US Patent Application 17828970. METHODS AND SYSTEMS FOR REDUCING FALSE POSITIVES FOR FINANCIAL TRANSACTION FRAUD MONITORING USING ARTIFICIAL INTELLIGENCE simplified abstract
METHODS AND SYSTEMS FOR REDUCING FALSE POSITIVES FOR FINANCIAL TRANSACTION FRAUD MONITORING USING ARTIFICIAL INTELLIGENCE
Organization Name
Mastercard International Incorporated
Inventor(s)
Fariborz Nadi of Fairfield CA (US)
Jose Qiu Chou of San Leandro CA (US)
Yuanzheng Du of San Francisco CA (US)
METHODS AND SYSTEMS FOR REDUCING FALSE POSITIVES FOR FINANCIAL TRANSACTION FRAUD MONITORING USING ARTIFICIAL INTELLIGENCE - A simplified explanation of the abstract
This abstract first appeared for US patent application 17828970 titled 'METHODS AND SYSTEMS FOR REDUCING FALSE POSITIVES FOR FINANCIAL TRANSACTION FRAUD MONITORING USING ARTIFICIAL INTELLIGENCE
Simplified Explanation
- The patent application is about reducing false positives for financial transaction fraud monitoring using machine learning techniques. - It introduces an original model for categorizing transactions into high risk and low risk for fraud. - Transactions labeled as high risk may be either false positives or true positives. - The application proposes training two or more false positive reduction models (FPRMs) using iterative machine learning techniques. - These FPRMs are trained using the labels and data associated with the transactions. - Once training is complete, future transactions can be processed using the original model. - If the original model indicates a future transaction as high risk, the trained FPRMs can process the associated data to determine if it is at high risk or low risk of being fraudulent.
Original Abstract Submitted
Systems and methods for reducing false positives for financial transaction fraud monitoring using machine learning techniques. Using an original model for separating transactions into high risk and low risk categories for fraud, transactions falling into the high-risk category may be labeled as a false positive or a true positive. The labels and data associated with the transactions may be used to train two or more false positive reduction models (FPRMs) using iterative machine learning techniques. Once training is complete, a future transaction may be processed using the original model, and, if the original model indicates that the future transaction is high risk, data associated with the future transaction may be processed by the trained FPRM(s), which may determine whether the future transaction is at a high risk or a low risk of being fraudulent.
