Systems and methods for cooperative data exchange

Various systems and methods for financial analysis are provided. A system is provided comprising a first node comprising a public facing data store in communication with a private facing data store, wherein the first node further comprises a cooperative lookup module configured to locate a second node, a secure data connection between the first node and the second node, and wherein the first node is configured to request processed internal data from the second node.

FIELD

The disclosure generally relates to financial analysis, and more particularly, to systems and methods for cooperative data exchange.

BACKGROUND

End consumer-facing business entities tend to have limited data relating to their customers. While a business entity may have a customer list containing demographic information about a customer and/or a set of prior transactions conducted by the business entity and the customer, many business entities may not have additional information about their customers. Moreover, it is often difficult to derive insight from this limited data set. It would thus be useful for a business entity to enhance the value of this limited data set to gain additional insights into its consumer base through, for example, a cooperative data exchange.

SUMMARY

Various systems and methods for financial analysis and data analysis are provided herein in various embodiments. A system is provided having a first node comprising a public facing data store in communication with a private facing data store, wherein the first node further comprises a cooperative lookup module configured to locate a second node, a secure data connection between the first node and the second node, and wherein the first node is configured to request processed internal data from the second node. In various embodiments, an analytics module performs a join between the processed internal data and data in the public facing data store to produce a first data set and/or performs a join between the first data set and data in the private facing data store to produce a second data set.

In various embodiments, a method is provided comprising requesting, from a first node, the address of a second node at a third node, wherein the first node comprises a private facing data store and a public facing data store, receiving, at the first node, a response from the third node, connecting, from the first node, to a public facing data store of the second node, via a secure connection, and receiving, at the first node, processed internal data from the second node.

In further embodiments, a method is provided comprising processing internal data to form processed internal data, populating, using a processor, a public facing data store with at least a portion of the processed internal data, and transmitting the processed internal data in response to a query for the processed internal data.

DETAILED DESCRIPTION

In various embodiments, the methods described herein are implemented using the various particular machines described herein. The methods described herein may be implemented using the below particular machines, and those hereinafter developed, in any suitable combination, as would be appreciated immediately by one skilled in the art. Further, as is unambiguous from this disclosure, the methods described herein may result in various transformations of certain articles. The disclosure may be implemented as a method, system or in a computer readable medium.

Business entities (for example, merchants) may improve their businesses by harnessing data related to their customers and consumers in general. As used herein, the term “consumer” may mean any person or entity that consumes or uses an item. As used herein, a customer may mean a person or entity that has purchased and/or may purchase in the future an item from a given business entity, such as a merchant. Thus, a customer list may be a list of people or entities that have purchased or may purchase an item from another entity, such as a merchant.

In various embodiments, cooperative data exchange may be useful for businesses to gain insight into their customers and consumers in general. Cooperative data exchange may also allow businesses to view data relating to their competitors and providers of complementary goods and services. Analytics may be performed on data sets created through a cooperative data exchange to improve business performance and prospects.

Cooperative data exchange100is illustrated inFIG. 1. A cooperative data exchange may comprise multiple nodes. For example, a cooperative data exchange may comprise a first node, second node, third node, fourth node, etc. A node may be any system that comprises a data store and, in various embodiments, also comprise a processor and/or a memory. A node may comprise, for example, a computer system, smart phone, or tablet computing device, as described herein. A node may also be connected to any network, including those described herein. For example, a node may be connected to the Internet, such as directly or through an intranet. A node may also be connected to a virtual private network, including the types described herein.

With continuing reference toFIG. 1, in one embodiment, node102communicates through secure channel114with node108. Node108communicates through secure channel136with node120. Node120communicates with node102through secure channel128. As used herein, the phrase secure channel is used interchangeably with the phrase secure connection, and may include fully secure, substantially secure or partially secure.

Node102comprises private facing data store104and public facing data store106. Private facing data store104may not connect to or otherwise access or be accessed directly by other nodes on cooperative data exchange100. However, public facing data store106may connect directly to public facing data stores of one or more other nodes on cooperative data exchange100. Connection118facilitates communication between private facing data store104and public facing data store106.

