Systematic Literature Review

Fraud Prevention with Cluster Analysis

DOI

10.2991/978-2-38476-090-9_4How to use a DOI?

Keywords

Cluster Analysis; Euclidean; Fraud; Literature Review

Abstract

Fraud is a theme that continues to be the focus of researchers in the world because of changes in a person’s character to commit fraud due to developments in technology and knowledge. The main focus is, among others, the existence of a corporate crisis due to the declining financial performance of the company due to fraud. In this study, the aim of this study is to classify studies that discuss fraud in companies and classify companies based on the company’s fraud data. The literature used has the topic of fraud using cluster analysis. The results of the study show that the cluster clustering technique used by previous researchers to classify fraud tends to use clustering based on mature methods, where 41% of the clustering techniques encountered are partitional clusters. It is still difficult to find relatively new groupings such as ensemble grouping, large-scale grouping, multi-way grouping. Among the partition clustering techniques, k-means, c-means and their variants with Euclidian distance as the dissimilarity metric is the most common and popular to use. The hierarchical clustering technique was in second place used in a quarter of the papers surveyed. Interactive, clustering visualization techniques are also used but only in a small number of cases. Furthermore, the results of grouping companies using the Ward Linkage method with Manhattan Distance show that two clusters are formed, each with 8 and 24 members. Cluster 1 is the highest because most cluster 1 has the largest average value of the indicator compared to the others.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Agung Budiwibowo
AU  - Endang Siti Astuti
AU  - Muhammad Saifi
AU  - Mohammad Iqbal
PY  - 2023
DA  - 2023/08/01
TI  - Systematic Literature Review
BT  - Proceedings of the Fifth Annual International Conference on Business and Public Administration (AICoBPA 2022)
PB  - Atlantis Press
SP  - 29
EP  - 41
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-090-9_4
DO  - 10.2991/978-2-38476-090-9_4
ID  - Budiwibowo2023
ER  -