Reducing false positives using customer feedback and machine learning

A computer-implemented method for reducing false positive electronic fraud alerts, the method comprising:

• receiving data detailing a first financial transaction associated with a customer;
• generating, by a rules-based engine, based at least in part on the data detailing the first financial transaction, an electronic fraud alert;
• transmitting the electronic fraud alert to a device of the customer;
• receiving customer feedback from the device of the customer, the customer feedback indicating that the electronic fraud alert was a false positive;
• generating, by one or more processors and based at least in part on the data detailing the first financial transaction, an updated rules-based engine to facilitate reducing an amount of false positive fraud alerts generated in the future, wherein generating the updated rules-based engine comprises:
• providing, to a trained machine learning program, input data associated with the first financial transaction;
• determining, based on an output of the machine learning program, a reason why the false positive was generated;
• and
• modifying a first rule of the rules-based engine, based on the determined reason why the false positive was generated;
• and
• evaluating, by the one or more processors using the updated rules-based engine, a second financial transaction.