Patent Publication Number: US-2020302450-A1

Title: System, Method, and Computer Program Product for False Decline Mitigation

Description:
BACKGROUND 
     1. Field 
     This disclosure relates generally to systems, devices, products, apparatuses, and methods that are used for false decline mitigation in transaction processing, and in some particular embodiments or aspects, to a system, a product, and a method for real-time false decline mitigation using alert processing. 
     2. Technical Considerations 
     Transaction fraud is on the rise in the payment industry. Issuers suffer losses in millions of dollars each year due to transaction fraud. Effective rules and quick enablement of fraud prevention rules in a transaction processing network can save millions of dollars in fraud losses for issuers and merchants. For example, fraud prevention tools detect fraudulent transactions using fraud prevention rules and decline a portion of transactions as fraudulent. However, not every declined transaction is actually a fraudulent transaction. For example, a significant number of the portion of transactions declined as fraudulent may actually be otherwise good transactions, which are known as false positives or false declines. Accordingly, there is a need for improved false decline mitigation in the payment industry. 
     SUMMARY 
     Accordingly, provided are improved systems, devices, products, apparatuses, and/or methods for false decline mitigation. 
     According to some non-limiting embodiments or aspects, provided is a computer-implemented method for false decline mitigation, including: obtaining, with at least one processor, an objective function associated with an issuer system; training, with at least one processor, a neural network, based on prior transaction data associated with one or more prior transactions, to optimize the objective function; providing, with at least one processor, the trained neural network; receiving, with at least one processor, transaction data generated, based on one or more case creation (CC) rules, during processing of a transaction associated with an account identifier; processing, with at least one processor, using the trained neural network, the transaction data to generate an exclude account list including the account identifier; receiving, with at least one processor, subsequent transaction data associated with a subsequent transaction associated with the account identifier; and authorizing, with at least one processor, based on the exclude account list and the account identifier, the subsequent transaction for the account identifier without applying one or more real-time decisioning (RTD) rules to the subsequent transaction. 
     In some non-limiting embodiments or aspects, training the neural network to optimize the objective function further includes: determining, with the neural network, one or more probabilities of the one or more prior transactions being falsely declined by the one or more RTD rules, the objective function depending on the one or more probabilities; and modifying, using the objective function, one or more parameters of the neural network. 
     In some non-limiting embodiments or aspects, the objective function further depends on one or more amounts of the one or more transactions. 
     In some non-limiting embodiments or aspects, the method further includes: providing, with at least one processor, an issuer interface to the issuer system; receiving, with at least one processor, via the issuer interface from the issuer system, issuer input; and determining, with at least one processor, the objective function based on the issuer input. 
     In some non-limiting embodiments or aspects, the method further includes: providing, with at least one processor, via the issuer interface to the issuer system, design feedback data associated with at least one of a number of false declined transactions and a number of true decline transactions for the issuer system. 
     In some non-limiting embodiments or aspects, receiving the transaction data generated based on the one or more CC rules further includes: receiving fraud data associated with the transaction; and determining that the transaction associated with the account identifier is associated with a false decline based on the fraud data, the transaction data being generated based on the one or more CC rules is generated and received before the transaction associated with the account identifier is determined to be associated with a false decline. 
     In some non-limiting embodiments or aspects, the subsequent transaction associated with the account identifier is authorized, based on the exclude account list and the account identifier, without applying the one or more RTD rules to the subsequent transaction, before receiving the fraud data associated with the transaction. 
     According to some non-limiting embodiments or aspects, provided is a computing system for false decline mitigation, including: one or more processors programmed and/or configured to: obtain an objective function associated with an issuer system; train a neural network, based on prior transaction data associated with one or more prior transactions, to optimize the objective function; provide the trained neural network; receive transaction data generated, based on one or more case creation (CC) rules, during processing of a transaction associated with an account identifier; process, using the trained neural network, the transaction data to generate an exclude account list including the account identifier; receive subsequent transaction data associated with a subsequent transaction for the account identifier; and authorize, based on the exclude account list and the account identifier, the subsequent transaction for the account identifier without applying one or more real-time decisioning (RTD) rules to the subsequent transaction. 
     In some non-limiting embodiments or aspects, the one or more processors train the neural network to optimize the objective function by: determining, with the neural network, one or more probabilities of the one or more prior transactions being falsely declined by the one or more RTD rules, the objective function depending on the one or more probabilities; and modifying, using the objective function, one or more parameters of the neural network. 
     In some non-limiting embodiments or aspects, the objective function further depends on one or more amounts of the one or more transactions. 
     In some non-limiting embodiments or aspects, the one or more processors are further programmed and/or configured to: provide an issuer interface to the issuer system; receive, via the issuer interface from the issuer system, issuer input; and determine the objective function based on the issuer input. 
     In some non-limiting embodiments or aspects, the one or more processors are further programmed and/or configured to: provide, via the issuer interface to the issuer system, design feedback data associated with at least one of a number of false declined transactions and a number of true decline transactions for the issuer system. 
     In some non-limiting embodiments or aspects, the one or more processors receive the transaction data generated based on the one or more CC rules by: receiving fraud data associated with the transaction; and determining that the transaction associated with the account identifier is associated with a false decline based on the fraud data, the transaction data being generated based on the one or more CC rules is generated and received before the transaction associated with the account identifier is determined to be associated with a false decline. 
     In some non-limiting embodiments or aspects, the subsequent transaction associated with the account identifier is authorized, based on the exclude account list and the account identifier, without applying the one or more RTD rules to the subsequent transaction, before receiving the fraud data associated with the transaction. 
     According to some non-limiting embodiments or aspects, provided is a computer program product including at least one non-transitory computer-readable medium including program instructions for false decline mitigation that, when executed by at least one processor, cause the at least one processor to: obtain an objective function associated with an issuer system; train a neural network, based on prior transaction data associated with one or more prior transactions, to optimize the objective function; provide the trained neural network; receive transaction data generated, based on one or more case creation (CC) rules, during processing of a transaction associated with an account identifier; process, using the trained neural network, the transaction data to generate an exclude account list including the account identifier; receive subsequent transaction data associated with a subsequent transaction for the account identifier; and authorize, based on the exclude account list and the account identifier, the subsequent transaction for the account identifier without applying one or more real-time decisioning (RTD) rules to the subsequent transaction. 
     In some non-limiting embodiments or aspects, the instructions further cause the at least one processor to train the neural network to optimize the objective function by: determining, with the neural network, one or more probabilities of the one or more prior transactions being falsely declined by the one or more RTD rules, the objective function depending on the one or more probabilities; and modifying, using the objective function, one or more parameters of the neural network. 
     In some non-limiting embodiments or aspects, the objective function further depends on one or more amounts of the one or more transactions. 
     In some non-limiting embodiments or aspects, the instructions further cause the at least one processor to: provide an issuer interface to the issuer system; receive, via the issuer interface from the issuer system, issuer input; and determine the objective function based on the issuer input. 
     In some non-limiting embodiments or aspects, the instructions further cause the at least one processor to: provide, via the issuer interface to the issuer system, design feedback data associated with at least one of a number of false declined transactions and a number of true decline transactions for the issuer system. 
     In some non-limiting embodiments or aspects, the instructions further cause the at least one processor to: receive the transaction data generated based on the one or more CC rules by: receiving fraud data associated with the transaction; and determining that the transaction associated with the account identifier is associated with a false decline based on the fraud data, the transaction data being generated based on the one or more CC rules is generated and received before the transaction associated with the account identifier is determined to be associated with a false decline, and the subsequent transaction associated with the account identifier being authorized, based on the exclude account list and the account identifier, without applying the one or more RTD rules to the subsequent transaction, before receiving the fraud data associated with the transaction. 
     According to some non-limiting embodiments or aspects, provided is a computer-implemented method for false decline mitigation, including: declining, with at least one processor, a transaction by applying one or more RTD rules to transaction data associated with the transaction, the transaction data including an account identifier associated with the transaction; receiving, with at least one processor, alert data associated with the transaction from an issuer system; processing, with at least one processor, using a neural network model associated with the issuer system, the alert data to generate an exclude account list including the account identifier, the exclude account list being associated with the issuer system; receiving, with at least one processor, subsequent transaction data associated with a subsequent transaction associated with the account identifier; and authorizing, with at least one processor, based on the exclude account list and the account identifier, the subsequent transaction for the account identifier. 
     According to some non-limiting embodiments or aspects, provided is a computing system for false decline mitigation including one or more processors programmed and/or configured to: decline a transaction by applying one or more RTD rules to transaction data associated with the transaction, the transaction data including an account identifier associated with the transaction; receive alert data associated with the transaction from an issuer system; process, using a neural network model associated with the issuer system, the alert data to generate an exclude account list including the account identifier, the exclude account list being associated with the issuer system; receive subsequent transaction data associated with a subsequent transaction associated with the account identifier; and authorize, based on the exclude account list and the account identifier, the subsequent transaction for the account identifier. 
     According to some non-limiting embodiments or aspects, provided is a computer program product including at least one non-transitory computer-readable medium including program instructions for false decline mitigation that, when executed by at least one processor, cause the at least one processor to: decline a transaction by applying one or more RTD rules to transaction data associated with the transaction, the transaction data including an account identifier associated with the transaction; receive alert data associated with the transaction from an issuer system; process, using a neural network model associated with the issuer system, the alert data to generate an exclude account list including the account identifier, the exclude account list being associated with the issuer system; receive subsequent transaction data associated with a subsequent transaction associated with the account identifier; and authorize, based on the exclude account list and the account identifier, the subsequent transaction for the account identifier. 
     Further embodiments or aspects are set forth in the following numbered clauses: 
     Clause 1: A computer-implemented method for false decline mitigation, comprising: obtaining, with at least one processor, an objective function associated with an issuer system; training, with at least one processor, a neural network, based on prior transaction data associated with one or more prior transactions, to optimize the objective function; providing, with at least one processor, the trained neural network; receiving, with at least one processor, transaction data generated, based on one or more case creation (CC) rules, during processing of a transaction associated with an account identifier; processing, with at least one processor, using the trained neural network, the transaction data to generate an exclude account list including the account identifier; receiving, with at least one processor, subsequent transaction data associated with a subsequent transaction associated with the account identifier; and authorizing, with at least one processor, based on the exclude account list and the account identifier, the subsequent transaction for the account identifier without applying one or more real-time decisioning (RTD) rules to the subsequent transaction. 
