Systems and methods for improved fraud detection

A computer-implemented method comprising:

• applying, by a risk management server, fraudulent transaction data to a machine learning model to determine fraud transaction parameters associated with known fraudulent transactions, and wherein the known fraudulent transactions are associated with merchants;
• generating, by the risk management server, a graphical user interface (GUI) for display via an issuer server, the GUI comprising:
• risk levels corresponding to the fraud transaction parameters,
• threshold fraud rates, and
• acceptable risk thresholds;
• receiving, by the risk management server from the issuer server via the GUI, a rule selection data signal comprising:
• a risk level selection;
• a threshold fraud rate selection;
• and
• an acceptable risk threshold selection;
• receiving, by the risk management server, a transaction data signal comprising live transaction data associated with live transactions being processed by the issuer server;
• identifying, by the risk management server, first risky transactions from the live transactions based on the risk level selection and the fraud transaction parameters;
• identifying, by the risk management server, high risk merchants from the merchants based on the threshold fraud rate selection;
• identifying, by the risk management server, second risky transactions from the live transactions, wherein the second risky transactions are associated with the high risk merchants;
• analyzing, by the risk management server, the first risky transactions and the second risky transactions based on the acceptable risk threshold selection to identify suspected fraudulent transactions;
• transmitting, by the risk management server, a transaction authorization signal to the issuer server to cause the first risky transactions and the second risky transactions satisfying the acceptable risk threshold selection to be automatically authorized;
• determining, by the risk management server, a fraud detection rate based on indicative of a proportion or a percentage of the known fraudulent transactions identified as suspected fraudulent transactions;
• and
• receiving, by the risk management server from the issuer server via the GUI, an updated rule selection data signal, the updated rule selection data comprising at least one of an updated risk level selection, an updated threshold fraud rate selection, or an updated acceptable risk threshold selection, and the updated rule selection data configured to increase the fraud detection rate.

A computer-implemented method comprising:

• receiving, by a risk management server, transaction parameters for known fraudulent transactions;
• identifying, by the risk management server, fraud transaction parameters by applying the transaction parameters to a machine learning model, wherein the fraud transaction parameters comprise ranges of fraud parameter values;
• associating, by the risk management server, the ranges of fraud parameter values with risk levels;
• receiving, by the risk management server from a transaction data server, test transaction parameters for test transactions corresponding to merchants;
• classifying, by the risk management server, the merchants associated with a fraud rate that exceeds a threshold fraud rate as high risk merchants;
• transmitting, by the risk management server, a graphical user interface (GUI) signal for accessing a GUI to an issuer server, wherein the GUI is to display:
• the risk levels;
• and
• the high risk merchants;
• receiving, by the risk management server from the issuer server, a risk level selection data signal based on a risk level selection at the issuer server via the GUI;
• determining, by the risk management server, suspected fraudulent test transactions in the test transactions based on the risk level selection;
• determining, by the risk management server, a fraud detection rate based on the suspected fraudulent test transactions and the known fraudulent transactions, the fraud detection rate indicative of a proportion or a percentage of the known fraudulent transactions identified as suspected fraudulent test transactions;
• receiving, by the risk management server from the issuer server, an updated risk level selection data signal based on an updated risk level selection at the issuer server via the GUI, the updated risk level selection configured to increase the fraud detection rate;
• receiving, by the risk management server, a transaction data signal comprising live transaction data associated with live transactions being processed by the issuer server;
• analyzing, by the risk management server, the live transactions based on the risk level selection to identify suspected fraudulent live transaction in the live transactions;
• and
• transmitting, by the risk management server, a transaction denial signal to the issuer server to cause the suspected fraudulent live transactions to be automatically denied.