Patent Publication Number: US-2021192527-A1

Title: Artificial intelligence enhanced transaction suspension

Description:
BACKGROUND 
     People continue to try to use fraud to improperly obtain funds from users. As many parties in the electronic payment environment have applied technology to try to prevent fraud, additional technology has become available that makes fraudulent merchants appear more legitimate. Further, the fraudulent merchants attempt to prey on less sophisticated users to obtain funds using the improved technology. 
     SUMMARY 
     The following presents a simplified summary of the present disclosure in order to provide a basic understanding of some aspects of the disclosure. This summary is not an extensive overview. It is not intended to identify key or critical elements of the disclosure or to delineate its scope. The following summary merely presents some concepts in a simplified form as a prelude to the more detailed description provided below. 
     At a high level, fraudulent transactions may cause problems for many parties in an electronic transaction. Attempts at fraudulent transactions may evolve and change. In response, approaches to combat the attempted fraudulent transactions may also evolve and change. By reducing fraud, electronic commerce may operate more effectively. 
     A system and method are described which attempt to connect merchants with phone numbers that are known to be associated with fraudulent users. In addition, transactions associated with similar phone numbers that have been rejected by similar members of the community also may be rejected as likely being fraudulent. Further, responsible parties are contacted to verify transaction where the responsible parties may be separate from the people that accepted the transaction. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The invention may be better understood by references to the detailed description when considered in connection with the accompanying drawings. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the invention. In the figures, like reference numerals designate corresponding parts throughout the different views. 
         FIG. 1  shows an illustration of an exemplary payment system for determining a real-time optimal price for an item; 
         FIG. 2A  shows a first view of an exemplary payment device for use with the system of  FIG. 1 ; 
         FIG. 2B  shows a second view of an exemplary payment device for use with the system of  FIG. 1 ; 
         FIG. 3  shows an exemplary machine learning architecture; 
         FIG. 4  shows an exemplary artificial intelligence architecture; 
         FIG. 5  is a flowchart of a method for determining a real-time optimal price for a product within the system of  FIG. 1 ; and 
         FIG. 6  shows an exemplary computing device that may be physically configured to execute the methods and include the various components described herein. 
     
    
    
     Persons of ordinary skill in the art will appreciate that elements in the figures are illustrated for simplicity and clarity so not all connections and options have been shown to avoid obscuring the inventive aspects. For example, common but well-understood elements that are useful or necessary in a commercially feasible embodiment are not often depicted in order to facilitate a less obstructed view of these various embodiments of the present disclosure. It will be further appreciated that certain actions and/or steps may be described or depicted in a particular order of occurrence while those skilled in the art will understand that such specificity with respect to sequence is not actually required. It will also be understood that the terms and expressions used herein are to be defined with respect to their corresponding respective areas of inquiry and study except where specific meanings have otherwise been set forth herein. 
     SPECIFICATION 
     The present invention now will be described more fully with reference to the accompanying drawings, which form a part hereof, and which show, by way of illustration, specific exemplary embodiments by which the invention may be practiced. These illustrations and exemplary embodiments are presented with the understanding that the present disclosure is an exemplification of the principles of one or more inventions and is not intended to limit any one of the inventions to the embodiments illustrated. The invention may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. Among other things, the present invention may be embodied as methods, systems, computer readable media, apparatuses, components, or devices. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. The hardware may be local, may be remote or may be a combination of local and remote. The following detailed description is, therefore, not to be taken in a limiting sense. 
       FIG. 1  generally illustrates one embodiment of a payment system  100  for determining whether a phone number related to the transaction indicates the transaction may be fraudulent. The system  100  may include a computer network  102  that links one or more systems and computer components. In some embodiments, the system  100  includes a user computer system  104 , a merchant computer system  106 , a payment network system  108 , and a fraud calculation system which may embody artificial intelligence  110 . 
     The network  102  may be described variously as a communication link, computer network, internet connection, etc. The system  100  may include various software or computer-executable instructions or components stored on tangible memories and specialized hardware components or modules that employ the software and instructions to identify related transaction nodes for a plurality of transactions by monitoring transaction communications between users and merchants. 
     The various modules may be implemented as computer-readable storage memories containing computer-readable instructions (i.e., software) for execution by one or more processors of the system  100  within a specialized or unique computing device. The modules may perform the various tasks, methods, blocks, sub-modules, etc., as described herein. The system  100  may also include both hardware and software applications, as well as various data communications channels for communicating data between the various specialized and unique hardware and software components. 