     Clause 2: The computer-implemented method of clause 1, wherein training the neural network to optimize the objective function further comprises: determining, with the neural network, one or more probabilities of the one or more prior transactions being falsely declined by the one or more RTD rules, wherein the objective function depends on the one or more probabilities; and modifying, using the objective function, one or more parameters of the neural network. 
     Clause 3: The computer-implemented method of clauses 1 or 2, wherein the objective function further depends on one or more amounts of the one or more transactions. 
     Clause 4: The computer-implemented method of any of clauses 1-3, further comprising: providing, with at least one processor, an issuer interface to the issuer system; receiving, with at least one processor, via the issuer interface from the issuer system, issuer input; and determining, with at least one processor, the objective function based on the issuer input. 
     Clause 5: The computer-implemented method of any of clauses 1-4, further comprising: providing, with at least one processor, via the issuer interface to the issuer system, design feedback data associated with at least one of a number of false declined transactions and a number of true decline transactions for the issuer system. 
     Clause 6: The computer-implemented method of any of clauses 1-5, wherein receiving the transaction data generated based on the one or more CC rules further comprises: receiving fraud data associated with the transaction; and determining that the transaction associated with the account identifier is associated with a false decline based on the fraud data, wherein the transaction data generated based on the one or more CC rules is generated and received before the transaction associated with the account identifier is determined to be associated with a false decline. 
     Clause 7: The computer-implemented method of any of clauses 1-6, the subsequent transaction associated with the account identifier is authorized, based on the exclude account list and the account identifier, without applying the one or more RTD rules to the subsequent transaction, before receiving the fraud data associated with the transaction. 
     Clause 8: A computing system for false decline mitigation, comprising: one or more processors programmed and/or configured to: obtain an objective function associated with an issuer system; train a neural network, based on prior transaction data associated with one or more prior transactions, to optimize the objective function; provide the trained neural network; receive transaction data generated, based on one or more case creation (CC) rules, during processing of a transaction associated with an account identifier; process, using the trained neural network, the transaction data to generate an exclude account list including the account identifier; receive subsequent transaction data associated with a subsequent transaction for the account identifier; and authorize, based on the exclude account list and the account identifier, the subsequent transaction for the account identifier without applying one or more real-time decisioning (RTD) rules to the subsequent transaction. 
     Clause 9: The computing system of clause 8, wherein the one or more processors train the neural network to optimize the objective function by: determining, with the neural network, one or more probabilities of the one or more prior transactions being falsely declined by the one or more RTD rules, wherein the objective function depends on the one or more probabilities; and modifying, using the objective function, one or more parameters of the neural network. 
     Clause 10: The computing system of clauses 8 or 9, wherein the objective function further depends on one or more amounts of the one or more transactions. 
     Clause 11: The computing system of any of clauses 8-10, wherein the one or more processors are further programmed and/or configured to: provide an issuer interface to the issuer system; receive, via the issuer interface from the issuer system, issuer input; and determine the objective function based on the issuer input. 
     Clause 12: The computing system of any of clauses 8-11, wherein the one or more processors are further programmed and/or configured to: provide, via the issuer interface to the issuer system, design feedback data associated with at least one of a number of false declined transactions and a number of true decline transactions for the issuer system. 
     Clause 13: The computing system of any of clauses 8-12, wherein the one or more processors receive the transaction data generated based on the one or more CC rules by: receiving fraud data associated with the transaction; and determining that the transaction associated with the account identifier is associated with a false decline based on the fraud data, wherein the transaction data generated based on the one or more CC rules is generated and received before the transaction associated with the account identifier is determined to be associated with a false decline. 
     Clause 14: The computing system of any of clauses 8-13, wherein the subsequent transaction associated with the account identifier is authorized, based on the exclude account list and the account identifier, without applying the one or more RTD rules to the subsequent transaction, before receiving the fraud data associated with the transaction. 
     Clause 15: A computer program product comprising at least one non-transitory computer-readable medium including program instructions for false decline mitigation that, when executed by at least one processor, cause the at least one processor to: obtain an objective function associated with an issuer system; train a neural network, based on prior transaction data associated with one or more prior transactions, to optimize the objective function; provide the trained neural network; receive transaction data generated, based on one or more case creation (CC) rules, during processing of a transaction associated with an account identifier; process, using the trained neural network, the transaction data to generate an exclude account list including the account identifier; receive subsequent transaction data associated with a subsequent transaction for the account identifier; and authorize, based on the exclude account list and the account identifier, the subsequent transaction for the account identifier without applying one or more real-time decisioning (RTD) rules to the subsequent transaction. 
     Clause 16: The computer program product of clause 15, wherein the instructions further cause the at least one processor to train the neural network to optimize the objective function by: determining, with the neural network, one or more probabilities of the one or more prior transactions being falsely declined by the one or more RTD rules, wherein the objective function depends on the one or more probabilities; and modifying, using the objective function, one or more parameters of the neural network. 
     Clause 17: The computer program product of clauses 15 or 16, wherein the objective function further depends on one or more amounts of the one or more transactions. 
     Clause 18: The computer program product of any of clauses 15-17, wherein the instructions further cause the at least one processor to: provide an issuer interface to the issuer system; receive, via the issuer interface from the issuer system, issuer input; and determine the objective function based on the issuer input. 
     Clause 19: The computer program product of any of clauses 15-18, wherein the instructions further cause the at least one processor to: provide, via the issuer interface to the issuer system, design feedback data associated with at least one of a number of false declined transactions and a number of true decline transactions for the issuer system. 
     Clause 20: The computer program product of any of clauses 15-19, wherein the instructions further cause the at least one processor to: receive the transaction data generated based on the one or more CC rules by: receiving fraud data associated with the transaction; and determining that the transaction associated with the account identifier is associated with a false decline based on the fraud data, wherein the transaction data generated based on the one or more CC rules is generated and received before the transaction associated with the account identifier is determined to be associated with a false decline, and wherein the subsequent transaction associated with the account identifier is authorized, based on the exclude account list and the account identifier, without applying the one or more RTD rules to the subsequent transaction, before receiving the fraud data associated with the transaction. 
     Clause 21: A computer-implemented method for false decline mitigation, comprising: declining, with at least one processor, a transaction by applying one or more RTD rules to transaction data associated with the transaction, wherein the transaction data includes an account identifier associated with the transaction; receiving, with at least one processor, alert data associated with the transaction from an issuer system; processing, with at least one processor, using a neural network model associated with the issuer system, the alert data to generate an exclude account list including the account identifier, wherein the exclude account list is associated with the issuer system; receiving, with at least one processor, subsequent transaction data associated with a subsequent transaction associated with the account identifier; and authorizing, with at least one processor, based on the exclude account list and the account identifier, the subsequent transaction for the account identifier. 
     Clause 22: A computing system for false decline mitigation comprising one or more processors programmed and/or configured to: decline a transaction by applying one or more RTD rules to transaction data associated with the transaction, wherein the transaction data includes an account identifier associated with the transaction; receive alert data associated with the transaction from an issuer system; process, using a neural network model associated with the issuer system, the alert data to generate an exclude account list including the account identifier, wherein the exclude account list is associated with the issuer system; receive subsequent transaction data associated with a subsequent transaction associated with the account identifier; and authorize, based on the exclude account list and the account identifier, the subsequent transaction for the account identifier. 
     Clause 23: A computer program product including at least one non-transitory computer-readable medium comprising program instructions for false decline mitigation that, when executed by at least one processor, cause the at least one processor to: decline a transaction by applying one or more RTD rules to transaction data associated with the transaction, wherein the transaction data includes an account identifier associated with the transaction; receive alert data associated with the transaction from an issuer system; process, using a neural network model associated with the issuer system, the alert data to generate an exclude account list including the account identifier, wherein the exclude account list is associated with the issuer system; receive subsequent transaction data associated with a subsequent transaction associated with the account identifier; and authorize, based on the exclude account list and the account identifier, the subsequent transaction for the account identifier. 
     These and other features and characteristics of the present disclosure, as well as the methods of operation and functions of the related elements of structures and the combination of parts and economies of manufacture, will become more apparent upon consideration of the following description and the appended claims with reference to the accompanying drawings, all of which form a part of this specification, wherein like reference numerals designate corresponding parts in the various figures. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of limits. As used in the specification and the claims, the singular form of “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Additional advantages and details are explained in greater detail below with reference to the exemplary embodiments or aspects that are illustrated in the accompanying schematic figures, in which: 
         FIG. 1  is a diagram of non-limiting embodiments or aspects of an environment in which systems, devices, products, apparatuses, and/or methods, described herein, may be implemented; 
         FIG. 2  is a diagram of non-limiting embodiments or aspects of components of one or more devices and/or one or more systems of  FIG. 1 ; 
         FIG. 3  is a flowchart of non-limiting embodiments or aspects of a process for false decline mitigation; 
         FIG. 4  is a flowchart of non-limiting embodiments or aspects of a process for false decline mitigation; 
         FIG. 5  is a flowchart of non-limiting embodiments or aspects of a process for false decline mitigation; 
         FIG. 6  is a diagram of non-limiting embodiments or aspects of an implementation of a real-time decisioning (RTD) rule; and 
         FIG. 7  is a diagram of non-limiting embodiments or aspects of an implementation of an RTD rule creation and validation architecture. 
     
    
    
     DESCRIPTION 
     It is to be understood that the present disclosure may assume various alternative variations and step sequences, except where expressly specified to the contrary. It is also to be understood that the specific devices and processes illustrated in the attached drawings, and described in the following specification, are simply exemplary and non-limiting embodiments or aspects. Hence, specific dimensions and other physical characteristics related to the embodiments or aspects disclosed herein are not to be considered as limiting. 
