Home
Author Guide
Editor Guide
Reviewer Guide
Published Issues
Special Issue
Introduction
Special Issues List
Sections and Topics
Sections
Topics
Internet of Things (IoT) in Smart Systems and Applications
Human-Computer Interaction (HCI) in Modern Technological Systems
journal menu
Aims and Scope
Editorial Board
Indexing Service
Article Processing Charge
Open Access
Copyright and Licensing
Preservation and Repository Policy
Publication Ethics
Editorial Process
Contact Us
General Information
ISSN:
1798-2340 (Online)
Frequency:
Monthly
DOI:
10.12720/jait
Indexing:
ESCI (Web of Science)
,
Scopus
,
CNKI
, EBSCO,
etc
.
Acceptance Rate:
17%
APC:
1000 USD
Average Days to Accept:
106 days
Managing Editor:
Ms. Mia Hu
E-mail:
editor@jait.us
Journal Metrics:
Impact Factor 2023: 0.9
4.2
2023
CiteScore
57th percentile
Powered by
Editor-in-Chief
Prof. Kin C. Yow
University of Regina, Saskatchewan, Canada
I'm delighted to serve as the Editor-in-Chief of
Journal of Advances in Information Technology
.
JAIT
is intended to reflect new directions of research and report latest advances in information technology. I will do my best to increase the prestige of the journal.
What's New
2025-04-02
Included in Chinese Academy of Sciences (CAS) Journal Ranking 2025: Q4 in Computer Science
2025-03-20
JAIT Vol. 16, No. 3 has been published online!
2025-02-27
JAIT has launched a new Topic: "Human-Computer Interaction (HCI) in Modern Technological Systems."
Home
>
Published Issues
>
2021
>
Volume 12, No. 2, May 2021
>
Credit Card Fraud Detection Model Based on LSTM Recurrent Neural Networks
Ibtissam Benchaji, Samira Douzi, and Bouabid El Ouahidi
Faculty of Sciences IPSS, University Mohammed V, Rabat, Morocco
Abstract
—With the increasing use of credit cards in electronic payments, financial institutions and service providers are vulnerable to fraud, costing huge losses every year. The design and the implementation of efficient fraud detection system is essential to reduce such losses. However, machine learning techniques used to detect automatically card fraud do not consider fraud sequences or behavior changes which may lead to false alarms. In this paper, we develop a credit card fraud detection system that employs Long Short-Term Memory (LSTM) networks as a sequence learner to include transaction sequences. The proposed approach aims to capture the historic purchase behavior of credit card holders with the goal of improving fraud detection accuracy on new incoming transactions. Experiments show that our proposed model gives strong results and its accuracy is quite high.
Index Terms
—credit card, fraud detection, sequence learning, recurrent neural networks, LSTM
Cite: Ibtissam Benchaji, Samira Douzi, and Bouabid El Ouahidi, "Credit Card Fraud Detection Model Based on LSTM Recurrent Neural Networks," Journal of Advances in Information Technology, Vol. 12, No. 2, pp. 113-118, May 2021. doi: 10.12720/jait.12.2.113-118
Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License (
CC BY-NC-ND 4.0
), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.
4-T1097_Morocco
PREVIOUS PAPER
Audio Annotation on Myanmar Traditional Boxing Video by Enhancing DT
NEXT PAPER
Deep Learning for Uplink Spectral Efficiency in Cell-Free Massive MIMO Systems
Article Metrics in Dimensions