     Networks are commonly thought to comprise the interconnection and interoperation of hardware, data, and other entities. A computer network, or data network, is a digital telecommunications network which allows nodes to share resources. In computer networks, computing devices exchange data with each other using connections, i.e., data links, between nodes. Hardware networks, for example, may include clients, servers, and intermediary nodes in a graph topology. In a similar fashion, data networks may include data nodes in a graph topology where each node includes related or linked information, software methods, and other data. It should be noted that the term “server” as used throughout this application refers generally to a computer, other device, program, or combination thereof that processes and responds to the requests of remote users across a communications network. Servers serve their information to requesting “clients.” The term “client” as used herein refers generally to a computer, program, other device, user and/or combination thereof that is capable of processing and making requests and obtaining and processing any responses from servers across a communications or data network. A computer, other device, set of related data, program, or combination thereof that facilitates, processes information and requests, and/or furthers the passage of information from a source user to a destination user is commonly referred to as a “node.” Networks generally facilitate the transfer of information from source points to destinations. A node specifically tasked with furthering the passage of information from a source to a destination is commonly called a “router.” There are many forms of networks such as Local Area Networks (LANs), Pico networks, Wide Area Networks (WANs), Wireless Networks (WLANs), etc. For example, the Internet is generally accepted as being an interconnection of a multitude of networks whereby remote clients and servers may access and interoperate with one another. 
     A user computer system  104  may include a processor  145  and memory  146 . The user computing system  104  may include a server, a mobile computing device, a smartphone, a tablet computer, a Wi-Fi-enabled device, wearable computing device or other personal computing device capable of wireless or wired communication, a thin client, or other known type of computing device. The memory  146  may include various modules including instructions that, when executed by the processor  145  control the functions of the user computer system generally and integrate the user computer system  104  into the system  100  in particular. For example, some modules may include an operating system  150 A, a browser module  1508 , a communication module  150 C, and an electronic wallet module  150 D. In some embodiments, the electronic wallet module  150 D and its functions described herein may be incorporated as one or more modules of the user computer system  104 . In other embodiments, the electronic wallet module  150 D and its functions described herein may be incorporated as one or more sub-modules of the payment network system  108 . In some embodiments, a responsible party  117  is in communication with the user computer system  104 . 
     In some embodiments, a module of the user computer system  104  may pass user payment data to other components of the system  100  to facilitate determining a real-time fraud determination. For example, one or more of the operating system  150 A, a browser module  1508 , a communication module  150 C, and an electronic wallet module  150 D may pass data to a merchant computer system  106  and/or to the payment network system  108  to facilitate a payment transaction for a good or service. Data passed from the user computer system  104  to other components of the system may include a customer name, a customer ID (e.g., a Personal Account Number or “PAN”), address, current location, and other data. 
     The merchant computer system  106  may include a computing device such as a merchant server  129  including a processor  130  and memory  132  including components to facilitate transactions with the user computer system  104  and/or a payment device  200  ( FIG. 2 ) via other entities of the system  100 . In some embodiments, the memory  132  may include a transaction communication module  134 . The transaction communication module  134  may include instructions to send merchant messages  134 A to other entities (e.g.,  104 ,  108 ,  110 ) of the system  100  to indicate a transaction has been initiated with the user computer system  104  and/or payment device  200  including payment device data and other data as herein described. The merchant computer system  106  may include a merchant transaction repository  142  and instructions to store payment and other merchant transaction data  142 A within the transaction repository  142 . The merchant transaction data  142 A may only correspond to transactions for products with the particular merchant or group of merchants having a merchant profile (e.g.,  164 B,  164 C) at the payment network system  108 . 
     The merchant computer system  106  may also include a product repository  143  and instructions to store product data  143 A within the product repository  143 . For each product offered by the merchant computer system  106 , the product data  143 A may include a product name, a product UPC code, an item description, an item category, an item price, a number of units sold at a given price, a merchant ID, a merchant location, a customer location, a calendar week, a date, a historical price of the product, a merchant phone number(s) and other information related to the product. In some embodiments, the merchant computer system  106  may send merchant payment data corresponding to a payment device  200  ( FIG. 2 ) to the payment network system  108  or other entities of the system  100 , or receive user payment data from the user computer system  104  in an electronic wallet-based or other computer-based transaction between the user computer system  104  and the merchant computer system  106 . 
     The merchant computer system  106  may also include a fraud module  152  having instructions to facilitate determining fraudulent transactions offered by the merchant computer system  106  to the user computer system  104 . In some embodiments, the fraud module  152  may communicate with one or more of the payment network system  108  and the artificial intelligence engine  110  to receive fraud data  144  from a backend system (e.g., the artificial intelligence engine  110 ) or to determine the fraud data  144  locally at the merchant computer system  106  via the fraud module  152  and a fraud API  152 A. 
     The fraud API  152 A may include instructions to access one or more backend components (e.g., the payment network system  108 , the artificial intelligence engine  110 , etc.) and/or the local fraud module  152  to configure a fraud graphical interface  1528  to dynamically present and apply the fraud data  144  to products or services  143 A offered by the merchant computer system  106  to the user computer system  104 . A merchant historical fraud determination module  152 C may include instructions to mine merchant transaction data  143 A and determine a list of past fraudulent phone numbers to get historical fraud information on the merchant. 