Node102also comprises lookup module130. Lookup module130comprises a lookup table. The lookup table comprises addresses of other nodes on cooperative data exchange100. For example, the lookup table may comprise IP addresses, domain names, port information, and other protocol information (e.g., security protocol information) relating to other nodes on cooperative data exchange100. In this manner, the lookup table may be used identify where and how to establish communication from node102to another node on cooperative data exchange100. For example, the lookup table may comprise an entry for node120that exists at IP address 12.34.567.89, communicates on port587, and requires a TLS connection. In the event node102would like to connect to a desired node that is not present on lookup module130, node102may connect with another known node and request (i.e., query) the lookup module of the known node for the address of the desired node. If the known node does not have the requested information, node102may query other known nodes, either simultaneously or sequentially, to determine the desired node's address. For example, if node102would like to connect to node120and does not know the correct port number, node102may query the lookup module of node108to determine the appropriate port. Node102may continue to access the lookup module for each node contained in lookup module130.

With momentary reference toFIG. 4, lookup methodology400is shown. A node may look up other nodes in its own lookup module at lookup members402. At decision point404, if there is a match between the desired node and the lookup table of the node's lookup module, then locate members408is performed. Locate members408may include resolving a domain name with a domain name system and/or establishing a secure channel with the desired node. A secure channel may be any encrypted channel or connextion, such as, for example, via virtual private network, tunneling protocol (e.g., ssh tunnel), SSL, TLS, or the like, as further described herein. If there is no match between the desired node and the lookup table of the node's lookup module at decision point404, then lookup members406is performed. Lookup members406may comprise querying the lookup modules of other, known nodes to find the desired node.

Referring back toFIG. 1, node108comprises private facing data store112and public facing data store110. Private facing data store112may not connect to or otherwise access or be accessed directly by other nodes on cooperative data exchange100. However, public facing data store110may connect directly to public facing data stores of one or more other nodes on cooperative data exchange100. Connection116facilitates communication between private facing data store112and public facing data store110.

Node120comprises private facing data store124and public facing data store122. Private facing data store124may not connect to or otherwise access or be accessed directly by other nodes on cooperative data exchange100. However, public facing data store122may connect directly to public facing data stores of one or more other nodes on cooperative data exchange100. Connection126facilitates communication between private facing data store124and public facing data store122.

Node102may initiate and/or use secure channel114to communicate with public facing data store110of node108. In like manner, node102may initiate and/or use secure channel128to communicate with public facing data store122of node120.

Cooperative data exchanges may be used to share and analyze business information. Merchants may keep customer lists. Customer lists may be populated with customers who “opt-in” to merchant frequent buyer programs (e.g., frequent flyer programs and in-store coupon programs) and/or customers who join a membership-driven merchant (e.g., a warehouse “club”). Moreover, customer lists may be populated with transactional data not tied to an individual's name or other identifying information. Thus, a customer list may contain a customer name, a transaction history (including stock keeping units “SKUs” purchased), and/or customer contact information. Any individual customer on a customer list may be referred to as a first customer or a reference customer.

For merchants who do not keep customer lists, but retain transactional data, a merchant may possess data relating to a particular transaction (e.g., time, date, amount purchased, geographic location, etc). Such data is still considered to be related to a reference customer, even though the reference customer's name is not known.

Customer lists, merchant transactional data and the like, standing alone, have a limited ability to provide a merchant with insight into its customers and consumers generally. Thus, merchants and other businesses may find it advantageous to create a node on a cooperative data exchange, obtain shared data from other nodes, join or merge the shared data with their own data and perform analytics on such data.

In various embodiments, a node comprises a private data store and/or a public data store that comprises internal data. “Internal data” and terms similar to “internal data” may include any data a credit issuer possesses or acquires pertaining to a particular consumer or group of consumers. Internal data may be gathered from a transaction system, such as a closed loop transaction system. 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 may include any data pertaining to a consumer. Consumer demographic data may include consumer name, gender, age, address (including ZIP code and 4 digit extension, also known as “ZIP+4”), telephone number, email address, employer and social security number. Consumer transactional data may include 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.

In various embodiments, as discussed above, a node may contain internal data in a private facing data store, a public facing data store, or a combination thereof. In various embodiments, a node may contain internal data in a private facing data store and then a subset of the internal data in a public facing data store. For example, the internal data in a public facing data store may not contain personally identifiable information, or it may contain a summary of internal data found in the private facing data store, or it may contain data derived from internal data, such as size of wallet or share of wallet information. A size of wallet may comprise a measure of the amount a consumer spends using a credit, debit, and/or charge card. A share of wallet may comprise a measure of the relative share of a consumer's size of wallet that is spent in one industry or one merchant. A secure connection, such as secure channel114, may be used to extract internal data from private facing data store into public facing data store.

