System, method, and computer program product for evaluating a fraud detection system

1. A method, comprising:

determining, with at least one processor, a set of transaction related actions for an agent to be conducted during a simulation, wherein the agent comprises a simulated adversarial actor that is designed to engage in fraudulent conduct involving an account of a user, wherein each transaction related action comprises an action associated with conducting a payment transaction by the agent;

selecting, with at least one processor, a first transaction related action from the set of transaction related actions for the agent based on a plurality of features associated with the agent, wherein selecting the first transaction related action from the set of transaction related actions for the agent comprises:

determining the plurality of features associated with the agent based on one or more historical payment transactions of a plurality of historical payment transactions involving the account of the user; and

selecting the first transaction related action from the set of transaction related actions for the agent based on an output from an agent action machine learning model and the plurality of features associated with the agent, wherein the plurality of features associated with the agent are provided as an input to the agent action machine learning model and the first transaction related action is an output of the agent action machine learning model based on the input;

generating, with at least one processor, transaction data associated with a fraudulent transaction based on the first transaction related action;

generating, with at least one processor, a feature vector, wherein the feature vector comprises transaction data associated with the fraudulent transaction;

providing, with at least one processor, the feature vector as an input to a fraud detection machine learning model;

determining, with at least one processor, an output of the fraud detection machine learning model based on the feature vector as the input;

generating, with at least one processor, a fraudulent reward parameter for the first transaction related action based on the output of the fraud detection machine learning model; and

updating, with at least one processor, a weight parameter of the agent action machine learning model based on the fraudulent reward parameter and a reinforcement learning algorithm, the reinforcement learning algorithm being defined as follows:

R t =r t+1 +γr t+2 +γ 2 r t+3 +γ 3 r t+4 + . . . =Σ k=0 ∞ γ k r t+k+1 ,

where r t+k+1 is a predicted fraudulent reward amount for a time interval of the simulation, wherein the predicted fraudulent reward amount is associated with a fraudulent payment transaction that will be conducted by the agent, and y k is a weight parameter with value range: 0<γ<1 that is chosen to optimize a fraudulent reward amount parameter, R t .

2. The method of claim 1 , further comprising:

generating, with at least one processor, a plurality of status indicators regarding fraudulent transaction outcomes associated with transaction related actions performed by the agent based on transaction data associated with the plurality of historical payment transactions, wherein each status indicator comprises an indication of a status of each historical payment transaction as being a fraudulent transaction or a non-fraudulent transaction, and

wherein generating the feature vector comprises:

generating, with at least one processor, the feature vector based on the transaction data associated with the plurality of historical payment transactions, wherein the transaction data associated with the plurality of historical payment transactions comprises the plurality of status indicators, wherein the feature vector comprises transaction data associated with the fraudulent transaction, and wherein the fraudulent transaction is a fraudulent transaction of the plurality of historical payment transactions.

3. The method of claim 1 , wherein determining the set of transaction related actions for the agent comprises: determining the set of transaction related actions for the agent based on historical transaction data associated with the one or more historical transactions.

4. The method of claim 1 , further comprising:

updating the agent action machine learning model based on a fraudulent reward parameter for another transaction related action.

5. The method of claim 1 , further comprising:

assigning, with at least one processor, the fraudulent reward parameter to the first transaction related action; and

storing, with at least one processor, the first transaction related action and the fraudulent reward parameter in a data structure.

6. The method of claim 1 , further comprising:

generating, with at least one processor, a plurality of fraudulent reward parameters for a sequence of transaction related actions based on a plurality of outputs of the fraud detection machine learning model;

determining, with at least one processor, a fraudulent reward amount based on the plurality of fraudulent reward parameters;

assigning, with at least one processor, the fraudulent reward amount to the sequence of transaction related actions; and

storing the fraudulent reward amount and the sequence of transaction related actions in a data structure.