     The payment network system  108  may include a payment server  156  including a processor  158  and memory  160 . The memory  160  may include a payment network module  162  including instructions to facilitate payment between parties (e.g., one or more users, merchants, etc.) using the payment system  100 . The module  162  may be communicably connected to an account holder data repository  164  including payment network account data  164 A. 
     The payment network account data  164 A may include any data to facilitate payment and other funds transfers between system entities (e.g.,  104 ,  106 ). For example, the payment network account data  164 A may include account identification data, account history data, payment device data, etc. The module  162  may also be communicably connected to a payment network system transaction repository  166  including payment network system global transaction data  166 A. 
     The global transaction data  166 A may include any data corresponding to a transaction employing the system  100  and a payment device  200  ( FIG. 2 ). For example, the global transaction data  166 A may include, for each transaction across a plurality of merchants, data related to a payment or other transaction using a PAN, account identification data, a product or service name, a product or service UPC code, an item or service description, an item or service category, an item or service price, a number of units sold at a given price, a merchant ID, a merchant location, a merchant phone number(s), a customer location, a calendar week, and a date, corresponding to the product data  143 A for the product that was the subject of the transaction or a merchant phone number. The module  162  may also include instructions to send payment messages  167  to other entities and components of the system  100  in order to complete transactions between users of the user computer system  104  and merchants of the merchant computer system  106  who are both account holders within the payment network system  108 . 
     The artificial intelligence engine  110  may include one or more instruction modules including an fraud calculation module  112  that, generally, may include instructions to cause a processor  114  of a fraud server  116  to functionally communicate with a plurality of other computer-executable steps or sub-modules, e.g., sub-modules  112 A,  112 B,  112 C,  112 D and components of the system  100  via the network  102 . These modules  112 A,  112 B,  112 C,  112 D may include instructions that, upon loading into the server memory  118  and execution by one or more computer processors  114 , dynamically determine fraud data for a product  143 A or a merchant  106  using various stores of data  122 A,  124 A in one more databases  122 ,  124 . As an example, sub-module  112 A may be dedicated to dynamically determine fraud based on a telephone number or parts of a telephone number associated with a merchant  106 . 
     With brief reference to  FIGS. 2A and 2B , an exemplary payment device  200  may take on a variety of shapes and forms. In some embodiments, the payment device  200  is a traditional card such as a debit card or credit card. In other embodiments, the payment device  200  may be a fob on a key chain, an NFC wearable, or other device. In other embodiments, the payment device  200  may be an electronic wallet where one account from a plurality of accounts previously stored in the wallet is selected and communicated to the system  100  to execute the transaction. As long as the payment device  200  is able to communicate securely with the system  100  and its components, the form of the payment device  200  may not be especially critical and may be a design choice. For example, many legacy payment devices may have to be read by a magnetic stripe reader and thus, the payment device  200  may have to be sized to fit through a magnetic card reader. In other examples, the payment device  200  may communicate through near field communication and the form of the payment device  200  may be virtually any form. Of course, other forms may be possible based on the use of the card, the type of reader being used, etc. 
     Physically, the payment device  200  may be a card and the card may have a plurality of layers to contain the various elements that make up the payment device  200 . In one embodiment, the payment device  200  may have a substantially flat front surface  202  and a substantially flat back surface  204  opposite the front surface  202 . Logically, in some embodiments, the surfaces  202 ,  204  may have some embossments  206  or other forms of legible writing including a personal account number (PAN)  206 A and the card verification number (CVN)  206 B. In some embodiments, the payment device  200  may include data corresponding to the primary account holder, such as payment network account data  164 A for the account holder. A memory  254  generally and a module  254 A in particular may be encrypted such that all data related to payment is secure from unwanted third parties. A communication interface  256  may include instructions to facilitate sending payment data  143 B,  143 A such as a payment payload, a payment token, or other data to identify payment information to one or more components of the system  100  via the network  102 . 
     With reference to  FIG. 3 , a machine learning (ML) architecture  300  may be used with the fraud calculation module  112  of system  100  in accordance with the current disclosure. In some embodiments, an AI module  112 D of the artificial intelligence system  110  may include instructions for execution on the processor  114  that implement the ML architecture  300 . The ML architecture  300  may include an input layer  302 , a hidden layer  304 , and an output layer  306 . The input layer  302  may include inputs  308 A,  308 B, etc., coupled to the fraud calculation module  112  and represent those inputs that are observed from actual product, customer, and merchant data in the transaction data  142 A,  166 A. The hidden layer  304  may include weighted nodes  310  that have been trained for the transactions being observed. Each node  310  of the hidden layer  304  may receive the sum of all inputs  308 A,  308 B, etc., multiplied by a corresponding weight. The output layer  306  may present various outcomes  312  based on the input values  308 A,  308 B, etc., and the weighting of the hidden layer  304 . Just as a machine learning system for a self-driving car may be trained to determine hazard avoidance actions based on received visual input, the machine learning architecture  300  may be trained to analyze a likely outcome for a given set of inputs based on thousands or even millions of observations of previous customer/merchant transactions. For example, the architecture  300  may be trained to determine fraud data  144  to be associated with the product data  143 A. 
