Deep behavioral networks for fraud detection

A method for detecting a fraudulent transaction using a fraud detection transaction processing system communicably coupled with a client and a user via one or more communication links, the method comprising:

• receiving, by a neural-based transaction processing module, which is a layer of neural cells with local memory where the memory is time-decayed prior to state updates upon processing of a new sample, the neural-based transaction processing module, first information associated with a first proposed electronic transaction from the client over the one or more communication links, the first proposed electronic transaction constituting the new sample for a given iteration, wherein the first information from the client comprises a currently attempted payment charge, including an account number, an account owner, a day and time of the first proposed electronic transaction, an amount of the first proposed electronic transaction, a location of where the first proposed electronic transaction is occurring, a vendor seeking authorization of the charge, or manner in which the transaction is executed;
• retrieving, by the neural-based transaction processing module from a communicably coupled storage repository, second information, wherein the second information comprises a state of an account used to make the first proposed electronic transaction, the state of the account indicative of a duration from at least one prior electronic transaction that is associated with the first proposed electronic transaction;
• generating third information by calculating, using a controller executing on a hardware processor and associated with and executable by the neural-based transaction processing module, an exponential time-decayed algorithm using the second information, wherein the calculating of the exponential time-decayed algorithm in a current iteration causes the local memory to be time-decayed, exponentially, and comprises
• invoking a weighting module to compute a weighting function from a time interval between the at least one prior electronic transaction and the first proposed electronic transaction,
• multiplying, by the weighting module or the controller of the transaction processing system, an output of a previous iteration of calculating the exponential time-decayed algorithm to the weighting function, wherein the output of a previous iteration of calculating the exponential time-decayed algorithm is a time-decayed algorithm calculated in the previous iteration, the exponential time-decayed algorithm calculated in the previous iterations providing long-term retention, for a duration set by a half-life parameter of said exponential time-decayed algorithm, of contributions of prior transactions that are not the most recent transaction, and
• adding, by the weighting module or the controller, the multiplication result and the received first information of the first proposed electronic transaction to update the exponential time-decayed algorithm, thereby generating the third information in the current iteration, which comprises a weighted summation of the first proposed electronic transaction relative to the prior transactions;
• generate fourth information by
• receiving, by the weighting module, the third information from the neural-based transaction processing module,
• applying, by the weighting module, a weighting factor to the third information to generate fourth information;
• calculating, by the weighting module, at least one processing algorithm using the first information and the fourth information to generate an output including a risk score that is indicative of whether the first proposed electronic transaction is fraudulent;
• and
• providing, by the weighting module or the neural-based transaction processing module, the output to the client, over the one or more communication links, to flag to the client while the first proposed electronic transaction is in-flight, and before the first proposed electronic transaction is completed, whether the first proposed electronic transaction is fraudulent,
• wherein a total latency of the fraud detection transaction processing system, as defined by a duration between a time of a receipt of the first information by the neural-based transaction module and a time of a corresponding provision of the output to the client, is a matter of milli-seconds.