Internal data placed in the public facing data store may be indexed or otherwise processed to preserve the underlying internal data as provide while still communicating valuable insight into a given consumer, consumer group, business, or industry. Such internal data may be referred to as processed internal data.

The internal data may be indexed, for example when stored in a public facing data store. For example, an index of data derived from internal data may comprise a measurement that relates the data to another data set. For example, the national average size of wallet of a consumer may be set arbitrarily at 100. A particular consumer may have an average size of wallet twice that of the national average and, thus, could have an indexed value of 200. In this manner, the indexed value could be placed in a public facing data store, so nodes that request this information do not receive the underlying data, but an indexed value as measured against a large pool of other consumers. Indexing may be useful in that is provides concrete trend information yet preserves specific aggregate data. In various embodiments, a large collection of internal data may exist on a node.

Internal data may also be processed to reflect the underlying data. For example, the internal data may comprise a metric that represents spend in various ranges over a given time period, such as the last quarter or year. As an example, a score of 5000 may indicate that ac consumer spent between $5000 and $6000 in the given time period. The internal data may include a range of numbers or a numeric indicator that indicates the trend of a consumer's spend over a given time period. For example, a trend score of +4 may indicate that the consumer has increased spending over the previous 4 months, while a trend score of −4 may indicate that the consumer has decreased spend over the previous 4 months. Further, aggregate data may be derived from the internal data relating to the purchasing of a population of transaction account users within a given industry.

In various embodiments, a node may comprise an analytics module or may be in communication with an analytics module. An analytics module may be any device, software, or combination thereof that is configured to perform analytics. Analytics may comprise one or more statistical analyses or other manipulation of data. The output of the analytics module may provide insight into consumer behaviors and may be used in a variety of contexts, as described herein.

With reference toFIG. 2, portion of a cooperative data exchange200is shown. Node204comprises a public facing data store202that houses transactional information from a first merchant, which may include transaction date, transaction time, transaction amount, SKUs in a given transaction, customer name, customer address (including ZIP+4), customer age, and customer gender. Node204uses secure channel208to connect from the public facing data stores of other various nodes, such as data stores206,210and212. Data store206comprises internal data. Data store210comprises advertising data that comprises data relating to present and historical advertising campaigns and customers who took an action in response to such advertising campaigns. Data store212comprises transactional information from a second merchant, which may include transaction date, transaction time, transaction amount, SKUs in a given transaction, customer name, customer address (including ZIP+4), customer age, customer gender, and or data derived from the same.

Public facing data store may further comprise customers who have purchased a specific product, customers identified as reaching a certain level in a sales cycle, customers who have made purchases through a particular sales channel, customers who have responded to marketing campaigns based on specific offer types, product bundling/product types, specific seasons, marketing creative and specific advertising/marketing channels, customers who are defined as high value through purchase amount (i.e., historical transactional amount), customer geographic information (including instantaneous geographic information obtained from a digital device such as a GPS-equipped smartphone and historical instantaneous geographic information obtained from similar sources) customer preference for a particular merchant or type of merchant, customer media preference or psychographic information (e.g., customer preference of NPR over Fox News), customers defined by the sequence of products that a consumer purchases (e.g., TV, computer, printer, ink), customer response to surveys, customer data collected by third parties (including credit bureaus), customers satisfied with a particular product or brand, customers who attrite, customers who make an insurance claim, customers who have been identified as providing a particular level of return on investment or return to a merchant based on marketing initiatives or purchase history, customers who have viewed a merchant's social network page, ad, and/or feed (e.g., a Facebook, MySpace, and/or LinkedIn page and/or a Twitter or RSS feed), other social network ad, television ad, customer's viewing of an advertising channel which lead to a sale or other action, customers who have a high or low opinion of a particular merchant's brand(s), or any other event or data point that may allow matching or joining with internal data.