     During training of the machine learning architecture  300 , a dataset of inputs may be applied and the weights of the hidden layer  310  may be adjusted for the known outcome (e.g., an actual fraud rating for a phone number) associated with that dataset. As more datasets are applied, the weighting accuracy may improve so that the outcome prediction is constantly refined to a more accurate result. In this case, the merchant transaction repository  142  and/or the payment network system repository  166  respectively including transaction data  142 A and  166 A may provide datasets for initial training and ongoing refining of the machine learning architecture  300 . 
     Additional training of the machine learning architecture  300  may include the an artificial intelligence engine (AI engine)  314  providing additional values to one or more controllable inputs  316  so that outcomes may be observed for particular changes to the transaction data  142 A and  166 A. The values selected may represent different data types such as selected digits of the phone number, community responses, merchant ratings and other alternative data presented at various points in the transaction process with the product data and may be generated at random or by a pseudo-random process. By adding controlled variables to the transaction process, over time, the impact may be measured and fed back into the machine learning architecture  300  weighting to allow capture of an impact on a proposed change to the process in order to optimize the determination of the fraud data  144 . Over time, the impact of various different data at different points in the transaction cycle may be used to predict an outcome for a given set of observed values at the inputs layer  302 . 
     After training of the machine learning architecture  300  is completed, data from the hidden layer may be fed to the artificial intelligence engine  314  to generate values for controllable input(s)  316  to optimize the fraud data  144 . Similarly, data from the output layer may be fed back into the artificial intelligence engine  314  so that the artificial intelligence engine  314  may, in some embodiments, iterate with different data to determine via the trained machine learning architecture  300 , whether the fraud data  144  is accurate, and other determinations. 
     With reference to  FIG. 4 , in other embodiments, the machine learning architecture  300  and artificial intelligence engine  314  may include a second instance of a machine learning architecture  400  and/or an additional node layer may be used. In some embodiments, a fraud data identification layer  402  may determine an optimum fraud determination  404  from observed inputs  404 A,  404 B. A transaction fraud layer  406  with outputs  408 A,  408 B, etc., may be used to generate transaction fraud recommendations  410  to an artificial intelligence engine  412 , which in turn, may modify one or more of telephone data generally and the fraud data in particular when communicating this data via an appropriate SDK. 
       FIG. 5  may illustrate a method that may be executed by the system. At a high level, the method may attempt to detect suspect transactions. Suspect transactions are transactions which may be for a fraudulent good or service. At block  500 , purchase information may be received at a payment network system from an electronic purchase device  200 . As mentioned previously, the purchase information may contain a variety of information including the merchant name, a transaction value and sometimes a merchant phone number  113  which may be received by a user  104 . The electronic purchase device may be any electronic device that is capable of receiving transaction data from a user such as a PAN associated with a credit card. Sample electronic purchase devices may include a Point of Sale (POS) terminal, an e-commerce web server, or an electronic cash register. 
     At block  505 , the method may determine if a phone number  113  for the merchant which may be received or displayed to a user is attached to the purchase information  167 . In some payment protocols, a space in the transaction or purchase data  167  is reserved for a merchant phone number  113 . In other situations, the merchant name may include a merchant phone number  113 . In effect, one goal of the system may be to reduce fraud from spoofed calls to unsuspecting customers. Spoofing may entail displaying a false phone number  113  that may appear to be a legitimate phone number or may be a number with the first six digits, for example, which may look similar to the number of the customer. As the number  113  appears familiar, consumers may be more trusting and may make purchases which turn out to be fraudulent. 
     If a phone number  113  is not attached to the transaction data, at block  510  the system may use the purchase data  167  that is available to attempt to find a phone number that is associated with the purchase data  167 . For example, by searching the merchant name, a phone number  113  may be found. In addition, previous transactions from the same merchant may have phone numbers  113  attached. 
     At block  515 , if the phone number  113  is found, the phone number  113  may be added to the purchase information  167 . As a result, future searches of the merchant name may find the phone number  113  and if a phone number  113  has been indicated as being fraudulent in past sales, it may be noted for future sales. 
     In addition, in some instances, merchants intentionally change their communicated phone number  113  using spoofing type devices. Phone numbers  113  carry data that identifies the caller and the number from which the call originated. As the format of the caller data is known, the caller data may be manipulated by fraudulent merchants to remove the name of the caller, to change the name of the caller, to remove the calling number  113  or to change the calling number  113 . 
     The calling number  113  may be changed in an effort to make the number close to the phone number of the consumer/purchaser. For example, a phone number  113  may have a three digit area code, a three digit local code and a four digit identification code for a total of ten digits. The first six digits may be intentionally made the same as the consumer&#39;s phone number such that the consumer may think it is a local number calling. 
