Q-Credit Card Fraud Detector for Imbalanced Classification using Reinforcement Learning

Luis Zhinin-Vera, Oscar Chang, Rafael Valencia-Ramos, Ronny Velastegui, Gissela E. Pilliza, Francisco Quinga-Socasi

Abstract: Every year, billions of dollars are lost due to credit card fraud, causing huge losses for users and the financial industry. This kind of illicit activity is perhaps the most common and the one that causes most concerns in the finance world. In recent years great attention has been paid to the search for techniques to avoid this significant loss of money. In this paper, we address credit card fraud by using an imbalanced dataset that contains transactions made by credit card users. Our Q-Credit Card Fraud Detector system classifies transactions into two classes: genuine and fraudulent and is built with artificial intelligence techniques comprising Deep Learning, Auto-encoder, and Neural Agents, elements that acquire their predicting abilities through a Q-learning algorithm. Our computer simulation experiments show that the assembled model can produce quick responses and high performance in fraud classification.

External IDs:dblp:conf/icaart/Zhinin-VeraCVVP20