7. The method of claim 6 , further comprising:

determining, with at least one processor, a plurality of sequences of transaction related actions that are each associated with a fraudulent reward amount; and

selecting, with at least one processor, a sequence of transaction related actions from the plurality of sequences of transaction related actions that is associated with a maximum reward amount of a plurality of fraudulent reward amounts.

8. The method of claim 1 , further comprising:

determining, with at least one processor, whether the fraud detection machine learning model is deployed in an active setting; and

performing, with at least one processor, an action associated with enhancing fraud detection for a transaction to be conducted based on determining that the fraud detection machine learning model is deployed in the active setting.

9. A system, comprising:

at least one processor programmed or configured to:

determine a set of transaction related actions for an agent to be conducted during a simulation, wherein the agent comprises a simulated adversarial actor that is designed to engage in fraudulent conduct involving an account of a user, wherein each transaction related action comprises an action associated with conducting a payment transaction by the agent;

select a first transaction related action from the set of transaction related actions for the agent based on a plurality of features associated with the agent; wherein, when selecting the first transaction related action, the at least one processor is programmed or configured to:

determine the plurality of features associated with the agent based on one or more historical payment transactions of a plurality of historical payment transactions involving the account of the user;

select the first transaction related action from the set of transaction related actions for the agent based on an output from an agent action machine learning model and the plurality of features associated with the agent, wherein the plurality of features associated with the agent are provided as an input to an agent action machine learning model, and the first transaction related action is the output of the agent action machine learning model based on the input;

generate transaction data associated with a fraudulent transaction based on the first transaction related action;

generate a feature vector, wherein the feature vector comprises transaction data associated with the fraudulent transaction;

provide the feature vector as an input to a fraud detection machine learning model;

determine an output of the fraud detection machine learning model based on the feature vector as the input;

generate a fraudulent reward parameter for the first transaction related action based on the output of the fraud detection machine learning model; and

update a weight parameter of the agent action machine learning model based on the fraudulent reward parameter and a reinforcement learning algorithm, the reinforcement learning algorithm being defined as follows:

R t =r t+1 +γr t+2 +γ 2 r t+3 +γ 3 r t+4 + . . . =Σ k=0 ∞ γ k r t+k+1 ,

where r t+k+1 is a predicted fraudulent reward amount for a time interval of the simulation, wherein the predicted fraudulent reward amount is associated with a fraudulent payment transaction that will be conducted by the agent, and y k is a weight parameter with value range: 0<γ<1 that is chosen to optimize a fraudulent reward amount parameter, R t .

10. The system of claim 9 , wherein the at least one processor is further programmed or configured to:

generate a plurality of status indicators regarding fraudulent transaction outcomes associated with transaction related actions performed by the agent based on transaction data associated with a plurality of payment transactions, wherein each status indicator comprises an indication of a status of each historical payment transaction as being a fraudulent transaction or a non-fraudulent transaction, and

wherein, when generating the feature vector, the at least one processor is programmed or configured to:

generate the feature vector based on the transaction data associated with the plurality of historical payment transactions, wherein the transaction data associated with the plurality of historical payment transactions comprises the plurality of status indicators, wherein the feature vector comprises transaction data associated with the fraudulent transaction, and wherein the fraudulent transaction is a fraudulent transaction of the plurality of historical payment transactions.

11. The system of claim 9 , wherein the at least one processor is further programmed or configured to:

update the agent action machine learning model based on a fraudulent reward parameter for another transaction related action.

12. A computer program product comprising at least one non-transitory computer-readable medium comprising one or more instructions that, when executed by at least one processor, cause the at least one processor to:

determine a set of transaction related actions for an agent to be conducted during a simulation, wherein the agent comprises a simulated adversarial actor that is designed to engage in fraudulent conduct involving an account of a user, wherein each transaction related action comprises an action associated with conducting a payment transaction by the agent;

select a first transaction related action from the set of transaction related actions for the agent based on a plurality of features associated with the agent; wherein, the one or more instructions that cause the at least one processor to select the first transaction related action from the set of transaction related actions for the agent, cause the at least one processor to:

determine the plurality of features associated with the agent based on one or more historical payment transactions of a plurality of historical payment transactions involving the account of the user;

select the first transaction related action from the set of transaction related actions for the agent based on an output from an agent action machine learning model and the plurality of features associated with the agent, wherein the plurality of features associated with the agent are provided as an input to an agent action machine learning model, and the first transaction related action is the output of the agent action machine learning model based on the input;

generate transaction data associated with a fraudulent transaction based on the first transaction related action;

generate a feature vector, wherein the feature vector comprises transaction data associated with the fraudulent transaction;

provide the feature vector as an input to a fraud detection machine learning model;

determine an output of the fraud detection machine learning model based on the feature vector as the input;

generate a fraudulent reward parameter for the first transaction related action based on the output of the fraud detection machine learning model; and

update a weight parameter of the agent action machine learning model based on the fraudulent reward parameter for the first transaction related action and a reinforcement learning algorithm, the reinforcement learning algorithm being defined as follows:

R t =r t+1 +γr t+2 +γ 2 r t+3 +γ 3 r t+4 + . . . =Σ k=0 ∞ γ k r t+k+1 ,

where r t+k+1 is a predicted fraudulent reward amount for a time interval of the simulation, wherein the predicted fraudulent reward amount is associated with a fraudulent payment transaction that will be conducted by the agent, and y k is a weight parameter with value range: 0<γ<1 that is chosen to optimize a fraudulent reward amount parameter, R t .

13. The computer program product of claim 12 , wherein the one or more instructions further cause the at least one processor to:

generate a plurality of status indicators regarding fraudulent transaction outcomes associated with transaction related actions performed by the agent based on transaction data associated with a plurality of payment transactions, wherein each status indicator comprises an indication of a status of each historical payment transaction as being a fraudulent transaction or a non-fraudulent transaction, and

wherein, the one or more instructions that cause the at least one processor to generate the feature vector, cause the at least one processor to:

generate the feature vector based on the transaction data associated with the plurality of historical payment transactions, wherein the transaction data associated with the plurality of historical payment transactions comprises the plurality of status indicators, wherein the feature vector comprises transaction data associated with the fraudulent transaction, and wherein the fraudulent transaction is a fraudulent transaction of the plurality of historical payment transactions.

14. The computer program product of claim 12 , wherein the one or more instructions that cause the at least one processor to determine the set of transaction related actions for the agent, cause the at least one processor to:

determine the set of transaction related actions for the agent based on historical transaction data associated with the one or more historical transactions.

15. The computer program product of claim 12 , wherein the one or more instructions further cause the at least one processor to:

assign the fraudulent reward parameter to the first transaction related action; and

store the first transaction related action and the fraudulent reward parameter in a data structure.

16. The method of claim 1 , wherein determining the plurality of features associated with the agent based on one or more historical payment transactions of a plurality of historical payment transactions comprises:

determining the plurality of features associated with the agent based on one or more previously-processed fraudulent payment transactions involving an account of a user.

17. The method of claim 1 , further comprising:

adding the transaction data associated with the fraudulent transaction to a training data set of the fraud detection machine learning model; and

retraining the fraud detection machine learning model using the training data set.

18. The system of claim 9 , wherein, when determining the plurality of features associated with the agent based on one or more historical payment transactions of the plurality of historical payment transactions, the at least one processor is programmed or configured to:

determine the plurality of features associated with the agent based on one or more previously-processed fraudulent payment transactions involving an account of a user.

19. The system of claim 9 , wherein the at least one processor is further programmed or configured to:

add the transaction data associated with the fraudulent transaction to a training data set of the fraud detection machine learning model; and

retrain the fraud detection machine learning model using the training data set.

20. The computer program product of claim 12 , wherein the one or more instructions further cause the at least one processor to:

add the transaction data associated with the fraudulent transaction to a training data set of the fraud detection machine learning model; and

retrain the fraud detection machine learning model using the training data set.