     No aspect, component, element, structure, act, step, function, instruction, and/or the like used herein should be construed as critical or essential unless explicitly described as such. Also, as used herein, the articles “a” and “an” are intended to include one or more items, and may be used interchangeably with “one or more” and “at least one.” Furthermore, as used herein, the term “set” is intended to include one or more items (e.g., related items, unrelated items, a combination of related and unrelated items, etc.) and may be used interchangeably with “one or more” or “at least one.” Where only one item is intended, the term “one” or similar language is used. Also, as used herein, the terms “has,” “have,” “having,” or the like are intended to be open-ended terms. Further, the phrase “based on” is intended to mean “based at least partially on” unless explicitly stated otherwise. 
     As used herein, the terms “communication” and “communicate” refer to the receipt or transfer of one or more signals, messages, commands, or other type of data. For one unit (e.g., any device, system, or component thereof) to be in communication with another unit means that the one unit is able to directly or indirectly receive data from and/or transmit data to the other unit. This may refer to a direct or indirect connection that is wired and/or wireless in nature. Additionally, two units may be in communication with each other even though the data transmitted may be modified, processed, relayed, and/or routed between the first and second unit. For example, a first unit may be in communication with a second unit even though the first unit passively receives data and does not actively transmit data to the second unit. As another example, a first unit may be in communication with a second unit if an intermediary unit processes data from one unit and transmits processed data to the second unit. It will be appreciated that numerous other arrangements are possible. 
     It will be apparent that systems and/or methods, described herein, can be implemented in different forms of hardware, software, or a combination of hardware and software. The actual specialized control hardware or software code used to implement these systems and/or methods is not limiting of the implementations. Thus, the operation and behavior of the systems and/or methods are described herein without reference to specific software code, it being understood that software and hardware can be designed to implement the systems and/or methods based on the description herein. 
     Some non-limiting embodiments or aspects are described herein in connection with thresholds. As used herein, satisfying a threshold may refer to a value being greater than the threshold, more than the threshold, higher than the threshold, greater than or equal to the threshold, less than the threshold, fewer than the threshold, lower than the threshold, less than or equal to the threshold, equal to the threshold, etc. 
     As used herein, the term “transaction service provider” may refer to an entity that receives transaction authorization requests from merchants or other entities and provides guarantees of payment, in some cases through an agreement between the transaction service provider and an issuer institution. The terms “transaction service provider” and “transaction service provider system” may also refer to one or more computer systems operated by or on behalf of a transaction service provider, such as a transaction processing system executing one or more software applications. A transaction processing system may include one or more server computers with one or more processors and, in some non-limiting embodiments or aspects, may be operated by or on behalf of a transaction service provider. 
     As used herein, the term “account identifier” may include one or more Primary Account Numbers (PAN), tokens, or other identifiers (e.g., a globally unique identifier (GUID), a universally unique identifier (UUID), etc.) associated with a customer account of a user (e.g., a customer, a consumer, and/or the like). The term “token” may refer to an identifier that is used as a substitute or replacement identifier for an original account identifier, such as a PAN. Account identifiers may be alphanumeric or any combination of characters and/or symbols. Tokens may be associated with a PAN or other original account identifier in one or more databases such that they can be used to conduct a transaction without directly using the original account identifier. In some examples, an original account identifier, such as a PAN, may be associated with a plurality of tokens for different individuals or purposes. 
     As used herein, the terms “issuer institution,” “portable financial device issuer,” “issuer,” or “issuer bank” may refer to one or more entities that provide one or more accounts to a user (e.g., a customer, a consumer, an entity, an organization, and/or the like) for conducting transactions (e.g., payment transactions), such as initiating credit card payment transactions and/or debit card payment transactions. For example, an issuer institution may provide an account identifier, such as a personal account number (PAN), to a user that uniquely identifies one or more accounts associated with that user. The account identifier may be embodied on a portable financial device, such as a physical financial instrument (e.g., a payment card), and/or may be electronic and used for electronic payments. In some non-limiting embodiments or aspects, an issuer institution may be associated with a bank identification number (BIN) that uniquely identifies the issuer institution. As used herein “issuer institution system” may refer to one or more computer systems operated by or on behalf of an issuer institution, such as a server computer executing one or more software applications. For example, an issuer institution system may include one or more authorization servers for authorizing a payment transaction. 
     As used herein, the term “merchant” may refer to an individual or entity that provides products and/or services, or access to products and/or services, to customers based on a transaction, such as a payment transaction. The term “merchant” or “merchant system” may also refer to one or more computer systems operated by or on behalf of a merchant, such as a server computer executing one or more software applications. A “point-of-sale (POS) system,” as used herein, may refer to one or more computers and/or peripheral devices used by a merchant to engage in payment transactions with customers, including one or more card readers, near-field communication (NFC) receivers, RFID receivers, and/or other contactless transceivers or receivers, contact-based receivers, payment terminals, computers, servers, input devices, and/or other like devices that can be used to initiate a payment transaction. 
     As used herein, the term “mobile device” may refer to one or more portable electronic devices configured to communicate with one or more networks. As an example, a mobile device may include a cellular phone (e.g., a smartphone or standard cellular phone), a portable computer (e.g., a tablet computer, a laptop computer, etc.), a wearable device (e.g., a watch, pair of glasses, lens, clothing, and/or the like), a personal digital assistant (PDA), and/or other like devices. The terms “client device” and “user device,” as used herein, refer to any electronic device that is configured to communicate with one or more servers or remote devices and/or systems. A client device or user device may include a mobile device, a network-enabled appliance (e.g., a network-enabled television, refrigerator, thermostat, and/or the like), a computer, a POS system, and/or any other device or system capable of communicating with a network. 
     As used herein, the term “computing device” may refer to one or more electronic devices that are configured to directly or indirectly communicate with or over one or more networks. The computing device may be a mobile device, a desktop computer, or the like. Furthermore, the term “computer” may refer to any computing device that includes the necessary components to receive, process, and output data, and normally includes a display, a processor, a memory, an input device, and a network interface. An “application” or “application program interface” (API) refers to computer code or other data sorted on a computer-readable medium that may be executed by a processor to facilitate the interaction between software components, such as a client-side front-end and/or server-side back-end for receiving data from the client. An “interface” refers to a generated display, such as one or more graphical user interfaces (GUIs) with which a user may interact, either directly or indirectly (e.g., through a keyboard, mouse, touchscreen, etc.). 
     As used herein, the terms “electronic wallet” and “electronic wallet application” refer to one or more electronic devices and/or software applications configured to initiate and/or conduct payment transactions. For example, an electronic wallet may include a mobile device executing an electronic wallet application, and may further include server-side software and/or databases for maintaining and providing transaction data to the mobile device. An “electronic wallet provider” may include an entity that provides and/or maintains an electronic wallet for a customer, such as Google Wallet™, Android Pay®, Apple Pay®, Samsung Pay®, and/or other like electronic payment systems. In some non-limiting examples, an issuer bank may be an electronic wallet provider. 
     As used herein, the term “portable financial device” may refer to a payment card (e.g., a credit or debit card), a gift card, a smartcard, smart media, a payroll card, a healthcare card, a wrist band, a machine-readable medium containing account information, a keychain device or fob, an RFID transponder, a retailer discount or loyalty card, a mobile device executing an electronic wallet application, a personal digital assistant (PDA), a security card, an access card, a wireless terminal, and/or a transponder, as examples. The portable financial device may include a volatile or a non-volatile memory to store information, such as an account identifier and/or a name of the account holder. 
     As used herein, the term “server” may refer to or include one or more processors or computers, storage devices, or similar computer arrangements that are operated by or facilitate communication and processing for multiple parties in a network environment, such as the Internet, although it will be appreciated that communication may be facilitated over one or more public or private network environments and that various other arrangements are possible. Further, multiple computers, e.g., servers, or other computerized devices, such as POS devices, directly or indirectly communicating in the network environment may constitute a “system,” such as a merchant&#39;s POS system. 
     As used herein, the term “acquirer” may refer to an entity licensed by the transaction service provider and/or approved by the transaction service provider to originate transactions using a portable financial device of the transaction service provider. Acquirer may also refer to one or more computer systems operated by or on behalf of an acquirer, such as a server computer executing one or more software applications (e.g., “acquirer server”). An “acquirer” may be a merchant bank, or in some cases, the merchant system may be the acquirer. The transactions may include original credit transactions (OCTs) and account funding transactions (AFTs). The acquirer may be authorized by the transaction service provider to sign merchants of service providers to originate transactions using a portable financial device of the transaction service provider. The acquirer may contract with payment facilitators to enable the facilitators to sponsor merchants. The acquirer may monitor compliance of the payment facilitators in accordance with regulations of the transaction service provider. The acquirer may conduct due diligence of payment facilitators and ensure that proper due diligence occurs before signing a sponsored merchant. Acquirers may be liable for all transaction service provider programs that they operate or sponsor. Acquirers may be responsible for the acts of its payment facilitators and the merchants it or its payment facilitators sponsor. 
     As used herein, the term “payment gateway” may refer to an entity and/or a payment processing system operated by or on behalf of such an entity (e.g., a merchant service provider, a payment service provider, a payment facilitator, a payment facilitator that contracts with an acquirer, a payment aggregator, and/or the like), which provides payment services (e.g., transaction service provider payment services, payment processing services, and/or the like) to one or more merchants. The payment services may be associated with the use of portable financial devices managed by a transaction service provider. As used herein, the term “payment gateway system” may refer to one or more computer systems, computer devices, servers, groups of servers, and/or the like, operated by or on behalf of a payment gateway. 
     Provided are improved systems, devices, products, apparatuses, and/or methods for false decline mitigation. 
     Issuers may create custom rules that identify fraudulent or suspect transactions. For example, a real-time decisioning (RTD) rule, may be applied to a transaction in progress (e.g., during processing of the transaction in a transaction processing network, etc.), and the transaction may be authorized or denied according to a result of the application of the RTD rule to the transaction. As an example, application of an RTD rule may cause a transaction to be declined at a point-of-sale (POS). In such an example, the RTD rule may be too general (e.g., the RTD rule may be applied at a relatively high level of granularity with respect to parameters of transaction data associated with the transaction, etc.), which may cause otherwise good transactions to be declined. This is known as a false positive or a false decline. Additionally, or alternatively, a true positive or a true decline may occur when an actually fraudulent transaction is declined. Further, because RTD rules may have a relatively higher granularity and/or higher shelf life, some RTD rules may be used for years based on predefined expectations. Moreover, “automatic” generation of RTD rules may always use a human or manual element to inspect and approve the RTD rules for production, enablement, and/or publishing, because RTD rules act on a relatively wider range of transactions and, therefore, a relatively simple problem in an RTD rule can impact millions of users and/or billions of transactions. 
