  during the optimization process for ranking the credit products  22 . For example, a numeric value may be assigned to specific benefits or concerns by the consumer C and/or the credit issuer CI. Based upon the numeric values, and the representative totals, the credit products  22  could be ranked. Also as seen in  FIG. 3 , the terms and conditions (T&amp;Cs) could be provided to the consumer C in another information box  32 . In addition, since such T&amp;Cs are typically lengthy, it is envisioned that the information box  32  could be expanded for a fuller view by the consumer C. 
     It may be beneficial to the credit issuer CI that the listing  24  be presented to the consumer C in an individual and sequential form, such that the consumer C would only consider a single optimal credit product  22  at a time. In this manner, the credit issuer CI could more specifically direct the consumer&#39;s C attention to and encourage the consumer C to select a credit issuer CI desired product. Accordingly, the presentation of the credit products  22  to the consumer C may be under the control of the credit issuer CI through one or more of the data fields  14  in the credit issuer selection data set  12 . 
     However, it is also envisioned that a system  10  can act as a selection and optimization engine on the consumer C side. For example, as illustrated in  FIG. 3 , the consumer C may select multiple ranking fields  30  and perform a side-by-side comparison of multiple credit products  22  based upon these ranking fields  30 . In addition, it is envisioned that the consumer C could continue selecting and sorting within each ranking field  30  listing in order to eventually identify the absolute optimal credit product  22  for ultimate selection. Therefore, the method and system  10  of the present invention can be used as a general index for obtaining, reviewing, locating and identifying the optimal credit product  22  for selection by the consumer C. 
     As discussed above, the presently-invented method and system  10  can be used in connection with a variety of different credit products  22 . For example, the credit product  22  may be a credit card, an online payment account, a co-branded credit account, a pre-approved credit product, a private label credit account, a debit account, a stored value account, a single transaction account, an option account to pay when due, etc. It is further envisioned that, based upon the data fields  20  in the consumer selection data set  18  and/or data fields  14  in the credit issuer selection data set  12 , the listing  24  presented to the consumer C may include different types of credit products  22 . For example, based upon the information and data provided by the consumer C, the method and system  10  may provide a mixture of credit products  22  that would fulfill the consumer&#39;s C needs. Accordingly, the assessment of the consumer&#39;s C needs could be taken into account and discussed in the information box  32  with respect to the benefits and concerns. Accordingly, the listing  24  may not be simply an offer of different credit cards, but an offer of different credit cards, online payment accounts, debit accounts, stored value accounts, etc. Any number of variations is envisioned for the optimization and subsequent offer and presentation of the optimal credit products  22  to the consumer C. Further, as discussed above, one or more of these credit products  22  could be selected in whole or in part to satisfy the debt incurred as instant credit at the point-of-sale POS. 