     In addition, the changing of phone numbers  113  or multiple phone numbers  113  for a merchant  106  may be yet another indication that fraud is likely. By saving the variety of phone numbers  113  and noting that the phone numbers  113  change, the system may learn that the merchant  106  is more likely to be fraudulent. In some instances, the merchant  106  may be large and may have a plurality of phone numbers  113  and eventually the phone numbers  113  may be stored repeatedly and the AI engine  110  may learn that a merchant  106  has multiple legitimate phone numbers  113  and the indication of fraud may be reduced. 
     Related, as will be discussed further, if a phone number  113  is noted as being related to fraud, it may be noted with the stored number. Similarly, the lack of fraud may be noted by the lack of fraud indications attached to a phone number  113 . 
     At block  520 , the phone number  113  may be analyzed for fraudulent activities using an artificial intelligence (AI) engine  110 . As mentioned previously, an artificial intelligence engine  110  may be a system that takes in a large amount of data and learns from the data. As a simple example, a set of data may be split into four parts. The first part may be used as verification data and the other three data sets may be analyzed as a training set to determine weights to make predictions for the verification data. Then the sets rotate, where the verification data is added to the training data and one of the sets from the training data is used as the verification data. The model runs again and the weights, such as the weights described in  FIGS. 3 and 4 , are adjusted until all the sets have been used as verification data. Assuming the model is accurate enough or above a threshold, the weights are used to analyze future data and make predictions using the weights as to what the data may mean. 
     Specific to the data in question, the phone number data  113  may be reviewed by the AI engine  110 . The AI engine  110  may analyze the data and learn whether phone numbers  113  for merchant  106  appear fraudulent. 
     In some embodiments, the phone numbers  113  may be studied in parts. As mentioned previously, fraudulent merchants may attempt to spoof their phone number  113  to appear similar to the number being called, often by making the first several digits of a phone number  113  appear similar to the number being called. By analyzing the data, the various pattern of having the calling number  113  look similar to the receiving number or having the calling number  113  change or having a few digits of the calling number change  113  or having a few of the calling number digits  113  match a previously determined fraudulent number may indicate that the phone number  113  is fraudulent. The use of the AI engine  110  may significantly improve the process of determining whether some or all of the merchant&#39;s indicated phone number  113  may be an indication of fraud. 
     In another aspect, a community of consumers may be analyzed to determine groups of consumers that may be similar for purposes of receiving fraudulent calls. In some situations, fraudulent merchants  106  may target a similar group of people such as senior citizens or residents of a neighborhood. By analyzing communities, patterns of phone numbers  113  that appear fraudulent or have a series of complaints may emerge. 
     Communities may be determined in a variety of ways and the AI engine  110  may be used to determine the communities. In some embodiments, the communities may be formed using demographic data if such data is available. For example, if users are senior citizens, they may form a group. Similarly, a community may be users in the same zip code or with the same area. In addition, the community may be users that have a similar start to their phone numbers  113  such as similar starting digits of a phone number  113 . 
     The AI engine  110  may create communities or may be used to refine communities. As an example, the AI engine  110  may analyze the senior citizen group and determine which have similar purchase habits. By learning which have similar purchase habits, it may be determined if one of the community has a fraudulent purchase related to a phone number, other members may also have fraudulent purchases. 
     As mentioned previously, the broader group may be broken into sub-groups. One subgroup may be used as the test group by the AI engine  110  and the other groups may be the training groups to train the AI engine  110 . The training may continue until all groups have been used as a test group. 
     Further, the system and method may review and analyze feedback from the responsible party  117  to assist in determining if a merchant phone number should be classified as fraudulent. For example, a phone caller may convince a customer to buy a good or service over the phone. Shortly thereafter, the customer may realize the phone call was fraudulent and may wish to cancel the transaction. The customer may call the card issuer and request that the transaction be canceled, citing possible fraud. The card issuer may note the phone number  113  associated with the merchant  106 . Further transactions that are connected to the fraudulent merchant phone number  113  may be identified as likely being fraudulent. 
     In addition, the AI engine  110  may review feedback from a community of account holders determined to be similar to determine if a merchant phone number  113  should be classified as fraudulent. If a community of consumers reports possible fraudulent transaction related to a similar merchant phone number  113 , transactions from that merchant  106  may be given a higher probability of being fraudulent. For example, if a community of senior citizens is receiving calls for a fraudulent phone number  113 , future transactions related to that phone number  113  may be identified as having a higher probability of being fraudulent. 
     If the telephone number  113  is not connected to any fraud at the current time, the transaction may not be suspended and the transaction may proceed at bloc  523 . 
     At block  525 , if the phone number  113  is determined to be fraudulent by the AI engine  110 , the phone number  113  may be communicated to the AI engine  110  and the fraud module  152 . In some embodiments, fraud determination may involve a score and in such cases, the score may be increased. In other situations, the phone number issue may be one of many factors considered in determining whether fraud is an issue. 