Node204may supplement received data from data stores206,210and212with third party data sources. For example, a third party data source may provide customer credit scores, social network histories (which include any information a social network may gather regarding a consumer, for example, posted messages, approximate age and gender of spouse, children and other members of household, pictures, past consumer geographic locations, patterns of past consumer geographic locations, propensity to engage in risky behaviors and the frequency of engaging in the same, marital status, substance use history, dating history, education level, present and past health status including disease status), public records, consumer transactions conducted using alternate payment systems, consumer health status, and any other data relating to consumers who may appear in the internal data. Data from third party data sources may be joined or appended to internal data relating to consumers. For example, if the internal data contains a record for consumer “A” and a third party data source has a history of A's postings on one of A's social network pages, all or a portion of the third party's data may join A's internal data.

Node204may then join or otherwise merge the received data from data stores206,210and212. Performing a join, for example, may be accomplished using any join function known in the art, as described herein. For example, a join may be performed to match records from one data set to another. For example, transaction date, transaction time, and transaction amount from data store202may be joined with transaction date, transaction time, and transaction amount from data store206. The join may then return internal data related to the customer who engaged in the transaction in question. The join may utilize a range of data depending upon the desired results, and the join query may account for differences in time stamps across various systems. For example, if a join fails to return any results, the transaction time may be altered slightly in the event the internal data is a few seconds or minutes different from the first merchant data. As an example, for first merchant data in table first_merchant and internal data in table internal_data, the following query may be used to obtain a customer size of wallet for a customer who engaged in a transaction with the first merchant: SELECT customer_size_wallet, customer_household_income FROM internal_data WHERE first_merchant.transaction_time=internal_data.transaction_time AND first_merchant.transaction_amount=internal_data.transaction_amount AND first_merchant.transaction_date=internal_data.transaction_date. In many cases, only one record will be returned, provided that the first merchant had one transaction for a given transaction time, date and amount. If multiple records are returned, other queries may be used to further match records, for example, by inserting transaction location.

Other data may be derived from the received data. For example, a consumer's social networking history may be queried for mentions of luxury brand names, and photo recognition software may be used to scan the consumer's posted social networking photos for the same. Consumers with high or low frequencies of occurrence may be isolated for further analytics. Photo recognition software may be used to scan the consumer's posted social networking photos for the gender and age of the consumer's spouse and children. Also for example, a consumer's social networking history may be queried for mentions of health status and/or disease state. These data, in combination with any internal data, may be useful in marketing medical and/or lifestyle products suited to that health status and/or disease state.

With reference toFIG. 3, method of using a cooperative data exchange300is illustrated. A node looks up other nodes to access lookup members302. The node then locates other nodes and selects which public facing data store from which to request data to locate members/identify data source304. The node then requests data from the desired public facing data store(s) in request data306. The node may then receive received data from the desired public facing data store(s). The node may process the received data, such as performing a merge or join on the received data to form a final data set. The final data set may then be analyzed in analyze310. Any analytical process may be performed on the final data set.

The cooperative data exchange may thus be used to produce a final data set upon which analytics may be performed to produce analyzed data. The analytics performed may be used in any business or market segment that sells goods, services, or extends credit or otherwise evaluates the creditworthiness of a particular consumer. In various embodiments, businesses will be referred to herein as falling into one of three categories: financial services companies, retail companies, and other companies.

The business cycle in each category may be divided into three phases: acquisition, retention, and disposal. The acquisition phase occurs when a business is attempting to gain new consumers. The acquisition phase includes, for example, targeted marketing, determining what items to offer a consumer, deciding whether to lend to a particular consumer and what the line size or loan should be, and deciding whether to buy a particular loan. The retention phase occurs after a consumer is already associated with the business. In the retention phase, the business interests shift to managing the consumer relationship through, for example, consideration of risk, determination of credit lines, cross-sell opportunities, increasing business from that consumer, and increasing the company's assets under management.

The disposal phase is entered when a business wishes to dissociate itself from a consumer or otherwise end the consumer relationship. The disposal phase can occur, for example, through settlement offers, collections, and sale of defaulted or near-default loans.

Financial services companies include, for example: banks and other lenders, mutual fund companies, financiers of leases and sales, life insurance companies, online brokerages, credit issuers, and loan buyers.

Banks and lenders can utilize the analyzed data in all phases of the business cycle. One exemplary use is in relation to home equity loans and the rating given to a particular bond issue in the capital market. Analyzed data may be useful in issuing home equity lines of credit and automobile loans in a similar manner.

