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Authors: Luis Zhinin-Vera ; Oscar Chang ; Rafael Valencia-Ramos ; Ronny Velastegui ; Gissela E. Pilliza and Francisco Quinga Socasi

Affiliation: School of Mathematical and Computational Sciences, Yachay Tech University, 100650, Urcuqui, Ecuador

Keyword(s): Agents, Credit Card Fraud Detector, Deep Learning.

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.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Zhinin-Vera, L.; Chang, O.; Valencia-Ramos, R.; Velastegui, R.; Pilliza, G. and Socasi, F. (2020). Q-Credit Card Fraud Detector for Imbalanced Classification using Reinforcement Learning. In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-758-395-7; ISSN 2184-433X, SciTePress, pages 279-286. DOI: 10.5220/0009156102790286

@conference{icaart20,
author={Luis Zhinin{-}Vera. and Oscar Chang. and Rafael Valencia{-}Ramos. and Ronny Velastegui. and Gissela E. Pilliza. and Francisco Quinga Socasi.},
title={Q-Credit Card Fraud Detector for Imbalanced Classification using Reinforcement Learning},
booktitle={Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2020},
pages={279-286},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009156102790286},
isbn={978-989-758-395-7},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - Q-Credit Card Fraud Detector for Imbalanced Classification using Reinforcement Learning
SN - 978-989-758-395-7
IS - 2184-433X
AU - Zhinin-Vera, L.
AU - Chang, O.
AU - Valencia-Ramos, R.
AU - Velastegui, R.
AU - Pilliza, G.
AU - Socasi, F.
PY - 2020
SP - 279
EP - 286
DO - 10.5220/0009156102790286
PB - SciTePress

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