     As discussed above, the determination step may be based upon a computer-implemented algorithm, where the data fields  14  of the credit issuer selection data set  12  and the data fields  20  of the consumer selection data set  18  serve as the baseline input data for use in the optimization. Further, this computer-implemented algorithm may use a variety of formulae and data points, and the input data may be consumer C data, merchant data, credit issuer CI data, consumer C credit worthiness, consumer C credit quality, size of purchase, type of purchase, consumer C demographic data, consumer C age, consumer C location, consumer C income, consumer C credit data, consumer C purchasing behavior, consumer C purchasing behavior with a specified credit issuer CI, credit issuer CI sales objectives, credit issuer CI goals, consumer C purchasing history, consumer C status, consumer C lifetime value to the credit issuer CI, credit issuer CI input data, consumer C input data, product credit rate, product credit terms, product benefit data, product relationships, product tie-ins, consumer C purchasing behavior at a specified merchant, merchant objectives, merchant goals, consumer C lifetime value to merchant, merchant input data, etc. It is further envisioned that this algorithm can be modifiable, dynamic and configurable. For example, the algorithm can be based upon the credit issuer CI goals, the consumer C goals or other system  10  determinations. 
     In one preferred and non-limiting embodiment, the computer-implemented algorithm is directed to some initiation or request by the consumer C for the funding of a purchase or purchases. The system  100  is configured to assemble the appropriate dimensional data, calculate or build the appropriate algorithm and select an “offer” vector that represents the ranked offer map for each individual request. 
     On example of this process (as implemented by the system  100 ) is as follows. For the dimension of “Customer Profile”, the metrics include: CP1—Number of open revolving trades; CP2—Total Revolving balances; CP3—Total revolving monthly payment; and CP4—Mortgage Present. For the dimension of “Purchase Profile”, the metrics include: PP1—Item Category Code; PP2—Purchase Price; PP3—Merchant Identification; and PP4—Purchase Terms. For the dimension of “Merchant Profile”, the metrics include: MP1—Merchant Category; MP2—Primary Accepted Loan Type; MP3—Secondary Accepted Loan Type; and MP4—Debit Acceptance Rate Index. In this example, the offer data, referred to as the “Offer Universe”, would include the following data points: Revolving Loan Type 1 (RLTP1); Revolving Loan Type 2 (RLTP2); Promotional Loan Type 1 (PLTP1); Promotional Loan Type 2 (PLTP2); Installment Loan Type 1 (ILTP1); Installment Loan Type 2 (ILTP2); Direct Debit Type 1 (DDTP1); and Direct Debit Type 2 (DDTP2). 
     Using this information and data, the following algorithms are used to determine a desired “offer score” for use in connection with an offer to the consumer C. In this example, the following algorithms are utilized: (1) Consumer Profile Thread (CPTF): CPT Factor=0.0034065*PP1+0.0476329*PP2+0.1283116*PP3+0.871211*PP4; (2) Purchase Profile Thread (PPTF): PPT Factor=0.2132485*PP1+0.1324954*PP2+0.7438293*PP3+0.9329232*PP4; and (3) Merchant Profile Thread (MPTF): MPT Factor=0.3565035*PP1 30 0.1003432*PP2+0.9512324*PP3+0.3293441*PP4. Next, and in this preferred embodiment of the algorithm, an “Offer Score” is calculated as follows: 0.4030312*CPTF+0.5121321*PPTF+0.7868544*MPTF+0.003431*CPTF 2 +0.0019432*PPTF 2 +0.0059695*MPTF 2 . Finally, an Offer Vector Map is created as illustrated in Table 1. It should be noted that, in this example, the 1st Quintile is designated as the lowest 20% of outcomes based upon the Offer Score, and the Offer Vector ranks the top three offers. In addition, it should be noted that this set of algorithms (or determination method) is only one example thereof, and these formulae may be modified in order to optimize the process to meet the goals of the consumer C, the merchant M, the credit issuer CI, etc. 
     
