Fraud Detection or Prevention Technologies

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 March 2023) | Viewed by 2324

Special Issue Editor


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Guest Editor
1. Faculty of Data Science, Musashino University, Tokyo, Japan
2. Professor Emeritus, Faculty of Environmental Information, Keio University, Tokyo, Japan
Interests: data science; science and health policy; artificial intelligence; molecular hydrogen
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Special Issue Information

Dear Colleagues,

COVID-19 has dramatically increased the number of different fraud issues in the world. Fraud costs the global economy over USD 5 trillion as of 2019. There are many types of fraud: payroll fraud, asset misappropriation, invoice fraud, financial statement fraud, tax fraud, identity theft, insurance fraud, banking fraud, money fraud, digital exploitation, and fraud in organizations or (local) government. The goal of this Special Issue is to reduce local or global fraud and to strengthen the resilience of a society, organization, or private entity against fraud. The Special Issue is interested in any types of fraud detection or prevention technologies or applications. Articles in the Special Issue include original articles, review articles, tutorials, case studies, and software articles.

Prof. Dr. Yoshiyasu Takefuji
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • digital fraud
  • fraud detection
  • fraud prevention
  • resilience against fraud
  • fraud in government

Published Papers (1 paper)

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Research

13 pages, 1469 KiB  
Article
STALITA: Innovative Platform for Bank Transactions Analysis
by David Jesenko, Štefan Kohek, Borut Žalik, Matej Brumen, Domen Kavran, Niko Lukač, Andrej Živec and Aleksander Pur
Appl. Sci. 2022, 12(23), 12492; https://0-doi-org.brum.beds.ac.uk/10.3390/app122312492 - 6 Dec 2022
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Abstract
Acts of fraud have become much more prevalent in the financial industry with the rise of technology and the continued economic growth in modern society. Fraudsters are evolving their approaches continuously to exploit the vulnerabilities of the current prevention measures in place, many [...] Read more.
Acts of fraud have become much more prevalent in the financial industry with the rise of technology and the continued economic growth in modern society. Fraudsters are evolving their approaches continuously to exploit the vulnerabilities of the current prevention measures in place, many of whom are targeting the financial sector. To overcome and investigate financial frauds, this paper presents STALITA, which is an innovative platform for the analysis of bank transactions. STALITA enables graph-based data analysis using a powerful Neo4j graph database and the Cypher query language. Additionally, a diversity of other supporting tools, such as support for heterogeneous data sources, force-based graph visualisation, pivot tables, and time charts, enable in-depth investigation of the available data. In the Results section, we present the usability of the platform through real-world case scenarios. Full article
(This article belongs to the Special Issue Fraud Detection or Prevention Technologies)
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