UTILIZING MACHINE LEARNING MODELS TO GENERATE FRAUD PREDICTIONS FOR CARD-NOT-PRESENT NETWORK TRANSACTIONS

1 . A computer-implemented method comprising:

receiving a request to initiate a network transaction comprising credit card information and an indication that a credit card associated with the credit card information is not present for the network transaction;

identifying one or more features associated with the network transaction;

generating, utilizing a card-not-present machine learning model, a fraud prediction for the network transaction based on the one or more features; and

processing the network transaction by applying transaction logic based on the fraud prediction.

2 . The computer-implemented method of claim 1 , wherein identifying one or more features associated with the network transaction further comprises identifying one or more of event features associated with the network transaction, merchant data features, user account data features, historical user transaction features, or historical merchant data features.

3 . The computer-implemented method of claim 1 , further comprising:

receiving a risk score associated with the network transaction; and

generating the fraud prediction based on the one or more features and the risk score.

4 . The computer-implemented method of claim 1 , wherein processing the network transaction based on the fraud prediction comprises:

identifying that the fraud prediction satisfies a high-risk fraud prediction; and

denying the request to initiate the network transaction based on the fraud prediction satisfying the high-risk fraud prediction.

5 . The computer-implemented method of claim 1 , wherein processing the network transaction comprises:

identifying that the fraud prediction satisfies a low-risk prediction; and

approving the request to initiate the network transaction based on the fraud prediction satisfying the low-risk prediction.

6 . The computer-implemented method of claim 1 , further comprising:

identifying that the fraud prediction satisfies a moderate-risk prediction;

sending a prompt to a client device associated with the network transaction requesting authorization for the network transaction; and

receiving, from the client device, a response to the prompt indicating whether the network transaction is authorized or unauthorized.

7 . The computer-implemented method of claim 6 , further comprising:

identifying that the response to the prompt indicates that the network transaction is not authorized; and

denying the request to initiate the network transaction based on the indication that the network transaction is not authorized.

8 . The computer-implemented method of claim 6 , further comprising:

identifying that the response to the prompt indicates that the network transaction is authorized; and

approving the request to initiate the network transaction based on the indication that the network transaction is authorized.

9 . The computer-implemented method of claim 6 , further comprising:

generating a fraud label for the network transaction based on the response to the prompt indicating whether the network transaction is authorized or unauthorized; and

modifying parameters of the card-not-present machine learning model based on the fraud label.

10 . The computer-implemented method of claim 9 , further comprising:

identifying a digital claim disputing the network transaction;

comparing the digital claim to the fraud label for the network transaction; and

modifying parameters of the card-not-present machine learning model based on comparing the digital claim to the fraud label.

11 . A non-transitory computer-readable medium storing instructions that, when executed by at least one processor, cause a computer system to:

receive a request to initiate a network transaction comprising credit card information and an indication that a credit card associated with the credit card information is not present for the network transaction;

identify one or more features associated with the network transaction;

generate, utilizing a card-not-present machine learning model, a fraud prediction for the network transaction based on the one or more features associated with the network transaction; and

process the network transaction based on the fraud prediction.

12 . The computer-readable medium of claim 11 , further comprising instructions that, when executed by the at least one processor, cause the computer system to:

receive, from a third-party transaction analysis system, a risk score associated with the network transaction; and

generate the fraud prediction based on the one or more features and the risk score.

13 . The computer-readable medium of claim 11 , wherein identifying one or more features associated with the network transaction further comprises identifying one or more of event data associated with the network transaction, a merchant data feature, a user account data feature, a historical user transaction feature, or a historical merchant data feature.

14 . The computer-readable medium storing of claim 11 , further comprising instructions that, when executed by at least one processor, cause a computer system to:

identify that the fraud prediction satisfies a high-risk prediction; and

deny the request to initiate the network transaction based on the fraud prediction satisfying the high-risk prediction.

15 . The computer-readable medium storing of claim 11 , further comprising instructions that, when executed by at least one processor, cause a computer system to:

identify that the fraud prediction satisfies a moderate-risk prediction;

send a prompt to a client device associated with the network transaction requesting additional information about the network transaction;

receive, from the client device, a response to the prompt indicating that the network transaction is authorized; and

approve the request to initiate the network transaction based on the response to the prompt.

16 . The computer-readable medium storing of claim 15 , further comprising instructions that, when executed by at least one processor, cause a computer system to:

identify a digital claim disputing the network transaction;

determine that the network transaction was approved based on the response to the prompt indicating that the network transaction was authorized; and

determine that the network transaction was fraudulent.

17 . A system comprising:

at least one processor; and

at least one non-transitory computer-readable storage medium storing instructions that, when executed by the at least one processor, cause the system to:

receive a request to initiate a network transaction comprising credit card information and an indication that a credit card associated with the credit card information is not present for the network transaction;

identify one or more features associated with the network transaction;

generate, utilizing a card-not-present machine learning model, a fraud prediction for the network transaction based on the one or more features associated with the network transaction; and

process the network transaction based on the fraud prediction.

18 . The system of claim 17 , further comprising instructions that, when executed by the at least one processor, cause the system to:

receive a risk score associated with the network transaction; and

generate the fraud prediction based on the one or more features and the risk score.

19 . The system of claim 17 , further comprising instructions that, when executed by the at least one processor, cause the system to:

identify that the fraud prediction satisfies a high-risk prediction; and

deny the request to initiate the network transaction based on the fraud prediction satisfying the high-risk prediction.

20 . The system of claim 17 , further comprising instructions that, when executed by the at least one processor, cause the system to:

identify that the fraud prediction satisfies a low-risk prediction; and

approve the request to initiate the network transaction based on the fraud prediction satisfying the low-risk prediction.