       
         
           
               
               
               
               
             
               
                   
                 TABLE 1 
               
               
                   
                   
               
               
                   
                 Offer Score Range 
                 Offer Vector 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
            
               
                   
                 1st Quintile 
                 PLTP1 
                 RLTP2 
                 RLTP1 
               
               
                   
                 2nd Quintile 
                 PLTP2 
                 PLTP1 
                 ILTP2 
               
               
                   
                 3rd Quintile 
                 PLTP2 
                 RLTP2 
                 RLTP1 
               
               
                   
                 4th Quintile 
                 RLTP1 
                 ILTP1 
                 DDTP1 
               
               
                   
                 5th Quintile 
                 ILTP2 
                 DDTP1 
                 DDTP2 
               
               
                   
                   
               
            
           
         
       
     
     As discussed above, the consumer C can be provided with or have access to an interface device  26 , such as a personal computer  102 , website, electronic device, etc. It is also envisioned that the credit issuer CI also have access to some interface device  26  for inputting the appropriate data fields  14  of the credit issuer selection data set  12 , and otherwise interacting with the system  10 . 
     Gathering the appropriate data to make the optimization and offering decisions occurs throughout the method and process. Further, the data fields  20  of the consumer selection data set  18  may contain a variety of data and information. For example, the data fields  20  may be populated with data reflecting a consumer C name, a consumer C key, a consumer C identification, an account number, an address, a city, a state, a zip code, a country, a telephone number, an e-mail address, a social security number, a date of birth, the merchant&#39;s name, an identification, a credit issuer CI name, credit issuer CI data, credit data, credit product data, credit rate data, credit terms data, credit product benefits data, a product identification, a service identification, a company identity, a merchant identity, consumer C credit account balance, merchant history, private label entity data, affiliated private label entity, or any combination thereof. 
     Similarly, the method and system  10  collects a large amount of data and information from the credit issuer CI. For example, the data fields  14  of the credit issuer selection data set  12  may be populated with data reflecting historical interaction between the consumer C and the credit issuer CI, historical data, merchant data, previous consumer/credit issuer transaction data, consumer C creditworthiness, consumer C credit quality, size of purchase, type of purchase, consumer C demographic data, consumer C age, consumer C location, consumer C income, consumer C credit data, consumer C purchasing behavior, consumer C purchasing behavior with a specified credit issuer CI, credit issuer CI sales objectives, credit issuer CI goals, consumer C purchasing history, consumer C status, consumer C lifetime value to credit issuer CI, credit issuer CI input data, consumer C input data, product credit rate, product credit terms, product benefit data, product relationships, product tie-ins, consumer C purchasing behavior at a specified merchant, merchant objectives, merchant goals, consumer C lifetime value to merchant, merchant input data, a transaction amount, a consumer C purchase demographic, a product identification, a service identification, consumer C type, a company identity, a merchant identity, a third-party risk score, risk data, authentication data, verification data, consumer C rating data, profitability data, credit risk data, fraud risk data, transaction risk data, denial data, processing data, a general credit risk score, a credit bureau risk score, a prior approval, prior report data, previous transaction data, a geographical risk factor, credit account data, bankcard balance data, delinquency data, credit segment data, previous transaction data, time between transactions data, previous transaction amount, previous transaction approval status, previous transaction time stamp data, a response code, active trades in database, public record data, trade line data, transaction medium, credit segment data, consumer C payment type, consumer C payment method, consumer C payment history, consumer C account history, consumer C credit account balance, merchant history, private label entity data, affiliated private label entity, consumer/merchant historical data, negative consumer/credit issuer data, positive consumer/credit issuer data, or any combination thereof. 
     As part of the optimization process, the optimal credit product  22  can be based upon data input by the consumer C and/or the credit issuer CI. Accordingly, the credit issuer selection data set  12  and/or the consumer selection data set  18  may include credit product offering data that reflects an established offer to the consumer C of a service, an item, a discount, a redemption, a coupon, a voucher, a non-cash benefit, an incentive, a ticket, an invitation, an event, etc. Accordingly, such offerings may be listed under the “benefits” section of the listing  24  of the optimal credit products  22 . For example, a non-cash benefit may be the offering of “frequent flyer miles” for an airline, or “points” for use in purchasing merchandise, etc. Many consumers C consider such non-cash benefits and other incentives when choosing an appropriate credit product  22 . 