     At block  530 , if the phone number  113  is determined to be fraudulent by the artificial intelligence engine  110 , the transaction may be suspended from completing. In some embodiments, the suspension may be automatic without input from the responsible party  117  for the account. In addition, in some embodiments, the suspension may be almost automatic such as when the approval is normally communicated to the merchant. 
     In other embodiments, the transaction may be suspended for a period of time such that a responsible party  117  may be contacted for approval or rejection of the transaction. In some embodiments, the suspension may be for a suspension period which may be a time sufficient to contact the responsible party  117 , such as 24 hours. In some embodiments, without approval, the transaction will be denied at the end of the suspension period. 
     At block  535 , a notice of the attempted fraudulent transaction may be communicated to a responsible party  117  of the account. The communication may contain details of the proposed transaction and the reason the transaction has been suspended. The communication may be in an electronic format such that a response may be received before the end of the suspense period. Example communication formats may include email, SMS message, text message or a notice inside an app which may be in communication with the system. The communication channel may be a channel set up as a default channel or may be selected by a responsible party  117 . 
     The responsible party  117  may be one or more people. Oftentimes, fraudulent merchants  106  attempt to fool consumer, especially less sophisticated consumers, into making a transaction and it may be helpful to have another person be the responsible party  117 . As an example, an elderly parent that is not technologically savvy may be persuaded to enter into a proposed transaction with a fraudulent merchant. A notice to a grown child of the elderly parent may be useful in that the grown child may be more technically savvy and may be able to better recognize the transaction as being fraudulent. In addition, numerous children of the elderly parent may be listed as responsible parties  117  and may receive the notification such that if one child is busy or unreachable, others may be able to review the transaction. Of course, the responsible party  117  does not have to be a relative but could be a trusted friend, neighbor, banker, attorney, trustee, etc. 
     In some embodiments, the responsible party  117  may be computer based logic such a software program or a purpose built server. The computer logic may be remote or may be local. The computer logic may operate using an API where the inquiry is submitted to the logic in a known format or protocol and the response may be return in an expected format or protocol. 
     At block  540 , a response from the responsible party  117  of how to proceed with the transaction may be received by the system. In some embodiments, a default response may be communicated and may occur absent a response from the responsible party  117 . For example, the message may state the transaction is going to be cancelled unless a response is received or vice versa. In some embodiments, the message may contain radio type buttons such that a user may simply select the desire radio button (“Approve” or “Decline”) and the desired message may be communicated to the system. In other situations, a responsible party  117  may be able to request additional information on the transaction and the reason it is suspended. 
     At block  545 , the transaction may proceed as indicated by the responsible party  117 . Again, a default value may be set and the default value may be executed absent contrary instructions from the responsible party  117 . In other embodiments, the transaction may remain suspended until a response is received from the responsible party  117 . 
     As mentioned previously, an user interface may be available for consumers and responsible parties to further personalize the system. For example, default values may be set but the values may be adjusted As a simple example, the responsible party  117  may be able to adjust the communication method and the communication address. In more advanced actions, the user may be able to adjust the suspension period, whether suspect transactions are automatically rejected or approved after the suspension period rejection, the people that are listed as responsible parties  117 , the level of acceptable risk for a consumer, etc. 
     As will be recognized by one skilled in the art, in light of the disclosure and teachings herein, other types of computing devices can be used that have different architectures. Processor systems similar or identical to the example systems and methods described herein may be used to implement and execute the example systems and methods described herein. Although the example system  100  is described below as including a plurality of peripherals, interfaces, chips, memories, etc., one or more of those elements may be omitted from other example processor systems used to implement and execute the example systems and methods. Also, other components may be added. 
     As shown in  FIG. 6 , the computing device  901  includes a processor  902  that is coupled to an interconnection bus. The processor  902  includes a register set or register space  904 , which is depicted in  FIG. 6  as being entirely on-chip, but which could alternatively be located entirely or partially off-chip and directly coupled to the processor  902  via dedicated electrical connections and/or via the interconnection bus. The processor  902  may be any suitable processor, processing unit or microprocessor. Although not shown in  FIG. 6 , the computing device  901  may be a multi-processor device and, thus, may include one or more additional processors that are identical or similar to the processor  902  and that are communicatively coupled to the interconnection bus. 
     The processor  902  of  FIG. 6  is coupled to a chipset  906 , which includes a memory controller  908  and a peripheral input/output (I/O) controller  910 . As is well known, a chipset typically provides I/O and memory management functions as well as a plurality of general purpose and/or special purpose registers, timers, etc. that are accessible or used by one or more processors coupled to the chipset  906 . The memory controller  908  performs functions that enable the processor  902  (or processors if there are multiple processors) to access a system memory  912  and a mass storage memory  914 , that may include either or both of an in-memory cache (e.g., a cache within the memory  912 ) or an on-disk cache (e.g., a cache within the mass storage memory  914 ). 
