Abstract: A method includes receiving historical interaction data, which includes a plurality of historical interactions. Each historical interaction is associated with a plurality of data fields. The method includes assigning a plurality of weights to the plurality of data fields, generating a neural network using the plurality of weights and the plurality of data fields, identifying a first plurality of feature indicators indicative of a first class, the first class being different from a second class; receiving a second plurality of feature indicators derived from data relating to compromised accounts, updating, a probability distribution component using the first plurality of feature indicators and the second plurality of feature indicators, and receiving current data for an interaction. The method also includes applying the probability distribution component to the current data, and scoring the interaction using the probability distribution component.

Abstract: A method includes receiving historical interaction data, which includes a plurality of historical interactions. Each historical interaction is associated with a plurality of data fields. The method includes assigning a plurality of weights to the plurality of data fields, generating a neural network using the plurality of weights and the plurality of data fields, identifying a first plurality of feature indicators indicative of a first class, the first class being different from a second class; receiving a second plurality of feature indicators derived from data relating to compromised accounts, updating, a probability distribution component using the first plurality of feature indicators and the second plurality of feature indicators, and receiving current data for an interaction. The method also includes applying the probability distribution component to the current data, and scoring the interaction using the probability distribution component.

Abstract: Some embodiments of the present invention relate to systems and methods for estimating validation time that use an estimation of the amount of data fulfilling a rule or a set of rules. This estimation method may be more accurate because it considers how much data will match the rule versus how much data can be discarded. The disclosed systems and methods give visibility into how much time a rule set validation will take and how long the computer system will be unavailable due to the rule set being evaluated. This allows users to make better decisions about whether and when a rule set should be evaluated, and/or whether and how to modify implementation of the rule set.

Abstract: A computer-implemented method includes, receiving, with at least one processor, payment transaction data, extracting, using the at least one processor, catastrophic event data from a network, generating, with the at least one processor, a catastrophic event score based on the payment transaction data and the catastrophic event data, and adjusting financial fraud rules used to decline or approve financial transactions. The financial fraud rules may be adjusted by adding a catastrophic event threshold to the financial fraud rules that utilize the catastrophic event score to determine whether a financial transaction is fraudulent.

Abstract: A method, system, and computer program product for fraud detection receive transaction data associated with a plurality of transactions; determine, based on the transaction data, that two or more consecutive transactions associated with a same account identifier include a value for a same at least one transaction parameter; in response to determining that each of the two or more consecutive transactions associated with the same account identifier include the value for the same at least one transaction parameter, determine a difference between the value for the at least one transaction parameter associated with a first transaction of the two or more consecutive transactions and the value for the at least one transaction parameter associated with a second transaction of the two or more consecutive transactions; and determine, based on the difference, that the two or more consecutive transactions are fraudulent transactions.

Abstract: Some embodiments of the present invention relate to systems and methods for estimating validation time that use an estimation of the amount of data fulfilling a rule or a set of rules. This estimation method may be more accurate because it considers how much data will match the rule versus how much data can be discarded. The disclosed systems and methods give visibility into how much time a rule set validation will take and how long the computer system will be unavailable due to the rule set being evaluated. This allows users to make better decisions about whether and when a rule set should be evaluated, and/or whether and how to modify implementation of the rule set.

Abstract: A method includes receiving historical interaction data, which includes a plurality of historical interactions. Each historical interaction is associated with a plurality of data fields. The method includes assigning a plurality of weights to the plurality of data fields, generating a neural network using the plurality of weights and the plurality of data fields, identifying a first plurality of feature indicators indicative of a first class, the first class being different from a second class; receiving a second plurality of feature indicators derived from data relating to compromised accounts, updating, a probability distribution component using the first plurality of feature indicators and the second plurality of feature indicators, and receiving current data for an interaction. The method also includes applying the probability distribution component to the current data, and scoring the interaction using the probability distribution component.

Abstract: Some embodiments of the present invention relate to systems and methods for estimating validation time that use an estimation of the amount of data fulfilling a rule or a set of rules. This estimation method may be more accurate because it considers how much data will match the rule versus how much data can be discarded. The disclosed systems and methods give visibility into how much time a rule set validation will take and how long the computer system will be unavailable due to the rule set being evaluated. This allows users to make better decisions about whether and when a rule set should be evaluated, and/or whether and how to modify implementation of the rule set.

Abstract: A method, system, and computer program product for fraud detection receive transaction data associated with a plurality of transactions; determine, based on the transaction data, that two or more consecutive transactions associated with a same account identifier include a value for a same at least one transaction parameter; in response to determining that each of the two or more consecutive transactions associated with the same account identifier include the value for the same at least one transaction parameter, determine a difference between the value for the at least one transaction parameter associated with a first transaction of the two or more consecutive transactions and the value for the at least one transaction parameter associated with a second transaction of the two or more consecutive transactions; and determine, based on the difference, that the two or more consecutive transactions are fraudulent transactions.

Abstract: Methods, systems, and computer program products are provided for simulating fraud detection rules. The method includes: receiving user input including at least one proposed fraud detection rule in at least one input field of a user interface; simulating the at least one proposed fraud detection rule by: querying historical transaction data based on the at least one proposed fraud detection rule; and generating a simulation result for the at least one proposed fraud detection rule based on the queried historical transaction data; and displaying the simulation result on the user interface. The simulation result is displayed on the user interface asynchronously and in near real-time relative to receiving the user input.

Abstract: A method, system, and computer program product for false decline mitigation. The method includes obtaining an objective function associated with an issuer system; training a neural network, based on prior transaction data associated with one or more prior transactions, to optimize the objective function; providing the trained neural network; receiving transaction data generated, based on one or more case creation (CC) rules, during processing of a transaction associated with an account identifier; processing, using the trained neural network, the transaction data to generate an exclude account list including the account identifier; receiving subsequent transaction data associated with a subsequent transaction associated with the account identifier; and authorizing, based on the exclude account list and the account identifier, the subsequent transaction associated with the account identifier without applying one or more real-time decisioning (RTD) rules to the subsequent transaction.

Abstract: Some embodiments of the present invention relate to systems and methods for estimating validation time that use an estimation of the amount of data fulfilling a rule or a set of rules. This estimation method may be more accurate because it considers how much data will match the rule versus how much data can be discarded. The disclosed systems and methods give visibility into how much time a rule set validation will take and how long the computer system will be unavailable due to the rule set being evaluated. This allows users to make better decisions about whether and when a rule set should be evaluated, and/or whether and how to modify implementation of the rule set.