     A case creation (CC) rule may initiate an investigation of a transaction that has occurred as to whether the transaction was fraudulent. For example, a CC rule may create cases that identify transactions as suspect if the transactions satisfy conditions in the CC rule, and a corresponding issuer system (e.g., a human agent, etc.) may review the suspect transactions to determine a correct follow-up action for the transaction. As an example, application of a CC rule (e.g., by an issuer system, by a transaction service provider system, etc.) may result in a message that indicates that a transaction is suspicious and/or needs human intervention, and a human agent of the issuer system may review the suspect transactions to determine whether fraud is happening and/or whether otherwise good transactions are resulting in false declines too often. 
     Exclude account lists include lists of accounts for which RTD rules may not be applied to transactions for the listed accounts (e.g., transactions for accounts on an exclude account list may automatically be authorized without applying RTD rules to the transactions, etc.). For example, an exclude account list enables an issuer to “whitelist” specific accounts such that the specific accounts are not impacted by RTD rules that may cause otherwise good transactions to be false declined. As an example, modification of an exclude account list may include a separate and/or different logic than a logic for modifying RTD rules. However, existing exclude account lists may be manually updated and, therefore, cannot be updated in real-time as new transactions are processed. For example, a transaction for a particular account not currently on an exclude account list may be false declined, and the exclude account list may not be updated to include the particular account before another, subsequent transaction for the particular account is initiated and/or false declined. As an example, a human agent of an issuer may review cases created by CC rules to determine that false declines are happening and manually create the exclude account list. However, existing exclude account lists may be updated only after cases created by CC rules are resolved by an issuer. For example, information and/or data associated with an initial alert of a case creation for a transaction generated immediately after the case is created and/or before the case is resolved may not be used and/or available (e.g., to a transaction service provider system, to an issuer system, etc.) to create or update an exclude account list. Additionally, or alternatively, a customer may call a human agent of the issuer to explain one or more false declines, and the human agent may manually create an exclude account list including an account identifier associated with the customer in response to the explanation from the customer. 
     Existing computer systems for optimizing RTD rules cannot update the RTD rules in real-time as new and/or current transactions are processed. For example, these existing computer systems may train a model based on an objective function, model parameters, and a methodology selected by a user, validate the model, and then convert the model to the RTD rules that can be implemented in real-time for processing a transaction. However, the RTD rules themselves cannot be updated continuously and/or in real-time as new and/or current transactions are processed. For example, these existing computer systems may not use or receive alert data generated by an issuer system during processing of a transaction, other fraud data generated based on and/or by CC rules, transaction outcomes determined by an issuer associated with the CC rules, and/or the like to update the RTD rules, and/or an interpretability of the RTD rules may be required to satisfy certain thresholds or standards that inhibit or prevent automatic updating of the RTD rules in real-time. In such an example, by not using and/or communicating alert data and/or past agent actions (e.g., between a transaction service provider system and an issuer system, etc.) to improve the RTD rules so that the RTD rules may not impact a future or subsequent transaction after a previous false decline, existing computer systems may not inhibit or prevent an impending false decline of an otherwise good or non-fraudulent transaction that impacts a good or non-fraudulent customer. Accordingly, these existing computer systems cannot update card or account-level rules continuously and/or in real-time as new transactions are processed. 
     Further, these existing computer systems may not, in the first place, avoid processing a transaction with a rules or decisioning engine that applies RTD rules. For example, executing or applying RTD rules to each transaction is computationally expensive and uses a relatively higher granularity and/or amount of data, which inhibits or prevents automatically generating and using RTD rules for processing transactions in real-time (e.g., a typical RTD rule takes at least  10  minutes to publish, enable, and/or implement for processing transactions). As an example, it is impractical to use an RTD rule system to manage RTD rules at an account identifier level of granularity, because RTD rules are business documents that are routinely read by human agents, and lengthy rules with respect to specific accounts may render the rules too difficult for consumption and/or comprehension by human agents required to publish, enable, and/or implement the RTD rules. 
     Non-limiting embodiments or aspects of the present disclosure are directed to systems, methods, and computer program products for false decline mitigation that obtain an objective function associated with an issuer system; train a neural network, based on prior transaction data associated with one or more prior transactions, to optimize the objective function; provide the trained neural network; receive transaction data generated, based on one or more case creation (CC) rules, during processing of a transaction associated with an account identifier; process, using the trained neural network, the transaction data to generate an exclude account list including the account identifier; receive subsequent transaction data associated with a subsequent transaction for the account identifier; and authorize, based on the exclude account list and the account identifier, the subsequent transaction for the account identifier without applying one or more RTD rules to the transaction. 
     In this way, non-limiting embodiments or aspects of the present disclosure provide a neural network model that updates an exclude account list based on data associated with new and/or current transactions as the new and/or current transactions are processed (and/or as data associated therewith is received) to optimize an objective function, which may be individually selected by an issuer system. For example, the neural network model can update the exclude account list based on alert data generated by an issuer system during processing of a first transaction for a particular account before a second, subsequent transaction for that particular account can be initiated (e.g., update the exclude account list immediately after the first transaction is processed and/or an alert based on a CC rule is created therefore without having to wait for a case-creation determination or outcome that indicates whether the first transaction is actually a false decline). Accordingly, non-limiting embodiments or aspects of the present disclosure may enable: (i) issuers to generate and validate an exclude account list programmatically; (ii) continuous updating of an exclude account list in real-time as new and/or current transactions are processed (and/or as data associated therewith is received) such that an account identifier associated with a new and/or current transaction can be proactively added to the exclude account list to avoid a false decline; (iii) avoiding application of RTD rules (e.g., by entirely short-circuiting or avoiding application of a rules engine, thereby saving computing system resources and making auditing and/or tracking of exclusions of certain accounts and/or durations of the exclusions of certain accounts easier; (iv) reducing and/or minimizing issuer losses and/or merchant losses due to false declines; (v) significantly reducing a granularity and/or an amount of data to be processed for authorizing a transaction (e.g., which may enable updating an exclude account list to include an account identifier in less than 20 seconds, updating an exclude account list to include an account identifier before a subsequent transaction associated with the account identifier is initiated and/or false declined, etc.); and/or the like. 
     Referring now to  FIG. 1 ,  FIG. 1  is a diagram of an example environment  100  in which devices, systems, methods, and/or products described herein, may be implemented. As shown in  FIG. 1 , environment  100  includes transaction processing network  101 , which may include merchant system  102 , payment gateway  104 , acquirer system  106 , transaction service provider system  108 , and/or issuer system  110 , user device  112 , and/or communication network  114 . Transaction processing network  101 , merchant system  102 , payment gateway  104 , acquirer system  106 , transaction service provider system  108 , issuer system  110 , and/or user device  112  may interconnect (e.g., establish a connection to communicate, etc.) via wired connections, wireless connections, or a combination of wired and wireless connections. 
     Merchant system  102  may include one or more devices capable of receiving information and/or data from payment gateway  104 , acquirer system  106 , transaction service provider system  108 , issuer system  110 , and/or user device  112  via communication network  114  and/or communicating information and/or data to payment gateway  104 , acquirer system  106 , transaction service provider system  108 , issuer system  110 , and/or user device  112  via communication network  114 . Merchant system  102  may include a device capable of receiving information and/or data from user device  112  via a communication connection (e.g., an NFC communication connection, an RFID communication connection, a Bluetooth® communication connection, etc.) with user device  112 , and/or communicating information and/or data to user device  112  via the communication connection. For example, merchant system  102  may include a computing device, such as a server, a group of servers, a client device, a group of client devices, and/or other like devices. In some non-limiting embodiments or aspects, merchant system  102  may be associated with a merchant as described herein. In some non-limiting embodiments or aspects, merchant system  102  may include one or more devices, such as computers, computer systems, and/or peripheral devices capable of being used by a merchant to conduct a payment transaction with a user. For example, merchant system  102  may include a POS device and/or a POS system. 
     Payment gateway  104  may include one or more devices capable of receiving information and/or data from merchant system  102 , acquirer system  106 , transaction service provider system  108 , issuer system  110 , and/or user device  112  via communication network  114  and/or communicating information and/or data to merchant system  102 , acquirer system  106 , transaction service provider system  108 , issuer system  110 , and/or user device  112  via communication network  114 . For example, payment gateway  104  may include a computing device, such as a server, a group of servers, and/or other like devices. In some non-limiting embodiments or aspects, payment gateway  104  is associated with a payment gateway as described herein. 
     Acquirer system  106  may include one or more devices capable of receiving information and/or data from merchant system  102 , payment gateway  104 , transaction service provider system  108 , issuer system  110 , and/or user device  112  via communication network  114  and/or communicating information and/or data to merchant system  102 , payment gateway  104 , transaction service provider system  108 , issuer system  110 , and/or user device  112  via communication network  114 . For example, acquirer system  106  may include a computing device, such as a server, a group of servers, and/or other like devices. In some non-limiting embodiments or aspects, acquirer system  106  may be associated with an acquirer as described herein. 
     Transaction service provider system  108  may include one or more devices capable of receiving information and/or data from merchant system  102 , payment gateway  104 , acquirer system  106 , issuer system  110 , and/or user device  112  via communication network  114  and/or communicating information and/or data to merchant system  102 , payment gateway  104 , acquirer system  106 , issuer system  110 , and/or user device  112  via communication network  114 . For example, transaction service provider system  108  may include a computing device, such as a server (e.g., a transaction processing server, etc.), a group of servers, and/or other like devices. In some non-limiting embodiments or aspects, transaction service provider system  108  may be associated with a transaction service provider as described herein. In some non-limiting embodiments or aspects, transaction service provider  108  may include and/or access one or more one or more internal and/or external databases including transaction data. 