For example, if the holder of a home equity loan borrows from the capital market, the loan holder issues asset-backed securities (“ABS”), or bonds, which are backed by receivables. The loan holder is thus an ABS issuer. The ABS issuer applies for an ABS rating, which is assigned based on the credit quality of the underlying receivables. One of skill in the art will recognize that the ABS issuer may apply for the ABS rating through any application means without altering the spirit and scope of the present invention. In assigning a rating, the rating agencies weigh a loan's probability of default by considering the lender's underwriting and portfolio management processes. Lenders generally secure higher ratings by credit enhancement. Examples of credit enhancement include over-collateralization, buying insurance (such as wrap insurance), and structuring ABS (through, for example, senior/subordinate bond structures, sequential pay vs. pari passu, etc.) to achieve higher ratings. Lenders and rating agencies may use certain characteristics the debtors of the underlying debt obligations into consideration when determining the appropriate level of credit enhancement. Thus, lenders and rating agencies, among others, may join a cooperative data exchange to obtain and/or join data from other sources with their own, for example, to assist in determining the appropriate level of credit enhancement.

During the acquisition phase of a loan, lenders may join a cooperative data exchange to obtain and/or join data from other sources with their own to improve their lending decisions. Before issuing the loan, lenders can obtain data relating to a given potential debtor and use the data to make credit extension evaluations. For example, a lender may join its own data with internal data from a transaction system and determine a potential debtor's size of wallet or a potential debtor's propensity to purchase goods or services associated with those who have poor credit histories. Evaluation leads to fewer bad loans and a reduced probability of default for loans in the lender's portfolio. For a given loan portfolio that has been originated using data analyzed from a cooperative data exchange in accordance with various embodiments, either a higher rating can be obtained with the same degree of over-collateralization, or the degree of over-collateralization can be reduced for a given debt rating. Thus, using data analyzed from a cooperative data exchange at the acquisition stage of the loan reduces the lender's overall borrowing cost and loan loss reserves.

During the retention phase of a loan, data analyzed from a cooperative data exchange can be used to track a consumer. Based on the trends observed, the lender can make various decisions regarding the consumer relationship.

The gaming industry can use data analyzed from a cooperative data exchange, for example, during the acquisition and retention phases of the business cycle. Casinos often extend credit to their wealthiest and/or most active players, also known as “high rollers.” The casinos can use data analyzed from a cooperative data exchange to gain better insight into these consumer and adjust their accommodations to better fit their customer's needs. For example, casinos can gain insight into a customer's accommodation preferences or food preferences.

Communications providers, such as telephone service providers, often contract into service plans with their consumers. In addition to improving their targeted marketing strategies, communications providers can use data analyzed from a cooperative data exchange during the acquisition and retention phases to better market new phones and phone services. For example, a telephone service provider may target advertising to those consumers whose cellular phone contracts are about to expire.

Members of the travel industry can make use of data analyzed from a cooperative data exchange in the acquisition and retention stages of the business cycle. For example, a hotelier typically has a brand of hotel that is associated with a particular “star-level” or class of hotel. In order to capture various market segments, hoteliers may be associated with several hotel brands that are of different classes. During the acquisition phase of the business cycle, a hotelier may use the data analyzed from a cooperative data exchange to better target individuals that have appropriate spend capacities for various classes of hotels. During the retention phase, the hotelier may use the data analyzed from a cooperative data exchange to determine, for example, when a particular individual may be associated with increased spending overall or increased spending in a given industry, such as the travel industry. Based on that determination, the hotelier can market a higher class of hotel to the consumer in an attempt to convince the consumer to upgrade.

One of skill in the relevant art(s) will recognize that many of the above described applications of one or more consumer data cohort attributes may be utilized by other industries and market segments without departing from the spirit and scope of the present invention. For example, the strategy of using one or more consumer data cohort attributes to model an industry's “best consumer” and targeting individuals sharing characteristics of that best consumer can be applied to nearly all industries.

A web client includes any device (e.g., personal computer or smartphone or tablet computer) which communicates via any network, for example such as those discussed herein. Such browser applications comprise Internet browsing software installed within a computing unit or a system to conduct online transactions and/or communications. These computing units or systems may take the form of a computer or set of computers, although other types of computing units or systems may be used, including laptops, notebooks, hand held computers, personal digital assistants, set-top boxes, workstations, computer-servers, main frame computers, mini-computers, PC servers, pervasive computers, network sets of computers, personal computers, such as tablet computers (e.g., tablets running Android, iPads), iMACs, and MacBooks, kiosks, terminals, point of sale (POS) devices and/or terminals, televisions, or any other device capable of receiving data over a network. A web-client may run Microsoft Internet Explorer, Mozilla Firefox, Google Chrome, Apple Safari, Opera, or any other of the myriad software packages available for browsing the internet.

