Methods and systems for generating rules for unseen fraud and credit risks using artificial intelligence

A computer-implemented method comprising:

• accessing, by a server system, payment transaction data associated with a plurality of customers from a transaction database, the payment transaction data comprising information of past payment transactions performed by the plurality of customers within a particular time interval;
• training, by the server system, a first generative adversarial network (GAN) model based, at least in part, on the payment transaction data and a plurality of probable fraud risk conditions, the first GAN model trained to generate simulated customer fraud behaviors, the simulated customer fraud behaviors representing fraudulent customer behaviors associated with respective simulated virtual customers;
• generating simulated customer fraud behaviors using the trained first GAN model;
• filtering, by the server system, the simulated customer fraud behaviors based, at least in part, on a predetermined filtering criterion;
• based on the filtering, outputting filtered simulated customer fraud behaviors;
• generating, by the server system, fraud risk scores for the filtered simulated customer fraud behaviors using a pre-trained fraud risk model;
• retaining, by the server system, a set of simulated customer fraud behaviors from the filtered simulated customer fraud behaviors, the fraud risk scores of the retained set of simulated customer fraud behaviors being lower than a threshold value;
• and
• extracting, by the server system, one or more fraud risk rules based, at least in part, on the retained set of simulated customer fraud behaviors, the retained set of simulated customer fraud behaviors representing customer fraud behaviors that are unknown by respective credit and fraud risk models.