     Issuer system  110  may include one or more devices capable of receiving information and/or data from merchant system  102 , payment gateway  104 , acquirer system  106 , transaction service provider system  108 , and/or user device  112  via communication network  114  and/or communicating information and/or data to merchant system  102 , payment gateway  104 , acquirer system  106 , transaction service provider system  108 , and/or user device  112  via communication network  114 . For example, issuer system  110  may include a computing device, such as a server, a group of servers, and/or other like devices. In some non-limiting embodiments or aspects, issuer system  110  may be associated with an issuer institution as described herein. For example, issuer system  110  may be associated with an issuer institution that issued a payment account or instrument (e.g., a credit account, a debit account, a credit card, a debit card, etc.) to a user (e.g., a user associated with user device  112 , etc.). 
     In some non-limiting embodiments or aspects, transaction processing network  101  includes a plurality of systems in a communication path for processing a transaction. For example, transaction processing network  101  can include merchant system  102 , payment gateway  104 , acquirer system  106 , transaction service provider system  108 , and/or issuer system  110  in a communication path (e.g., a communication path, a communication channel, a communication network, etc.) for processing an electronic payment transaction. As an example, transaction processing network  101  can process (e.g., initiate, conduct, authorize, etc.) an electronic payment transaction via the communication path between merchant system  102 , payment gateway  104 , acquirer system  106 , transaction service provider system  108 , and/or issuer system  110 . 
     User device  112  may include one or more devices capable of receiving information and/or data from merchant system  102 , payment gateway  104 , acquirer system  106 , transaction service provider system  108 , and/or issuer system  110  via communication network  114  and/or communicating information and/or data to merchant system  102 , payment gateway  104 , acquirer system  106 , transaction service provider system  108 , and/or issuer system  110  via communication network  114 . For example, user device  112  may include a client device and/or the like. In some non-limiting embodiments or aspects, user device  112  may be capable of receiving information (e.g., from merchant system  102 , etc.) via a short range wireless communication connection (e.g., an NFC communication connection, an RFID communication connection, a Bluetooth® communication connection, and/or the like), and/or communicating information (e.g., to merchant system  102 , etc.) via a short range wireless communication connection. In some non-limiting embodiments or aspects, user device  112  may include an application associated with user device  112 , such as an application stored on user device  112 , a mobile application (e.g., a mobile device application, a native application for a mobile device, a mobile cloud application for a mobile device, an electronic wallet application, a peer-to-peer payment transfer application, and/or the like) stored and/or executed on user device  112 . 
     Communication network  114  may include one or more wired and/or wireless networks. For example, communication network  114  may include a cellular network (e.g., a long-term evolution (LTE) network, a third generation (3G) network, a fourth generation (4G) network, a code division multiple access (CDMA) network, etc.), a public land mobile network (PLMN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a telephone network (e.g., the public switched telephone network (PSTN)), a private network, an ad hoc network, an intranet, the Internet, a fiber optic-based network, a cloud computing network, and/or the like, and/or a combination of these or other types of networks. 
     The number and arrangement of devices, systems, and networks shown in  FIG. 1  are provided as an example. There may be additional devices, systems, and/or networks, fewer devices, systems, and/or networks, different devices, systems, and/or networks, or differently arranged devices, systems, and/or networks than those shown in  FIG. 1 . Furthermore, two or more devices and/or systems shown in  FIG. 1  may be implemented within a single device and/or system, or a single device and/or system shown in  FIG. 1  may be implemented as multiple, distributed devices and/or systems. Additionally, or alternatively, a set of devices and/or systems (e.g., one or more devices or systems) of environment  100  may perform one or more functions described as being performed by another set of devices or systems of environment  100 . 
     Referring now to  FIG. 2 ,  FIG. 2  is a diagram of example components of a device  200 . Device  200  may correspond to one or more devices of transaction processing network  101 , one or more devices of merchant system  102 , one or more devices of payment gateway  104 , one or more devices of acquirer system  106 , one or more devices of transaction service provider system  108 , one or more devices of issuer system  110 , user device  112  (e.g., one or more devices of a system of user device  112 , etc.), and/or one or more devices of communication network  114 . In some non-limiting embodiments or aspects, one or more devices of transaction processing network  101 , one or more devices of merchant system  102 , one or more devices of payment gateway  104 , one or more devices of acquirer system  106 , one or more devices of transaction service provider system  108 , one or more devices of issuer system  110 , user device  112  (e.g., one or more devices of a system of user device  112 , etc.), and/or one or more devices of communication network  114  can include at least one device  200  and/or at least one component of device  200 . As shown in  FIG. 2 , device  200  may include a bus  202 , a processor  204 , memory  206 , a storage component  208 , an input component  210 , an output component  212 , and a communication interface  214 . 
     Bus  202  may include a component that permits communication among the components of device  200 . In some non-limiting embodiments or aspects, processor  204  may be implemented in hardware, firmware, or a combination of hardware and software. For example, processor  204  may include a processor (e.g., a central processing unit (CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), etc.), a microprocessor, a digital signal processor (DSP), and/or any processing component (e.g., a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), etc.) that can be programmed to perform a function. Memory  206  may include random access memory (RAM), read only memory (ROM), and/or another type of dynamic or static storage device (e.g., flash memory, magnetic memory, optical memory, etc.) that stores information and/or instructions for use by processor  204 . 
     Storage component  208  may store information and/or software related to the operation and use of device  200 . For example, storage component  208  may include a hard disk (e.g., a magnetic disk, an optical disk, a magneto-optic disk, a solid state disk, etc.), a compact disc (CD), a digital versatile disc (DVD), a floppy disk, a cartridge, a magnetic tape, and/or another type of computer-readable medium, along with a corresponding drive. 
     Input component  210  may include a component that permits device  200  to receive information, such as via user input (e.g., a touch screen display, a keyboard, a keypad, a mouse, a button, a switch, a microphone, etc.). Additionally, or alternatively, input component  210  may include a sensor for sensing information (e.g., a global positioning system (GPS) component, an accelerometer, a gyroscope, an actuator, etc.). Output component  212  may include a component that provides output information from device  200  (e.g., a display, a speaker, one or more light-emitting diodes (LEDs), etc.). 
     Communication interface  214  may include a transceiver-like component (e.g., a transceiver, a separate receiver and transmitter, etc.) that enables device  200  to communicate with other devices, such as via a wired connection, a wireless connection, or a combination of wired and wireless connections. Communication interface  214  may permit device  200  to receive information from another device and/or provide information to another device. For example, communication interface  214  may include an Ethernet interface, an optical interface, a coaxial interface, an infrared interface, a radio frequency (RF) interface, a universal serial bus (USB) interface, a Wi-Fi® interface, a cellular network interface, and/or the like. 
     Device  200  may perform one or more processes described herein. Device  200  may perform these processes based on processor  204  executing software instructions stored by a computer-readable medium, such as memory  206  and/or storage component  208 . A computer-readable medium (e.g., a non-transitory computer-readable medium) is defined herein as a non-transitory memory device. A memory device includes memory space located inside of a single physical storage device or memory space spread across multiple physical storage devices. 
     Software instructions may be read into memory  206  and/or storage component  208  from another computer-readable medium or from another device via communication interface  214 . When executed, software instructions stored in memory  206  and/or storage component  208  may cause processor  204  to perform one or more processes described herein. Additionally, or alternatively, hardwired circuitry may be used in place of or in combination with software instructions to perform one or more processes described herein. Thus, embodiments or aspects described herein are not limited to any specific combination of hardware circuitry and software. 
     Memory  206  and/or storage component  208  may include data storage or one or more data structures (e.g., a database, etc.). Device  200  may be capable of receiving information from, storing information in, communicating information to, or searching information stored in the data storage or one or more data structures in memory  206  and/or storage component  208 . For example, transaction service provider system  108  may include and/or access one or more internal and/or external databases that store transaction data associated with transactions processed and/or being processed in transaction processing network  101  (e.g., prior or historical transactions processed via transaction service provider system  108 , etc.). 
     The number and arrangement of components shown in  FIG. 2  are provided as an example. In some non-limiting embodiments or aspects, device  200  may include additional components, fewer components, different components, or differently arranged components than those shown in  FIG. 2 . Additionally or alternatively, a set of components (e.g., one or more components) of device  200  may perform one or more functions described as being performed by another set of components of device  200 . 
     Referring now to  FIG. 3 ,  FIG. 3  is a flowchart of non-limiting embodiments or aspects of a process  300  for false decline mitigation. In some non-limiting embodiments or aspects, one or more of the steps of process  300  may be performed (e.g., completely, partially, etc.) by transaction service provider system  108  (e.g., one or more devices of transaction service provider system  108 ). In some non-limiting embodiments or aspects, one or more of the steps of process  300  may be performed (e.g., completely, partially, etc.) by another device or a group of devices separate from or including transaction service provider system  108 , such as merchant system  102  (e.g., one or more devices of merchant system  102 ), payment gateway  104  (e.g., one or more devices of payment gateway  104 ), acquirer system  106  (e.g., one or more devices of acquirer system  106 ), issuer system  110  (e.g., one or more devices of issuer system  110 ), and/or user device  112  (e.g., one or more devices of a system of user device  112 ). 
     As shown in  FIG. 3 , at step  302 , process  300  includes obtaining an objective function. For example, transaction service provider system  108  may obtain an objective function. As an example, transaction service provider system  108  may obtain an objective function associated with issuer system  110 . 
     In some non-limiting embodiments or aspects, an objective function depends on one or more probabilities of the one or more transactions being falsely declined by one or more RTD rules, one or more amounts of the one or more transactions, and/or the like. 
     Further details regarding non-limiting embodiments or aspects of step  302  of process  300  are provided below with regard to  FIG. 4 . 
     As shown in  FIG. 3 , at step  304 , process  300  includes training a neural network based on prior transaction data to optimize an objective function. For example, transaction service provider system  108  may train a neural network based on prior transaction data to optimize an objective function. As an example, transaction service provider system  108  may train a neural network, based on prior transaction data associated with one or more prior transactions, to optimize the objective function associated with issuer system  110 . 
