JOURNAL ARTICLE

Graph Distances for Determining Entities Relationships: A Topological Approach to Fraud Detection.

  • Published In: International Journal of Information Technology & Decision Making, 2023, v. 22, n. 4. P. 1403 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Calabuig, J. M.; Falciani, H.; Sapena, A. Ferrer; Raffi, L. M. García; Pérez, E. A. Sánchez 3 of 3

Abstract

A new model for the control of financial processes based on metric graphs is presented. Our motivation has its roots in the current interest in finding effective algorithms to detect and classify relations among elements of a social network. For example, the analysis of a set of companies working for a given public administration or other figures in which automatic fraud detection systems are needed. Given a set Ω and a proximity function ϕ : Ω × Ω → ℝ + , we define a new metric for Ω by considering a path distance in Ω that is considered as a graph. We analyze the properties of such a distance, and several procedures for defining the initial proximity matrix (ϕ (a , b)) (a , b) ∈ Ω × Ω . Using this formalism, we state our main idea regarding fraud detection: financial fraud can be detected because it produces a meaningful local change of density in the metric space defined in this way. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Information Technology & Decision Making. 2023/07, Vol. 22, Issue 4, p1403
  • Document Type:Article
  • Subject Area:Physics
  • Publication Date:2023
  • ISSN:0219-6220
  • DOI:10.1142/S0219622022500730
  • Accession Number:168590250
  • Copyright Statement:Copyright of International Journal of Information Technology & Decision Making is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.