Practitioners will appreciate that a web client may or may not be in direct contact with an application server. For example, a web client may access the services of an application server through another server and/or hardware component, which may have a direct or indirect connection to an Internet server. For example, a web client may communicate with an application server via a load balancer. In an exemplary embodiment, access is through a network or the Internet through a commercially-available web-browser software package.

As those skilled in the art will appreciate, a web client includes an operating system (e.g., Windows NT, 95/98/2000/CE/Mobile/XP/Vista/7, OS2, UNIX, Linux, Solaris, MacOS, MacOS X, PalmOS, iOS, Android, etc.) as well as various conventional support software and drivers typically associated with computers. A web client may include any suitable personal computer, network computer, workstation, personal digital assistant, cellular phone, smartphone, minicomputer, mainframe or the like. A web client can be in a home or business environment with access to a network. In an exemplary embodiment, access is through a network or the Internet through a commercially available web-browser software package. A web client may implement security protocols such as Secure Sockets Layer (SSL) and Transport Layer Security (TLS). A web client may implement several application layer protocols including http, https, ftp, and sftp.

“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/groups/SNS/cloud-computing/cloud-def-v15.doc (last visited Feb. 4, 2011), which is hereby incorporated by reference in its entirety.

As used herein, “issue a debit”, “debit” or “debiting” refers to either causing the debiting of a stored value or prepaid card-type financial account, or causing the charging of a credit or charge card-type financial account, as applicable.

Any databases discussed herein may include relational, hierarchical, graphical, or object-oriented structure and/or any other database configurations. Common database products that may be used to implement the databases include DB2 by IBM (Armonk, N.Y.), various database products available from Oracle Corporation (Redwood Shores, Calif.), Microsoft Access or Microsoft SQL Server by Microsoft Corporation (Redmond, Wash.), MySQL by MySQL AB (Uppsala, Sweden), or any other suitable database product. Moreover, the databases may be organized in any suitable manner, for example, as data tables or lookup tables. Each record may be a single file, a series of files, a linked series of data fields or any other data structure. Association of certain data may be accomplished through any desired data association technique such as those known or practiced in the art. For example, the association may be accomplished either manually or automatically. Automatic association techniques may include, for example, a database search, a database merge, GREP, AGREP, SQL, using a key field in the tables to speed searches, sequential searches through all the tables and files, sorting records in the file according to a known order to simplify lookup, and/or the like. The association step may be accomplished by a database merge function, for example, using a “key field” in pre-selected databases or data sectors. Various database tuning steps are contemplated to optimize database performance. For example, frequently used files such as indexes may be placed on separate file systems to reduce In/Out (“I/O”) bottlenecks.

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, PKI, and symmetric and asymmetric cryptosystems. Any form of encryption may be used to implement a secure channel, as described herein.

The computing unit of the web client may be further equipped with an Internet browser connected to the Internet or an intranet using standard dial-up, cable, DSL or any other Internet protocol known in the art. Transactions originating at a web client may pass through a firewall in order to prevent unauthorized access from users of other networks. Further, additional firewalls may be deployed between the varying components of CMS to further enhance security.

Firewall may include any hardware and/or software suitably configured to protect CMS components and/or enterprise computing resources from users of other networks. Further, a firewall may be configured to limit or restrict access to various systems and components behind the firewall for web clients connecting through a web server. Firewall may reside in varying configurations including Stateful Inspection, Proxy based, access control lists, and Packet Filtering among others. Firewall may be integrated within an web server or any other CMS components or may further reside as a separate entity. A firewall may implement network address translation (“NAT”) and/or network address port translation (“NAPT”). A firewall may accommodate various tunneling protocols to facilitate secure communications, such as those used in virtual private networking A firewall may implement a demilitarized zone (“DMZ”) to facilitate communications with a public network such as the Internet. A firewall may be integrated as software within an Internet server, any other application server components or may reside within another computing device or may take the form of a standalone hardware component.