     The system memory  912  may include any desired type of volatile and/or non-volatile memory such as, for example, static random access memory (SRAM), dynamic random access memory (DRAM), flash memory, read-only memory (ROM), etc. The mass storage memory  914  may include any desired type of mass storage device. For example, the computing device  901  may be used to implement a module  916  (e.g., the various modules as herein described). The mass storage memory  914  may include a hard disk drive, an optical drive, a tape storage device, a solid-state memory (e.g., a flash memory, a RAM memory, etc.), a magnetic memory (e.g., a hard drive), or any other memory suitable for mass storage. As used herein, the terms module, block, function, operation, procedure, routine, step, and method refer to tangible computer program logic or tangible computer executable instructions that provide the specified functionality to the computing device  901 , the systems and methods described herein. Thus, a module, block, function, operation, procedure, routine, step, and method can be implemented in hardware, firmware, and/or software. In one embodiment, program modules and routines are stored in mass storage memory  914 , loaded into system memory  912 , and executed by a processor  902  or can be provided from computer program products that are stored in tangible computer-readable storage mediums (e.g. RAM, hard disk, optical/magnetic media, etc.). 
     The peripheral I/O controller  910  performs functions that enable the processor  902  to communicate with a peripheral input/output (I/O) device  924 , a network interface  926 , a local network transceiver  928 , (via the network interface  926 ) via a peripheral I/O bus. The I/O device  924  may be any desired type of I/O device such as, for example, a keyboard, a display (e.g., a liquid crystal display (LCD), a cathode ray tube (CRT) display, etc.), a navigation device (e.g., a mouse, a trackball, a capacitive touch pad, a joystick, etc.), etc. The I/O device  924  may be used with the module  916 , etc., to receive data from the transceiver  928 , send the data to the components of the system  100 , and perform any operations related to the methods as described herein. The local network transceiver  928  may include support for a Wi-Fi network, Bluetooth, Infrared, cellular, or other wireless data transmission protocols. In other embodiments, one element may simultaneously support each of the various wireless protocols employed by the computing device  901 . For example, a software-defined radio may be able to support multiple protocols via downloadable instructions. In operation, the computing device  901  may be able to periodically poll for visible wireless network transmitters (both cellular and local network) on a periodic basis. Such polling may be possible even while normal wireless traffic is being supported on the computing device  901 . The network interface  926  may be, for example, an Ethernet device, an asynchronous transfer mode (ATM) device, an 802.11 wireless interface device, a DSL modem, a cable modem, a cellular modem, etc., that enables the system  100  to communicate with another computer system having at least the elements described in relation to the system  100 . 
     While the memory controller  908  and the I/O controller  910  are depicted in  FIG. 6  as separate functional blocks within the chipset  906 , the functions performed by these blocks may be integrated within a single integrated circuit or may be implemented using two or more separate integrated circuits. The computing environment  900  may also implement the module  916  on a remote computing device  930 . The remote computing device  930  may communicate with the computing device  901  over an Ethernet link  932 . In some embodiments, the module  916  may be retrieved by the computing device  901  from a cloud computing server  934  via the Internet  936 . When using the cloud computing server  934 , the retrieved module  916  may be programmatically linked with the computing device  901 . The module  916  may be a collection of various software platforms including artificial intelligence software and document creation software or may also be a Java® applet executing within a Java® Virtual Machine (JVM) environment resident in the computing device  901  or the remote computing device  930 . The module  916  may also be a “plug-in” adapted to execute in a web-browser located on the computing devices  901  and  930 . In some embodiments, the module  916  may communicate with back end components  938  via the Internet  936 . 
     The system  900  may include but is not limited to any combination of a LAN, a MAN, a WAN, a mobile, a wired or wireless network, a private network, or a virtual private network. Moreover, while only one remote computing device  930  is illustrated in  FIG. 6  to simplify and clarify the description, it is understood that any number of client computers are supported and can be in communication within the system  900 . 
     Additionally, certain embodiments are described herein as including logic or a number of components, modules, or mechanisms. Modules may constitute either software modules (e.g., code or instructions embodied on a machine-readable medium or in a transmission signal, wherein the code is executed by a processor) or hardware modules. A hardware module is tangible unit capable of performing certain operations and may be configured or arranged in a certain manner. In example embodiments, one or more computer systems (e.g., a standalone, client or server computer system) or one or more hardware modules of a computer system (e.g., a processor or a group of processors) may be configured by software (e.g., an application or application portion) as a hardware module that operates to perform certain operations as described herein. 
     In various embodiments, a hardware module may be implemented mechanically or electronically. For example, a hardware module may comprise dedicated circuitry or logic that is permanently configured (e.g., as a special-purpose processor, such as a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC)) to perform certain operations. A hardware module may also comprise programmable logic or circuitry (e.g., as encompassed within a general-purpose processor or other programmable processor) that is temporarily configured by software to perform certain operations. It will be appreciated that the decision to implement a hardware module mechanically, in dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g., configured by software) may be driven by cost and time considerations. 
