Abstract: A computer system for detecting banking frauds in historical data and future transactions from a user supplied specimen set of fraudulent transactions can include: a user console that receives a type of fraud and a plurality of known fraudulent banking transactions associated with the type of fraud from the user; a first set of clue detectors operating on the plurality of known fraudulent banking transactions from the user; a clue detector archive that stores a second set of clue detectors, wherein each of the stored second set of clue detectors has a score that exceeds a threshold value for each clue detector; and a backpropagation neural network that calculates a weight for the stored second set of clue detectors using a learning scheme, wherein a fraud scenario is created based on the stored second set of clue detectors and corresponding weights.

Abstract: A system for detecting banking frauds in historical data and future transactions from a user supplied specimen set of fraudulent transactions, said specimen set of transactions defining one type of fraud identified by the user, said system comprises: means (301) to accept at least one set of banking transactions from the user and means to accept a type of fraud associated with each said set of transactions from the user (FIG. 3, Step 1); means (302) to run a set of atomic clue detectors on each said transaction for each said specimen (FIG. 3, Step 2); means (303) to store the output of said clue detectors for each said transactions for each said specimen fraudulent transactions (FIG. 3, Step 3); means (303) to compare the output of each said clue detector with a pre-defined threshold (FIG. 3, Step 3); means (304, 305) to assign weight to each said clue detector (FIG. 3, Step 4 and 5); means (306) to combine the clue detectors and their said weights into one fraud scenario (FIG.