Fraud Detection Techniques in the Big Data Era

Hanae Abbassi, Imane El Alaoui, Youssef Gahi

2021

Abstract

In this age of digital technology, the methodology, complexity, and extent of fraud are drastically increasing, comprising various sectors such as credit card transactions, insurance claims, et cetera, resulting in significant financial losses. To detect fraudulent activities, organizations and financial institutions have implemented different models basing on several techniques, including data mining, machine learning (ML), and deep learning (DL). However, with the advent of big data, the traditional approaches have shown many limits, such as real-time detection and false-positive alerts. These limits have been solved mainly by advanced big data solutions. This paper aims to provide a state of the art of fraud detection techniques in a meaningful data context. Then, we review the traditional methods and identifying their limits by taking advantage of Big Data analytics patterns.

Paper Citation

in Harvard Style

Abbassi H., El Alaoui I. and Gahi Y. (2021). Fraud Detection Techniques in the Big Data Era. In Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML, ISBN 978-989-758-559-3, pages 161-170. DOI: 10.5220/0010730300003101

in Bibtex Style

@conference{bml21,
author={Hanae Abbassi and Imane El Alaoui and Youssef Gahi},
title={Fraud Detection Techniques in the Big Data Era},
booktitle={Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML,},
year={2021},
pages={161-170},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010730300003101},
isbn={978-989-758-559-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML,
TI - Fraud Detection Techniques in the Big Data Era
SN - 978-989-758-559-3
AU - Abbassi H.
AU - El Alaoui I.
AU - Gahi Y.
PY - 2021
SP - 161
EP - 170
DO - 10.5220/0010730300003101