     Accordingly, the term “hardware module” should be understood to encompass a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a certain manner or to perform certain operations described herein. As used herein, “hardware-implemented module” refers to a hardware module. Considering embodiments in which hardware modules are temporarily configured (e.g., programmed), each of the hardware modules need not be configured or instantiated at any one instance in time. For example, where the hardware modules comprise a general-purpose processor configured using software, the general-purpose processor may be configured as respective different hardware modules at different times. Software may accordingly configure a processor, for example, to constitute a particular hardware module at one instance of time and to constitute a different hardware module at a different instance of time. 
     Hardware modules can provide information to, and receive information from, other hardware modules. Accordingly, the described hardware modules may be regarded as being communicatively coupled. Where multiple of such hardware modules exist contemporaneously, communications may be achieved through signal transmission (e.g., over appropriate circuits and buses) that connect the hardware modules. In embodiments in which multiple hardware modules are configured or instantiated at different times, communications between such hardware modules may be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple hardware modules have access. For example, one hardware module may perform an operation and store the output of that operation in a memory device to which it is communicatively coupled. A further hardware module may then, at a later time, access the memory device to retrieve and process the stored output. Hardware modules may also initiate communications with input or output devices, and can operate on a resource (e.g., a collection of information). 
     The various operations of example methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented modules that operate to perform one or more operations or functions. The modules referred to herein may, in some example embodiments, comprise processor-implemented modules. 
     Similarly, the methods or routines described herein may be at least partially processor-implemented. For example, at least some of the operations of a method may be performed by one or processors or processor-implemented hardware modules. The performance of certain of the operations may be distributed among the one or more processors, not only residing within a single machine, but deployed across a number of machines. In some example embodiments, the processor or processors may be located in a single location (e.g., within a home environment, an office environment or as a server farm), while in other embodiments the processors may be distributed across a number of locations. 
     The one or more processors may also operate to support performance of the relevant operations in a “cloud computing” environment or as a “software as a service” (SaaS). For example, at least some of the operations may be performed by a group of computers (as examples of machines including processors), these operations being accessible via a network (e.g., the Internet) and via one or more appropriate interfaces (e.g., application program interfaces (APIs).) 
     The performance of certain of the operations may be distributed among the one or more processors, not only residing within a single machine, but deployed across a number of machines. In some example embodiments, the one or more processors or processor-implemented modules may be located in a single geographic location (e.g., within a home environment, an office environment, or a server farm). In other example embodiments, the one or more processors or processor-implemented modules may be distributed across a number of geographic locations. 
     Some portions of this specification are presented in terms of algorithms or symbolic representations of operations on data stored as bits or binary digital signals within a machine memory (e.g., a computer memory). These algorithms or symbolic representations are examples of techniques used by those of ordinary skill in the data processing arts to convey the substance of their work to others skilled in the art. As used herein, an “algorithm” is a self-consistent sequence of operations or similar processing leading to a desired result. In this context, algorithms and operations involve physical manipulation of physical quantities. Typically, but not necessarily, such quantities may take the form of electrical, magnetic, or optical signals capable of being stored, accessed, transferred, combined, compared, or otherwise manipulated by a machine. It is convenient at times, principally for reasons of common usage, to refer to such signals using words such as “data,” “content,” “bits,” “values,” “elements,” “symbols,” “characters,” “terms,” “numbers,” “numerals,” or the like. These words, however, are merely convenient labels and are to be associated with appropriate physical quantities. 
     Unless specifically stated otherwise, discussions herein using words such as “processing,” “computing,” “calculating,” “determining,” “presenting,” “displaying,” or the like may refer to actions or processes of a machine (e.g., a computer) that manipulates or transforms data represented as physical (e.g., electronic, magnetic, or optical) quantities within one or more memories (e.g., volatile memory, non-volatile memory, or a combination thereof), registers, or other machine components that receive, store, transmit, or display information. 
     As used herein any reference to “some embodiments” or “an embodiment” or “teaching” means that a particular element, feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. The appearances of the phrase “in some embodiments” or “teachings” in various places in the specification are not necessarily all referring to the same embodiment. 
     Some embodiments may be described using the expression “coupled” and “connected” along with their derivatives. For example, some embodiments may be described using the term “coupled” to indicate that two or more elements are in direct physical or electrical contact. The term “coupled,” however, may also mean that two or more elements are not in direct contact with each other, but yet still co-operate or interact with each other. The embodiments are not limited in this context. 
     Further, the figures depict preferred embodiments for purposes of illustration only. One skilled in the art will readily recognize from the following discussion that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles described herein 
     Upon reading this disclosure, those of skill in the art will appreciate still additional alternative structural and functional designs for the systems and methods described herein through the disclosed principles herein. Thus, while particular embodiments and applications have been illustrated and described, it is to be understood that the disclosed embodiments are not limited to the precise construction and components disclosed herein. Various modifications, changes and variations, which will be apparent to those skilled in the art, may be made in the arrangement, operation and details of the systems and methods disclosed herein without departing from the spirit and scope defined in any appended claims.