     In order to appropriately reward or offer benefits or incentives to the consumer C, it is envisioned that the credit issuer selection data set  12  includes certain consumer recognition data. For example, this consumer recognition data may be selected based upon some current transaction between the consumer C and a merchant, a previous transaction between the consumer C and the merchant, a historical transaction between the consumer C and the merchant, etc. In addition, the consumer recognition data may include tracking data specific to the consumer C, a value reflective of the consumer&#39;s C transaction history with a merchant, a current transaction between the consumer C and the credit issuer CI, a previous transaction between the consumer C and the credit issuer CI, and historical transaction data between the consumer C and the credit issuer CI. In addition, the consumer recognition data may include some value reflective of the consumer&#39;s C transaction history with the credit issuer CI, data associated with transactions with the credit issuer CI, transaction-specific data, consumer-specific data, transaction frequency data, transaction amount data, cumulative transaction frequency data, cumulative transaction frequency data, cumulative transaction amount data, consumer demographic data, etc. Accordingly, a variety of historical and/or collected data may be used to offer certain special benefits and incentives to the consumer C for selecting the optimal credit product  22 . In addition, this consumer recognition data may be “negative” data, such that the credit issuer CI limits the optimal credit product  22  offerings based upon some past or historical negative event, such as credit problems. 
     As discussed above, the system  10  includes the central optimization database  16 , which maintains the data fields  14  of the credit issuer selection data set  12  and the data fields  20  of the consumer selection data set  18 . In addition, the optimization process may be engaged in or carried out by the system  10 , a central processing system, the central system  106 , the credit issuer CI, a merchant, a seller, an Internet site, an online entity, a web store, a telephone seller, a group of credit issuers CI, a group of merchants, an organization of credit issuers CI, an organization of merchants, an entity, a corporation, a company, an offerer of goods, an offerer of services, an affiliation of a plurality of entities, etc. In this manner, the system  10  (and associated method) could be maintained and engaged in by a variety of entities to maximize its application and usefulness to any specific entity. Therefore, the method could be optimized to meet the credit issuer&#39;s CI goals and objectives and/or the consumer C goals and objectives, or some balanced system maintained by a third party. 
     As discussed above, and as illustrated in  FIG. 2 , a credit issuer report  28  may be provided to the credit issuer CI. This credit issuer report  28  may include compiled credit issuer-specific data, credit product  22  data, consumer C data, consumer C selection data, consumer C behavior data, consumer C survey data, etc. Accordingly, after the consumer C selects at least one of the optimal credit products  22 , it is envisioned that certain selection data may be obtained from the consumer C regarding his or her selection of a specific optimal credit product  22 . For example, this selection data may include the reason for a selection, a reason for not selecting, data directed to the consumer selection, credit product  22  data, credit rate data, credit terms, credit benefits data, credit affiliation data, etc. Using this “survey”-type data, the credit issuer CI can better understand the consumer C behavior and the reasons behind his or her selection or failure to select. 
     As discussed above, the consumer C may transmit a data field  20  (as part of the consumer selection data set  18 ) that reflects the consumer&#39;s C requested credit product. Therefore, the method and system  10  must determine whether the consumer C is eligible for the requested credit product. This selection is referred to as the consumer preferred credit product  34 . Alternatively, the consumer C may not have a specific credit product  34  in mind, but instead a preferred set of product terms  36 . Therefore, as seen in  FIG. 4 , the consumer C provides the consumer preferred credit product  34  and/or credit product terms  36  to the system  10  as part of the data fields  20  transmitted to the optimization database  16 . 
     The consumer C may receive various responses back from the system  10  based upon the data fields  20  of the consumer selection data set  18 , as well as data fields  14  of the credit issuer selection data set  12 . For example, the consumer C may receive a listing  24  indicating all of the optimal credit products  22  for which the consumer C is eligible, with a specific notation of the consumer preferred credit product  34 . If the consumer C is not eligible for his or her consumer preferred credit product  34 , a listing  24  may include such an indication, but indicate that the consumer C is eligible for other credit products  22 . 
     Also as seen in  FIG. 4 , another option or response from the system  10  to the consumer C would be an indication that the consumer C is not eligible for any credit product  22 . In many cases, the system  10  may require additional data fields  20  from the consumer C for identifying the optimal credit product  22  and/or the consumer&#39;s C eligibility for any of the credit products  22 . Accordingly, one screen or response may be that the consumer C may be eligible for certain credit products  22 , but additional data is required. Of course, if the consumer C has already been provided with instant credit, the consumer C would, of course, be eligible for some type of credit or debit product. 