The computers discussed herein may provide a suitable website or other Internet-based graphical user interface which is accessible by users. In various embodiments, 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.

In various embodiments, each participant is equipped with a computing device in order to interact with the system and facilitate online commerce transactions. The customer has a computing unit in the form of a personal computer, although other types of computing units may be used including laptops, notebooks, hand held computers, set-top boxes, cellular telephones, touch-tone telephones and the like. The merchant has a computing unit implemented in the form of a computer-server, although other implementations are contemplated by the system. The bank may have a computing center shown as a main frame computer. However, the bank computing center may be implemented in other forms, such as a mini-computer, a PC server, a network of computers located in the same of different geographic locations, or the like. Moreover, the system contemplates the use, sale or distribution of any goods, services or information over any network having similar functionality described herein

The merchant computer and the bank computer may be interconnected via a second network, referred to as a payment network. The payment network which may be part of certain transactions represents existing proprietary networks that presently accommodate transactions for credit cards, debit cards, and other types of financial/banking cards. The payment network is a closed network that is assumed to be secure from eavesdroppers. Exemplary transaction networks may include the American Express®, VisaNet® and the Veriphone® networks. A transaction system may comprise a payment network.

The electronic commerce system may be implemented at the customer and issuing bank In an exemplary implementation, the electronic commerce system is implemented as computer software modules loaded onto the customer computer and the banking computing center. The merchant computer does not require any additional software to participate in the online commerce transactions supported by the online commerce system.

As will be appreciated by one of ordinary skill in the art, the system may be embodied as a customization of an existing system, an add-on product, upgraded software, a stand alone system, a distributed system, a method, a data processing system, a device for data processing, and/or a computer program product. Accordingly, the system may take the form of an entirely software embodiment, an entirely hardware embodiment, or an embodiment combining aspects of both software and hardware. Furthermore, the system may take the form of a computer program product on a computer-readable storage medium having computer-readable program code means embodied in the storage medium. Any suitable computer-readable storage medium may be utilized, including hard disks, CD-ROM, optical storage devices, magnetic storage devices, and/or the like.

The process flows and screenshots illustrated or described are merely embodiments and are not intended to limit the scope of the disclosure. For example, the steps recited in any of the method or process descriptions may be executed in any order and are not limited to the order presented. It will be appreciated that the following description makes appropriate references not only to the steps and user interface elements, but also to the various system components as described herein.

Phrases and terms similar to “business” or “merchant” may be used interchangeably with each other and shall mean any person, entity, distributor system, software and/or hardware that is a provider, broker and/or any other entity in the distribution chain of goods or services. For example, a merchant may be a grocery store, a retail store, a travel agency, a service provider, an on-line merchant or the like.

The terms “payment vehicle,” “financial transaction instrument,” “transaction instrument” and/or the plural form of these terms may be used interchangeably throughout to refer to a financial instrument.

Phrases similar to a “payment processor” may include a company (e.g., a third party) appointed (e.g., by a merchant) to handle transactions for merchant banks. Payment processors may be broken down into two types: front-end and back-end. Front-end payment processors have connections to various transaction accounts and supply authorization and settlement services to the merchant banks' merchants. Back-end payment processors accept settlements from front-end payment processors and, via The Federal Reserve Bank, move money from an issuing bank to the merchant bank. In an operation that will usually take a few seconds, the payment processor will both check the details received by forwarding the details to the respective account's issuing bank or card association for verification, and may carry out a series of anti-fraud measures against the transaction. Additional parameters, including the account's country of issue and its previous payment history, may be used to gauge the probability of the transaction being approved. In response to the payment processor receiving confirmation that the transaction account details have been verified, the information may be relayed back to the merchant, who will then complete the payment transaction. In response to the verification being denied, the payment processor relays the information to the merchant, who may then decline the transaction.

Phrases similar to a “payment gateway” or “gateway” may include an application service provider service that authorizes payments for e-businesses, online retailers, and/or traditional brick and mortar merchants. The gateway may be the equivalent of a physical point of sale terminal located in most retail outlets. A payment gateway may protect transaction account details by encrypting sensitive information, such as transaction account numbers, to ensure that information passes securely between the customer and the merchant and also between merchant and payment processor.

Phrases similar to “vendor software” or “vendor” may include software, hardware and/or a solution provided from an external vendor (e.g., not part of the merchant) to provide value in the payment process (e.g., risk assessment).