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 generating a lexicon of words, organized into weighted categories, from a user defined set of example documents for detecting suspicious e-mails from a mail archive is provided. The system uses a set of example documents and e-mails given by the user to probabilistically find possible lists of critical words. The obtained list is now applied on an archive of e-mails. The system generates an inverted index on the mails from the archive to facilitate search for the key phrases. User feedback is taken on the results obtained and corrections in the lexicon made if necessary. Thus, the mails are scanned based on user feedback, user defined words and automatically generated word list. These lists constantly adapt as e-mails in the archive change. The system then combines all these to present the user with several possible sets of keywords and their relative importance that can be used as a policy for a desired level of accuracy. The system also shows the user any change if the set is modified.

Abstract: A system for detecting information leakage in e-mails using neural network and support vector machines is provided. This system does not use the content of the e-mail or the content of the attachments in the e-mail. Instead, a set of non-sensitive variables or attributes is picked from the e-mails originating from a given establishment and also from the profiles of the users sending those mails. The said attributes are extracted for all outbound mails. This extraction process does not involve reading the main text of the mail and thus the sensitivity of the mail information is protected. These attributes are chosen using filters built into the detection hardware. Neural networks and support vector machine built into the detection hardware are then used on these attributes to detect pattern violation and possible information leakage.

Abstract: A system for generating a lexicon of words, organized into weighted categories, from a user defined set of example documents for detecting suspicious e-mails from a mail archive is provided. The system uses a set of example documents and e-mails given by the user to probabilistically find possible lists of critical words. The obtained list is now applied on an archive of e-mails. The system generates an inverted index on the mails from the archive to facilitate search for the key phrases. User feedback is taken on the results obtained and corrections in the lexicon made if necessary. Thus, the mails are scanned based on user feedback, user defined words and automatically generated word list. These lists constantly adapt as e-mails in the archive change. The system then combines all these to present the user with several possible sets of keywords and their relative importance that can be used as a policy for a desired level of accuracy. The system also shows the user any change if the set is modified.

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.

Abstract: A system for detecting information leakage in e-mails using neural network and support vector machines is provided. This system does not use the content of the e-mail or the content of the attachments in the e-mail. Instead, a set of non-sensitive variables or attributes is picked from the e-mails originating from a given establishment and also from the profiles of the users sending those mails. The said attributes are extracted for all outbound mails. This extraction process does not involve reading the main text of the mail and thus the sensitivity of the mail information is protected. These attributes are chosen using filters built into the detection hardware. Neural networks and support vector machine built into the detection hardware are then used on these attributes to detect pattern violation and possible information leakage.