Reducing false positives using customer feedback and machine learning

A computer-implemented method of reducing a future amount of electronic fraud alerts generated that are false positives using customer feedback, the method comprising:

• receiving, by one or both of (i) one or more processors and (ii) one or more transceivers, data detailing a first financial transaction associated with a customer;
• inputting, by the one or more processors, the data detailing the first financial transaction into a rules-based engine that generates an electronic fraud alert based upon the data detailing the first financial transaction;
• transmitting, by one or both of (i) the one or more processors and (ii) the one or more transceivers, the electronic fraud alert to a mobile device of the customer via a wireless communication channel to alert the customer;
• receiving, by one or both of (i) the one or more processors and (ii) the one or more transceivers, customer feedback from the mobile device of the customer via the wireless communication channel, the customer feedback indicating that the electronic fraud alert was a false positive or otherwise erroneous;
• determining, by the one or more processors using a machine learning program and based at least in part on the customer feedback, a reason indicating why the electronic fraud alert was generated;
• updating, by the one or more processors using the machine learning program, the rules-based engine to account for the reason, and to no longer generate electronic fraud alerts based upon (a) fact patterns substantially identical to fact patterns of the financial transaction, or (b) data substantially identical to the data detailing the first financial transaction, to facilitate reducing an amount of false positive fraud alerts generated in the future;
• and
• evaluating, by the one or more processors using the updated rules-based engine, a second financial transaction.