     In some non-limiting embodiments or aspects, transaction data includes one or more of the following: real-time or current transaction data (e.g., transaction data associated with on-going transactions in transaction processing network  101  that have already been approved by one or more systems in transaction processing network  101 , such as, by transaction service provider system  108  applying RTD rules to approve the on-going transactions, etc.); prior or historical transaction data (e.g., transaction data associated with one or more transactions for which an outcome, such as, a false decline, a true decline, a false authorization, and/or a true authorization is known, for example, prior or historical transaction data may be used to simulate application of a model for generating exclude account lists to determine which accounts may be affected by the model, and/or may be used to simulate application of one or more RTD rules to determine which accounts may be affected by the RTD rules, etc.); alert data (e.g., transaction data generated, based on one or more CC rules, during processing of a transaction associated with an account identifier, transaction data generated and received before a transaction associated with an account identifier is determined to be associated with a false decline, authorization data associated with authorization of a transaction before receiving agent action or outcome data associated with the transaction, transaction data for which one or more RTD rules are not applied, such as transaction data based on use of a portable financial device to initiate a transaction, which may have a one to many relationship with an existing transaction, and/or the like, etc.); fraud, agent action, and/or outcome data (e.g., transaction data associated with an indication that a transaction is a fraudulent transaction or a good transaction, which may be generated by issuer system  110  (e.g., an agent of issuer system  110 , automatically by issuer system  110 , etc.), an indication received from issuer system  110  to place a specific account identifier in an exclude account list, etc.), or any combination thereof. 
     In some non-limiting embodiments or aspects, issuer system  110  determines whether alert data (e.g., an alert, an alert message, etc.) should be generated for a transaction. For example, issuer system  110  may generate an alert message based on processing a transaction (e.g., in response to a request for authorization of a transaction, etc.) that satisfies one or more CC rules. As an example, issuer system  110  may provide the alert to transaction service provider system  108  in response to generating the alert (e.g., immediately upon generating the alert, during processing of the transaction at issuer system  110 , before authorizing or denying the transaction at issuer system  110 , with an authorization or denial response associated with the transaction received from issuer system  110 , before determining at and/or providing from issuer system  110  fraud, agent action, and/or outcome data associated with the transaction, etc.). 
     In some non-limiting embodiments or aspects, transaction data may include transaction parameters associated with transactions, such as payment transactions initiated and/or conducted with an electronic wallet application, and/or the like. Non-limiting examples of transaction parameters include: electronic wallet card data, an account identifier (e.g., a primary account number (PAN), etc.), transaction amount, transaction date and time, conversion rate of currency, merchant type, acquiring institution country, PAN country, response code, merchant name/location, type of currency, and/or the like. Response code may refer to a successful approval/completion of a transaction, denial because card reported as lost or stolen, do not honor, partial approval, VIP approval (e.g., VIP program, exclude account list inclusion, etc.), amount exceeds maximum, insufficient funds, incorrect PIN, suspected fraud, activity amount exceeded, allowable number of PIN-entry tries exceeded, and/or the like. In some non-limiting embodiments or aspects, electronic wallet card data includes one or more of data associated with an identifier regarding a portable financial device to be provided to an electronic wallet application, data associated with an identifier of an issuer associated with the portable financial device to be provided to an electronic wallet application, data associated with an identifier of a transaction service provider associated with the portable financial device to be provided to an electronic wallet application, data associated with a name of a user associated with the portable financial device to be provided to an electronic wallet application, data associated with an account identifier of an account associated with the portable financial device to be provided to an electronic wallet application, and/or the like. 
     In some non-limiting embodiments or aspects, transaction service provider system  108  obtains (e.g., receives, retrieves, accesses, etc.) transaction data from one or more databases and/or from one or more systems of transaction processing network  101 . For example, transaction service provider system  108  may obtain transaction data from the one or more databases and/or from the one or more systems of transaction processing network  101  for training a neural network for generating an exclude account list, generating an exclude account list, updating or modifying an exclude account list, processing a transaction by comparing an account identifier associated with the transaction to an exclude account list, providing design feedback data to issuer system  110 , and/or the like. 
     In some non-limiting embodiments or aspects, transaction service provider system  108  generates one or more probabilities associated with whether one or more account identifiers should be added to an exclude account list based on a machine learning technique (e.g., a pattern recognition technique, a data mining technique, a heuristic technique, a supervised learning technique, an unsupervised learning technique, etc.). For example, transaction service provider system  108  generates a neural network model (e.g., an estimator, a classifier, a prediction model, etc.) based on a machine learning algorithm (e.g., a decision tree algorithm, a gradient boosted decision tree algorithm, a random forest algorithm, a neural network algorithm, a convolutional neural network algorithm, etc.). In such an example, transaction service provider system  108  generates the one or more probabilities using the neural network model. 
     In some non-limiting embodiments or aspects, transaction service provider system  108  generates the neural network model based on prior or historical transaction data associated with one or more prior or historical transactions, to optimize the objective function. For example, the neural network model may be designed to receive, as an input, transaction data associated with a transaction of an account identifier, and provide, as an output, a prediction (e.g., a probability, a binary output, a yes-no output, a score, a prediction score, etc.) as to the transaction of the account identifier being falsely decline (e.g., as to whether to include the account identifier of the transaction on an exclude account list, etc.). 
     In some non-limiting embodiments or aspects, training the neural network to optimize the objective function includes determining, with the neural network, one or more probabilities of the one or more prior transactions being falsely declined by one or more RTD rules, the objective function being dependent on the one or more probabilities; and modifying, using the objective function, one or more parameters of the neural network. In some non-limiting embodiments or aspects, the objective function is dependent on one or more amounts of the one or more transactions. For example, transaction service provider system  108  may process prior or historical transaction data to obtain training data for the neural network model. For example, transaction service provider system  108  may process the prior or historical transaction data to change the transaction data into a format that is analyzed (e.g., by transaction service provider system  108 , etc.) to generate the neural network model. The prior or historical transaction data that is changed may be referred to as training data. As an example, transaction service provider system  108  may process the prior or historical transaction data to obtain the training data based on receiving the prior or historical transaction data. Additionally, or alternatively, transaction service provider system  108  may process the prior or historical transaction data to obtain the training data based on transaction service provider system  108  receiving an indication that transaction service provider system  108  is to process the prior or historical transaction data from a user of transaction service provider system  108  and/or from a user of issuer system  110  (e.g., via the issuer interface described herein, etc.), such as when transaction service provider system  108  receives an indication to create a neural network model for issuer system  110  for generating an exclude account list for false decline mitigation for transactions associated with issuer system  110 . 
     In some non-limiting embodiments or aspects, transaction service provider system  108  processes the prior or historical transaction data by determining one or more false decline variables (and/or true decline variables) based on the prior or historical transaction data. For example, a false decline variable may include a metric, associated with a false decline of a transaction of an account identifier, which is derived based on the prior or historical transaction data (and/or a true decline variable may include a metric, associated with a true decline of a transaction of an account identifier, which is derived based on the prior or historical transaction data). As an example, the false decline variable may be analyzed to generate a model. For example, the false decline variable may include a variable associated with one or more parameters of the prior or historical transaction data. In some non-limiting embodiments or aspects, the false decline variable is a variable associated with a false declined transaction, a true declined transaction, an account identifier, an amount of a transaction, alert data generated, based on one or more CC rules, during processing of a transaction, one or more RTD rules, and/or any other parameter of the transaction data. 
     In some non-limiting embodiments or aspects, transaction service provider system  108  analyzes the training data to generate a neural network model (e.g., a classifier model, a perdition model, etc.). For example, transaction service provider system  108  uses machine learning techniques to analyze the training data to generate the neural network model. As an example, generating the neural network model (e.g., based on training data, etc.) is referred to as training the neural network model. In such an example, machine learning techniques may include supervised and/or unsupervised techniques, such as decision trees (e.g., gradient boosted decision trees), random forest algorithms, logistic regressions, artificial neural networks (e.g., convolutional neural networks, etc.), Bayesian statistics, learning automata, Hidden Markov Modeling, linear classifiers, quadratic classifiers, association rule learning, and/or the like. In some non-limiting embodiments or aspects, the neural network model includes a classifier model that is specific to a particular issuer system  110 , a particular account identifier, a particular group of issuer systems  110 , a particular group of account identifiers (e.g., a subset of PANS, a group of PANS included in a particular BIN, etc.), and/or the like. 
     Additionally, or alternatively, when analyzing the training data, transaction service provider system  108  may identify one or more false decline variables (e.g., one or more independent false decline variables) as predictor variables that are used to make a prediction (e.g., when analyzing the training data, etc.). For example, values of the predictor variables may be inputs to the model. As an example, transaction service provider system  108  may identify a subset (e.g., a proper subset) of false decline variables as predictor variables that are used to accurately predict whether a transaction will be falsely declined. In such an example, the predictor variables may include one or more of the false decline variables, as discussed above, that have a significant impact (e.g., an impact satisfying a threshold, etc.) on a probability of a transaction being a false decline. 
     In some non-limiting embodiments or aspects, transaction service provider system  108  validates the neural network model by providing validation data associated with one or more prior or historical transactions, and determining, based on an output of the neural network model, whether the neural network model correctly, or incorrectly, predicted a false decline (and/or a true decline, a true authorization, a false authorization, etc.) of the one or more prior or historical transactions. For example, transaction service provider system  108  may validate the neural network model based on a validation threshold (e.g., a threshold value of the validation data, etc.). As an example, transaction service provider system  108  may be programmed and/or configured to validate the model when a false decline (and/or a true decline, a true authorization, a false authorization, etc.) is correctly predicted by the model (e.g., when the prediction model correctly predicts 50% of the validation data, when the prediction model correctly predicts 70% of the validation data, etc.). 
     In some non-limiting embodiments or aspects, after the neural network model has been validated, transaction service provider system  108  may further train and/or update the neural network model and/or creates new models based on receiving new training data. In some non-limiting embodiments or aspects, the new training data includes transaction data associated with one or more new and/or current transactions and/or one or more subsequent transactions to the one more new and/or current transactions that is different from the prior or historical transactions. 