     When engaging in the optimization process, the credit issuers CI may provide the system  10  and/or optimization database  16  with credit issuer target goal data  38 . This data would be reflective of the individual credit issuer&#39;s CI goals and objectives regarding the types of preferred consumers C or data about these consumers C for which to tailor their credit products  22 . It is envisioned that the credit issuer target goal data  38  from each credit issuer CI may be further analyzed and combined and/or compared with system credit issuer priority data  40 . In this manner, the system  10  may include its own unique data fields, targets, goals and objectives regarding the optimization process and the matching of credit issuers CI and consumers C. Ultimately, the system  10  may have control over what is offered to and presented to the consumer C for selection. 
     In another embodiment, as illustrated in  FIG. 5 , the consumer C transmits his or her preferred credit product  34 , together with the appropriate data fields  20 , in the consumer selection data set  18  to the system  10  and the optimization database  16 . Further, multiple credit issuers CI (indicated as A and B), provide individual and discrete credit issuer selection data sets  12  to the system  10  and optimization database  16 . It should be noted that, at this point, the consumer C has indicated that his or her preferred credit product  34  is a credit product of credit issuer A. After the optimization process, the system  10  may transmit a message to the consumer C that he or she is eligible for a credit product of credit issuer B. Of course, if the consumer C is also eligible for a credit product of credit issuer A, the system  10  could also indicate this in response to the consumer C, but may place it on a different page or different area. In this manner, the system  10  may have located a different optimal credit product  22  for the consumer C, based upon either the consumer C data fields  20  and/or the credit issuer CI data fields  14 . It is further envisioned that the sequential listing of optimal credit products  22  may serve to urge the consumer C to select a specified and optimal credit product  22 , as determined by the system  10 . 
     As seen in  FIG. 6 , the present invention is further directed to an apparatus and system  10  for identifying the optimal credit products  22 , as discussed above. In particular, the apparatus and system  10  would include a storage mechanism  42  including the central optimization database  16 . Both the consumer C and the credit issuer CI could interface with an input mechanism  44  for transmitting the consumer selection data set  18  and credit issuer selection data set  12 . In addition, the system  10  would include a processor mechanism  46  that is configured to determine one or more optimal credit products  22  to be offered by the credit issuer CI to the consumer C. Furthermore, the processor mechanism  46  could be programmed with software or hardware in order to execute an algorithm  48  for determining the optimal credit products  22 , the respective rankings and list generation. Finally, the system  10  would include an output mechanism  50  for offering and presenting the optimal credit products  22  to the consumer C. Such an arrangement is illustrated in schematic form in  FIG. 6 . 
     Yet another embodiment is illustrated in  FIG. 7 . In this embodiment, the method and system  10  are in the form of a wireless network system, where communications are occurring over a network N. Accordingly, the consumer C transmits the data fields  20  and the consumer selection data set  18  to the system  10  over the network N. Furthermore, the credit issuers CI provide data fields  14  of the credit issuer selection data set  12  over the network N to the system  10 . In this embodiment, the consumers C and credit issuers CI use an interface device  26  in the form of a wireless computing device, such as a computer or terminal connected to the Internet or other device capable of wireless communication. Of course, the method and system  100  described above could also be used in connection with and over the network N. 
     Next, the listing  24  of optimal credit products  22  is transmitted to the interface device  26  of the consumer C, and the consumer&#39;s C selection can be transmitted to the system  10  and/or directly to the credit issuer CI. Accordingly, the system  10  may facilitate direct communications and contact between the consumer C and the selected credit issuer CI. In addition, the above-discussed credit issuer report  28  may be transmitted wirelessly to the interface device  26  of the respective credit issuer CI, or alternatively, a hard copy of the credit issuer report  28  may be sent to the credit issuer CI. 
     In this manner, the present invention provides a method and system  10  for identifying one or more optimal credit products  22  for offering and presentation to a consumer C. Further, the method and system  10  can take into account the consumer selection data set  18  and/or the credit issuer selection data set  12  during the optimization process. The optimal credit products  22  may be identified based upon the credit issuer&#39;s CI goals and objectives and/or the consumer&#39;s C goal and objectives. Therefore, the present invention provides a method and system  10  for identifying optimal credit products  22  that operates effectively in both a “down sell” and “up sell” situation. Further, the present invention provides a system  100  for engaging in an instant credit transaction between a consumer C and a merchant M, which allows for the satisfaction of the debt after the transaction has been consummated between the consumer C and merchant M. 
     Although the invention has been described in detail for the purpose of illustration based on what is currently considered to be the most practical and preferred embodiments, it is to be understood that such detail is solely for that purpose and that the invention is not limited to the disclosed embodiments, but, on the contrary, is intended to cover modifications and equivalent arrangements that are within the spirit and scope of the appended claims. For example, it is to be understood that the present invention contemplates that, to the extent possible, one or more features of any embodiment can be combined with one or more features of any other embodiment.