An Analysis of the Most Used Machine Learning Algorithms for Online Fraud Detection

Elena-Adriana Minastireanu () and Gabriela Mesnita ()

Informatica Economica, 2019, vol. 23, issue 1, 5-16

Abstract: Today illegal activities regarding online financial transactions have become increasingly complex and borderless, resulting in huge financial losses for both sides, customers and organizations. Many techniques have been proposed to fraud prevention and detection in the online environment. However, all of these techniques besides having the same goal of identifying and combating fraudulent online transactions, they come with their own characteristics, advantages and disadvantages. In this context, this paper reviews the existing research done in fraud detection with the aim of identifying algorithms used and analyze each of these algorithms based on certain criteria. To analyze the research studies in the field of fraud detection, the systematic quantitative literature review methodology was applied. Based on the most called machine-learning algorithms in scientific articles and their characteristics, a hierarchical typology is made. Therefore, our paper highlights, in a new way, the most suitable techniques for detecting fraud by combining three selection criteria: accuracy, coverage and costs.

Keywords: Bank fraud; Detection algorithms; Machine-Learning algorithms; Online transactions (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://revistaie.ase.ro/content/89/01%20-%20minastireanu,%20mesnita.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:aes:infoec:v:23:y:2019:i:1:p:5-16

Access Statistics for this article

Informatica Economica is currently edited by Ion Ivan

More articles in Informatica Economica from Academy of Economic Studies - Bucharest, Romania Contact information at EDIRC.
Bibliographic data for series maintained by Paul Pocatilu ().