Virtual Fraud Detection
A virtual fraud detection system and method is described for the real time processing of banking transactions seen on a banking rail. The transaction is processed through natural language processing to determine who the parties are, and natural language processing is performed on the web site and the social media pages of employees to ascertain if the originator and the beneficiary of the transaction make sense. In addition, the age of the DNS records of the parties is checked to see if the parties are established organizations.
1 . A special purpose computing apparatus for real time detection of fraud on a banking rail, the apparatus comprising:
at least one network interface electrically connected to the banking rail, where the banking rail uses a secure, encrypted channel;
a plurality of processing cores electrically connected to the at least one network interface; and
a storage subsystem electrically connected to the plurality of processing cores,
wherein at least one of the network interfaces receives a transaction from the banking rail and passes the transaction to the processing cores,
wherein the processing cores, using natural language processing on the transaction and on a web page for an originator of the transaction and a web page for a receiver of the transaction, determines a set of industry classifications for the originator and a set of industry classifications for the receiver, and sends the transaction for further review if the industry classification set for the originator does not overlap with the industry classification set for the receiver.
2 . The apparatus of claim 1 wherein the further review is performed by an automaton.
3 . The apparatus of claim 1 wherein the processing cores pipeline analysis of the transactions.
4 . The apparatus of claim 1 wherein the processing cores analyze the transaction in parallel.
5 . The apparatus of claim 1 wherein the processing cores check a date of a domain name server record for the receiver and sends the transaction for the further review if the date is less than a predetermined value.
6 . The apparatus of claim 1 wherein the processing cores check a date of a domain name server record for the originator and sends the transaction for the further review if the date is less than a predetermined value.
7 . The apparatus of claim 1 wherein the processing cores check social media sites for employees of the originator and sends the transaction for the further review if no employees are found on social media.
8 . The apparatus of claim 7 wherein the processing cores perform natural language processing on the social media sites for the employees of the originator to create a set of employee related industry classifications and send the transaction for the further review if the sets of employee related industry classifications do not overlap with the set of classifications of the receiver.
9 . The apparatus of claim 1 wherein the processing cores check social media sites for employees of the receiver and sends the transaction for the further review if no employees are found on social media.
10 . The apparatus of claim 9 wherein the processing cores perform natural language processing on the social media sites for the employees of the receiver to create a set of employee related industry classifications and send the transaction for the further review if the sets of employee related industry classifications do not overlap with the set of classifications of the originator.
11 . A virtual method for detecting fraud from a stream of transactions on a banking rail, the method comprising:
receiving a transaction from the banking rail, where the banking rail uses a secure, encrypted channel for transactions;
executing natural language processing on the transaction to determine a receiver web page associated with a receiving party of the transaction;
determining a set of receiver industry classifications by performing natural language processing on the receiving party web page;
executing natural language processing on the transaction to determine an originator web page associated with an originating party of the transaction;
determining a set of originator industry classifications by performing natural language processing on the originating party web page;
sending the transaction to additional review if the set of originator industry classifications do not overlap the set of receiver industry classifications.
12 . The method of claim 11 wherein the additional review is performed by an automaton.
13 . The method of claim 11 further comprising
checking a date of a domain name server record for the receiver and
sending the transaction for the additional review if the date is less than a predetermined value.
14 . The method of claim 11 further comprising
checking a date of a domain name server record for the originator and
sending the transaction for the additional review if the date is less than a predetermined value.
15 . The method of claim 11 further comprising
checking social media sites for employees of the originator and
sending the transaction for the additional review if no employees are found on social media.
16 . The method of claim 15 further comprising
natural language processing on the social media sites for the employees of the originator to create a set of employee related industry classifications and
sending the transaction for the additional review if the sets of employee related industry classifications do not overlap with the set of classifications of the receiver.
17 . The method of claim 11 further comprising
checking social media sites for employees of the receiver and
sending the transaction for the additional review if no employees are found on social media.
18 . The method of claim 17 further comprising
natural language processing on the social media sites for the employees of the receiver to create a set of employee related industry classifications and
sending the transaction for the additional review if the sets of employee related industry classifications do not overlap with the set of originator industry classifications.