     As shown in  FIG. 3 , at step  306 , process  300  includes providing a trained neural network. For example, transaction service provider system  108  may provide a trained neural network. As an example, transaction service provider system  108  may provide the trained neural network. In such an example, transaction service provider system  108  may store the trained neural network model (e.g., store the neural network model for later use, etc.). In some non-limiting embodiments or aspects, transaction service provider system  108  stores the trained neural network model in a data structure (e.g., a database, a linked list, a tree, etc.). In some non-limiting embodiments or aspects, the data structure is located within transaction service provider system  108  and/or external (e.g., remote from) transaction service provider system  108 . In some non-limiting embodiments or aspects, transaction service provider system  108  provides the trained neural network model to another system or device of transaction processing network  101 , such as issuer system  110 , and/or the like. 
     As shown in  FIG. 3 , at step  308 , process  300  includes receiving transaction data generated based on one or more CC rules. For example, transaction service provider system  108  may receive transaction data generated based on one or more CC rules. As an example, transaction service provider system  108  may receive transaction data (e.g., alert data, an alert, an alert message, etc.) generated, based on one or more CC rules, during processing of a transaction (e.g., in transaction processing network  101 , at issuer system  110 , etc.) associated with an account identifier. In such an example, the transaction data (e.g., alert data, an alert, an alert message, etc.) generated, based on the one or more CC rules, may be generated (e.g., by issuer system  110 , etc.) and/or received (e.g., by transaction service provider system  108 , etc.) immediately upon generating the alert, during processing of the transaction at issuer system  110 , before authorizing or denying the transaction at issuer system  110 , with an authorization or denial response associated with the transaction received from issuer system  110 , before determining at issuer system  110  and/or receiving at transaction service provider system  108  fraud, agent action, and/or outcome data associated with the transaction, etc.). 
     In some non-limiting embodiments or aspects, receiving the transaction data generated, based on the one or more CC rules, further includes receiving fraud, agent action, and/or outcome data associated with the transaction and determining that the transaction associated with the account identifier is associated with a false decline based on the fraud, agent action, and/or outcome data. For example, transaction service provider system  108  may receive the fraud, agent action, and/or outcome data associated with the transaction after authorizing or approving the transaction. As an example, transaction service provider system  108  may receive the fraud, agent action, and/or outcome data associated with the transaction after applying an exclude account list and/or one or more RTD rules to determine whether to approve or deny the transaction. 
     As shown in  FIG. 3 , at step  310 , process  300  includes processing transaction data using a trained neural network to generate an exclude account list. For example, transaction service provider system  108  may process transaction data using a trained neural network to generate an exclude account list. As an example, transaction service provider system  108  may process, using the trained neural network, the transaction data to generate an exclude account list (e.g., an exclude account list associated with issuer system  110 , etc.) including the account identifier associated with the transaction data generated based on one or more CC rules. 
     In some non-limiting embodiments or aspects, transaction service provider system  108  generates an a priori active exclude account list. For example, transaction service provider system  108  may automatically add an account identifier to an exclude account list in response to the trained neural network determining, based on transaction data associated with one or more transactions of the account identifier, a probability satisfying a probability threshold (e.g., in response to transaction data associated with one or more transactions of the account identifier satisfying the objective function, etc.). As an example, a threshold probability and/or an objective function may result in the account identifier being automatically added to an exclude account list in response to the objective function and/or the threshold probability being satisfied (e.g., in response to a probability of a transaction being a false decline that is greater than 90 percent and/or associated with a cumulative transaction amount that is greater than a predetermined amount, such as $300, etc.). 
     In some non-limiting embodiments or aspects, transaction service provider system  108  may provide an account identifier to issuer system  110  for review (e.g., via issuer interface, etc.) before adding the account identifier to an exclude account list in response to the trained neural network determining, based on transaction data associated with one or more transactions of the account identifier, a probability that fails to satisfy a probability threshold (e.g., in response to transaction data associated with one or more transactions of the account identifier violating the objective function, etc.). For example, transaction service provider system  108  may add (or decline to add) the account identifier to an exclude account list in response to issuer input received via the issuer interface from issuer system  110 . As an example, a threshold probability and/or an objective function may result in the account identifier being provided to issuer system  110  for review in response to the probability of the transaction failing to satisfy the probability threshold (e.g., in response to a probability of a transaction being a false decline that is greater than 30 percent and less than 90 percent and/or associated with a cumulative transaction amount that is greater than a predetermined amount, such as $100, etc.). 
     In some non-limiting embodiments or aspects, transaction service provider system  108  may not automatically add an account identifier to an exclude account list and may not provide the account identifier to issuer system  110  for review in response to the trained neural network determining, based on transaction data associated with one or more transactions of the account identifier, a probability that fails to satisfy a probability threshold (e.g., in response to a probability of a transaction not being a false decline, such as a probability of the transaction being a false decline that is less than 30 percent, etc.) 
     Accordingly, prior or historical transaction data can be used to predict whether existing RTD rules may cause false declines, and alert data generated based on a CC rule may be used to predict whether the decline may be a false decline. If a false decline is predicted based on the objective function of issuer system  110 , transaction service provider system  108  may pro-actively place an account identifier associated with the transaction predicted to be a false decline on an exclude account list associated with issuer system  110 . 
     As shown in  FIG. 3 , at step  312 , process  300  includes receiving subsequent transaction data associated with a subsequent transaction for an account identifier. For example, transaction service provider system  108  may receive subsequent transaction data associated with a subsequent transaction for an account identifier. As an example, transaction service provider system  108  may receive subsequent transaction data (e.g., a PAN, etc.) associated with a subsequent transaction for the account identifier associated with the transaction data processed, using the trained neural network, to add the account identifier to the exclude account list associated with issuer system  110 . 
     As shown in  FIG. 3 , at step  314 , process  300  includes determining whether to authorize a subsequent transaction, without applying one or more RTD rules, based on an exclude account list and an account identifier associated with the subsequent transaction. For example, transaction service provider system  108  may determine whether to authorize a subsequent transaction, without applying one or more RTD rules, based on an exclude account list and an account identifier associated with the subsequent transaction. As an example, transaction service provider system  108  may determine whether to authorize the subsequent transaction, without applying the one or more RTD rules to the transaction data associated with the subsequent transaction to determine whether to approve or decline the subsequent transaction, based on the exclude account list associated with issuer system  110  and the account identifier associated with the subsequent transaction. 
     Referring now to  FIG. 4 ,  FIG. 4  is a flowchart of non-limiting embodiments or aspects of a process  400  for false decline mitigation. In some non-limiting embodiments or aspects, one or more of the steps of process  400  may be performed (e.g., completely, partially, etc.) by transaction service provider system  108  (e.g., one or more devices of transaction service provider system  108 ). In some non-limiting embodiments or aspects, one or more of the steps of process  400  may be performed (e.g., completely, partially, etc.) by another device or a group of devices separate from or including transaction service provider system  108 , such as merchant system  102  (e.g., one or more devices of merchant system  102 ), payment gateway  104  (e.g., one or more devices of payment gateway  104 ), acquirer system  106  (e.g., one or more devices of acquirer system  106 ), issuer system  110  (e.g., one or more devices of issuer system  110 ), and/or user device  112  (e.g., one or more devices of a system of user device  112 ). 
     As shown in  FIG. 4 , at step  402 , process  400  includes providing an issuer interface. For example, transaction service provider system  108  may provide an issuer interface. As an example, transaction service provider system  108  may provide an issuer interface to issuer system  110 . In such an example, the issuer interface may be programmed and/or configured to receive issuer input from issuer system  110  (e.g., via input component  210 , communication interface  214 , etc.) and/or provide information and/or data to issuer system  110  (e.g., via output component  212 , communication interface  214 , etc.). 
     As shown in  FIG. 4 , at step  404 , process  400  includes receiving issuer input. For example, transaction service provider system  108  may receive issuer input. As an example, transaction service provider system  108  may receive, via the issuer interface from issuer system  110 , issuer input. 
     In some non-limiting embodiments or aspects, transaction service provider system  108  may provide a plurality of different objective functions and/or probability thresholds via the user interface to issuer system  110 . In some non-limiting embodiments or aspects, transaction service provider system  108  may provide options via the user interface for identifying or selecting one or more neural network models, one or more parameters of one or more neural network models, an objective function, one or more parameters of an objective function, a desired number of false declines, a desired number of true declines, and/or the like. 
     As shown in  FIG. 4 , at step  406 , process  400  includes determining an objective function based on issuer input. For example, transaction service provider system  108  may determine an objective function based on issuer input. As an example, transaction service provider system  108  may determine the objective function based on the issuer input received from issuer system  110  via the issuer interface. 
     In some non-limiting embodiments or aspects, transaction service provider system  108  may receive issuer input identifying or selecting a particular objective function of the plurality of different objective functions (and/or a particular probability threshold of a plurality of different probability thresholds) for use in training and/or using a neural network for generating an exclude account list for issuer system  110 . In some non-limiting embodiments or aspects, transaction service provider system  108  may receive issuer input associated with one or more neural network models, one or more parameters of one or more neural network models, an objective function, one or more parameters of an objective function, a desired number of false declines, a desired number of true declines, and/or the like and determine a particular objective function of the plurality of different objective functions for use in training and/or using a neural network for generating an exclude account list with a neural network for issuer system  110  based on the issuer input (e.g., according to one or more algorithms for optimizing parameters to achieve optimal false declines and/or optimal true declines, etc.). 
     As shown in  FIG. 4 , at step  408 , process  400  includes providing design feedback data. For example, transaction service provider system  108  may provide design feedback data. As an example, transaction service provider system  108  may provide, via the issuer interface to issuer system  110 , design feedback data associated with at least one of a number of false declined transactions, a number of true decline transactions, a number of true authorized transactions, a number of false authorized transactions, or any combination thereof associated with issuer system  110 . 
     In some non-limiting embodiments or aspects, transaction service provider system  108  may receive the issuer input in response to providing the design feedback to issuer system  110  via the issuer interface. For example, an issuer may provide user input associated with and/or selecting one or more neural network models, one or more parameters of one or more neural network models, an objective function, one or more parameters of an objective function, a desired number of false declines, a desired number of true declines, and/or the like via the user interface, and transaction service provider system  108  may implement a neural network model, one or more parameters thereof, and/or an objective function thereof for use in training and/or using an exclude account list according to the issuer input. As an example, and as described in more detail herein, transaction service provider system  108  may train a neural network model to optimize the objective function based on prior or historical transaction data (e.g., declines, approves, etc.), alert data (e.g., transaction data generated, based on one or more CC rules, during processing of a transaction associated with an account identifier, etc.), fraud, agent action, or outcome data associated with results of transaction at an account identifier level, and/or the like. 
