Systems and methods for detection of fraud attacks using merchants to test payment accounts

A computer-implemented method for detecting compromised merchants in a payment card network, wherein the method is performed by a computing system including at least one processor in communication with at least one memory, the method comprising:

• receiving historical transaction data associated with a plurality of historical transactions carried out at a plurality of merchants;
• determining, based upon the historical transaction data, historical values for key merchant variables for each merchant of the plurality of merchants;
• storing the historical key merchant variable values in the at least one memory;
• receiving current transaction data associated with a plurality of current transactions from a merchant of the plurality of merchants, wherein the plurality of current transactions are processed at the merchant of the plurality of merchants, wherein the plurality of current transactions comprises transactions and test transactions of payment accounts, wherein the test transactions are initiated at the merchant over the payment card network during a testing period;
• determining, based upon the current transaction data, current values for the key merchant variables for the merchant;
• generating, utilizing at least one of machine learning and artificial intelligence techniques, one or more detection models based upon the historical key merchant variable values to identify abnormalities in the current transaction data;
• comparing, in real-time utilizing the one or more detection models, the current key merchant variable values with the historical key merchant variable values for the merchant;
• identifying, in real-time utilizing the one or more detection models, abnormalities between the current key merchant variable values and the historical key merchant variable values for the merchant;
• determining, based upon the identified abnormalities, that the merchant is a compromised merchant and the test transactions processed at the compromised merchant are fraudulent;
• identifying a merchant computing system of the compromised merchant being used to process the test transactions, wherein the merchant computing system is a compromised merchant computing system;
• based on the identified compromised merchant computing system, identifying at least one of code fixes and security patches for the compromised merchant computing system of the compromised merchant;
• and
• transmitting a notification to the compromised merchant, wherein the notification comprises an alert of the compromised merchant computing system, and the at least one of code fixes and security patches for the compromised merchant computing system, wherein the transmitting the notification further comprises deploying the at least one of the code fixes and the security patches to the compromised merchant computing system.

A non-transitory computer-readable storage medium including computer-executable instructions stored thereon, wherein when executed by a computing device including at least one processor in communication with at least one memory, the computer-executable instructions cause the at least one processor to perform the steps of:

• receiving historical transaction data associated with a plurality of historical transactions carried out at a plurality of merchants;
• determining, based upon the historical transaction data, historical values for key merchant variables for each merchant of the plurality of merchants;
• storing the historical key merchant variable values in the at least one memory;
• receiving current transaction data associated with a plurality of current transactions from a merchant of the plurality of merchants, wherein the plurality of current transactions are processed at the merchant of the plurality of merchants, wherein the plurality of current transactions comprises transactions and test transactions of payment accounts, wherein the test transactions are initiated at the merchant over the payment card network during a testing period;
• determining, based upon the current transaction data, current values for the key merchant variables for the merchant;
• generating, utilizing at least one of machine learning and artificial intelligence techniques, one or more detection models based upon the historical key merchant variable values to identify abnormalities in the current transaction data;
• comparing, in real-time utilizing the one or more detection models, the current key merchant variable values with the historical key merchant variable values for the selected merchant;
• identifying, in real-time utilizing the one or more detection models, abnormalities between the current key merchant variable values and the historical key merchant variable values for the merchant;
• determining, based upon the identified abnormalities, that the merchant is a compromised merchant and the test transactions processed at the compromised merchant are fraudulent;
• identifying a merchant computing system of the compromised merchant being used to process the test transactions, wherein the merchant computing system is a compromised merchant computing system;
• based on the identified compromised merchant computing system, identifying at least one of code fixes and security patches for the compromised merchant computing system of the compromised merchant;
• and
• transmitting a notification to the compromised merchant, wherein the notification comprises an alert of the compromised merchant computing system, and the at least one of code fixes and security patches for the compromised merchant computing system, wherein the transmitting the notification further comprises deploying the at least one of the code fixes and the security patches to the compromised merchant computing system.