| CPC G06Q 20/4016 (2013.01) [G06Q 30/0283 (2013.01)] | 10 Claims |
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1. A computer implemented method for optimizing at least one machine learning model in real-time, the method comprising performing the following steps by a processor:
(a) applying each of a plurality machine learning based fraud detection strategies of the at least one machine learning model to a set of transactions to determine a subset of potentially fraudulent transactions provided for each of the strategies,
(b) determining a fraud value for each of the potentially fraudulent transactions for each of the strategies based on one or more pre-defined factors, wherein the fraud value is a monetary value;
(c) determining an overall fraud value from the fraud value of the potentially fraudulent transactions for each of the strategies;
(d) identifying a first strategy from the machine learning based fraud detection strategies having a highest overall fraud value for respective potentially fraudulent transactions associated with the first strategy as compared to remaining other strategies and corresponding potentially fraudulent transactions and define the first strategy as having a highest priority on a ranked list of the machine learning based fraud detection strategies;
wherein the step of applying the plurality of machine learning based fraud detection strategies is performed in parallel for all of the fraud detection strategies and concurrently to all the transactions in the set so that the fraud value for each of the potentially fraudulent transactions are calculated and compared at a same time point;
(e) removing one or more transactions from the subset of potentially fraudulent transactions from the remaining other strategies that overlap with one or more of the respective potentially fraudulent transactions from the first strategy;
(f) identifying a subsequent strategy from the machine learning based fraud detection strategies having a next highest overall fraud value for its associated potentially fraudulent transactions and add to the ranked list of machine learning based fraud detection strategies while removing from consideration, each of the machine learning based fraud detection strategies with potentially fraudulent transactions associated with previously identified strategies in the ranked list;
(g) repeat step (f) for ranking all remaining strategies from the machine learning based fraud detection strategies in the ranked list until no further strategies left for ranking while subsequent to each ranking, removing corresponding transactions identified in the ranking from the machine learning based fraud detection strategies;
(h) applying in real-time the ranked list of machine learning based fraud detection strategies to subsequent transactions for determining subsequent potentially fraudulent transactions.
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