     Further details regarding non-limiting embodiments or aspects of step  314  of process  300  are provided below with regard to  FIG. 5 . 
     Referring now to  FIG. 5 ,  FIG. 5  is a flowchart of non-limiting embodiments or aspects of a process  500  for false decline mitigation. In some non-limiting embodiments or aspects, one or more of the steps of process  500  may be performed (e.g., completely, partially, etc.) by transaction service provider system  108  (e.g., one or more devices of transaction service provider system  108 ). In some non-limiting embodiments or aspects, one or more of the steps of process  500  may be performed (e.g., completely, partially, etc.) by another device or a group of devices separate from or including transaction service provider system  108 , such as merchant system  102  (e.g., one or more devices of merchant system  102 ), payment gateway  104  (e.g., one or more devices of payment gateway  104 ), acquirer system  106  (e.g., one or more devices of acquirer system  106 ), issuer system  110  (e.g., one or more devices of issuer system  110 ), and/or user device  112  (e.g., one or more devices of a system of user device  112 ). 
     As shown in  FIG. 5 , at step  502 , process  500  includes determining if an account identifier is included in an exclude account list. For example, transaction service provider system  108  may determine if an account identifier is included in an exclude account list. As an example, transaction service provider system  108  may determine if the account identifier associated with the subsequent transaction is included in the exclude account list associated with issuer system  110 . In such an example, transaction service provider system  108  may compare the account identifier associated with the subsequent transaction to account identifiers included in the exclude account list associated with issuer system  110  to determine whether the account identifier is included in the exclude account list. 
     As shown in  FIG. 5 , at step  504 , process  500  includes, in response to determining that an account identifier is included in an exclude account list, authorizing a transaction without applying one or more RTD rules. For example, transaction service provider system  108  may, in response to determining that an account identifier is included in an exclude account list, authorize a transaction without applying one or more RTD rules. As an example, transaction service provider system  108  may, in response to determining that the account identifier associated with the subsequent transaction is included in the exclude account list associated with issuer system  110 , authorize the transaction without applying the one or more RTD rules to the subsequent transaction. In such an example, transaction service provider system  108  may authorize (e.g., automatically authorize, etc.), based on the exclude account list associated with issuer system  110  and the account identifier associated with the subsequent transaction, the subsequent transaction associated with the account identifier without applying one or more RTD rules (e.g., without applying RTD rules created and/or validated by rule validator  720 , etc.) to the subsequent transaction. As an example, the subsequent transaction associated with the account identifier may be authorized, based on the exclude account list and the account identifier, without applying the one or more RTD rules to the transaction, before receiving the fraud, agent action, or outcome data associated with the transaction for the account identifier that caused transaction service provider system  108  to generate (e.g., create, update, modify, etc.) the exclude account list including the account identifier. In such an example, the one or more RTD rules may be applied to the transaction associated with the account identifier that is processed using the trained neural network (e.g., to the transaction associated with the account identifier that is processed in transaction processing network  101  before (e.g., immediately before, etc.) the subsequent transaction, etc.). 
     As shown in  FIG. 5 , at step  506 , process  500  includes, in response to determining that an account identifier is not included in an exclude account list, processing a transaction using one or more RTD rules. For example, transaction service provider system  108  may, in response to determining that an account identifier is not included in an exclude account list, process a transaction using one or more RTD rules. As an example, transaction service provider system  108  may, in response to determining that the account identifier is not included in the exclude account list, process the transaction using one or more RTD rules. In such an example, transaction service provider system  108  may, in response to determining that the account identifier is not included in the exclude account list, process the transaction associated with the account identifier that is processed in transaction processing network  101  before (e.g., immediately before, etc.) the subsequent transaction using the one or more RTD rules (e.g., by applying RTD rules created and/or validated by rule validator  720 , etc.). 
     As shown in  FIG. 5 , at step  508 , process  500  includes generating and/or receiving transaction data based on one or more CC rules. For example, transaction service provider system  108  may generate transaction data (e.g., alert data, etc.) based on one or more CC rules. As an example, transaction service provider system  108  may generate the transaction data associated with the transaction for the account identifier that is processed in transaction processing network  101  before the subsequent transaction based on the one or more CC rules. Additionally, or alternatively, issuer system  110  may generate the transaction data based on the one or more CC rules. For example, transaction service provider system  108  may receive the transaction data (e.g., alert data, etc.) generated based on the one or more CC rules from issuer system  110 . 
     As shown in  FIG. 5 , at step  510 , process  500  includes determining and/or receiving fraud, agent action, and/or outcome data based on transaction data generated from one or more CC rules and a transaction outcome. For example, transaction service provider system  108  may determine fraud, agent action, and/or outcome data based on transaction data generated based on the one or more CC rules and a transaction outcome. As an example, transaction service provider system  108  may determine fraud, agent action, and/or outcome data based on the transaction data associated with the transaction for the account identifier that is processed in transaction processing network  101  before the subsequent transaction based on the transaction data generated from the one or more CC rules being applied to the transaction and a transaction outcome (e.g., a false decline, a true decline, a false authorization, a true authorization, etc.) of the transaction. Additionally, or alternatively, issuer system  110  may determine the fraud, agent action, and/or outcome data based on the one or more CC rules and a transaction outcome. For example, transaction service provider system  108  may receive the fraud, agent action, and/or outcome data (e.g., an indication of a false decline, an indication of a true decline, an indication of a false authorization, an indication of a true authorization, etc.) from issuer system  110 . 
     Referring also to  FIG. 6 ,  FIG. 6  is a diagram of non-limiting embodiments or aspects of an implementation  600  of an RTD rule. For example, an RTD rule may include one or more criteria (e.g., a set of business rules, a time period, a program, a type of portable financial device, a BIN of the issuer, a group of BINs (BID) of the issuer, etc.). As an example, the implementation  600  of an RTD rule shown in  FIG. 6  includes two criteria, which may be search conditions or filters applied to determine whether to authorize/approve or decline/deny a transaction: (i) panFirstDigits BETWEEN 433430000 and 433439999 and (ii) digCommind NOTIN 1. In such an example, panFirstDigits and digCommind may be fields or parameters in the transaction data associated with a transaction and, if each of the criteria are satisfied, the RTD rule may cause transaction processing network  101  (e.g., one or more systems of transaction processing network  101 , such as transaction service provider system  108 , issuer system  110 , and/or the like, etc.) to decline or deny the transaction. For example, RTD rules may include relatively simple criteria or conditions on various data fields in transaction data that can be applied to approve or decline a transaction associated with the transaction data, and multiple conditions may be connected with operators, such as AND, OR, and/or the like (e.g., transactionAmt GREATER-THAN 3000 AND merchantLocation EQUALS “Moscow”, etc.). 
     Referring also to  FIG. 7 ,  FIG. 7  is a diagram of non-limiting embodiments or aspects of an implementation  700  of an RTD rule creation and validation architecture. As shown in  FIG. 7 , an issuer (e.g., issuer system  110 , etc.) may connect to rule validator  720  via a user interface  710 . User interface  710  and/or rule validator  720  may be implemented in hardware, software, or a combination of hardware and software. User interface  710  may include a module, process, and/or application programming interface (API) for a rules test module  712 , a module, process, and/or API for a rules test reports module  714 , and/or a module, process, and/or API for a rules manager  716 . For example, a human agent of an issuer may create rules with rules manager  716  and submit a set of rules to rule validator  720  for validation via rules test module  712 . As an example, results of rule validation may be made available to the issuer via rules test reports module  714 . 
     Still referring to  FIG. 7 , user interface  710  may communicate with rule validator  720  via a data loader module  760 . For example, data loader module  760  submits user requested rule file  772  to rule validator  720  and receives response report file  774  from rule validator  720 . As an example, fraud data may be submitted by issuers and/or cardholders/account holders through an independent process  762  in a form of a flat file  776 , which may be stored in a fraud transactions (TXNs) database  726  of rule validator  720 . In such an example, rule validator  720  may obtain transaction data from an independent risk management plan (RMP) module or process  764 . 
     Rule validator  720  may include a module, process, and/or application programming interface (API) for a process supervisor  722 , a module, process, and/or application programming interface (API) for a local data processor  724 , a module, process, and/or application programming interface (API) for the fraud TXNs database  726 , a module, process, and/or application programming interface (API) for a rules validation process  728 , and/or a module, process, and/or application programming interface (API) for the historical transaction (TXNs) database  730 . For example, process supervisor  722  may co-ordinate various processes of rule validator  720 . As an example, local data processor  724  may be responsible for in-memory data processing. In such an example, rules validation process  728  may read fraudulent transactions from fraud TXNs database  726  and historical transaction data from historical transaction (TXNs) database  730  and perform rules validation using local data processor  728  based on the fraudulent transactions and the historical transactions. For example, rule validator  720  may store results of a rules validation in fraud TXNs database  726  and/or provide the results in response report file  774  to user interface  710 . 
     In some non-limiting embodiments or aspects, transaction service provider system  108  authorizes, based on an exclude account list and an account identifier, a transaction associated with the account identifier without applying one or more RTD rules (e.g., without applying RTD rules created and/or validated by rule validator  720 , etc.) to the transaction. 
     Although embodiments or aspects have been described in detail for the purpose of illustration and description, it is to be understood that such detail is solely for that purpose and that embodiments or aspects are not limited to the disclosed embodiments or aspects, but, on the contrary, are intended to cover modifications and equivalent arrangements that are within the spirit and scope of the appended claims. For example, it is to be understood that the present disclosure contemplates that, to the extent possible, one or more features of any embodiment or aspect can be combined with one or more features of any other embodiment or aspect. In fact, many of these features can be combined in ways not specifically recited in the claims and/or disclosed in the specification. Although each dependent claim listed below may directly depend on only one claim, the disclosure of possible implementations includes each dependent claim in combination with every other claim in the claim set.