Pentagon fraud perspective analysis in detecting indications of financial statement fraud

https://doi.org/10.21744/irjmis.v8n6.1958

Authors

  • Ni Putu Winda Ayuningtyas Faculty of Economics and Business, Udayana University, Bali, Indonesia
  • I Putu Sudana Faculty of Economics and Business, Udayana University, Bali, Indonesia
  • I Wayan Suartana Faculty of Economics and Business, Udayana University, Bali, Indonesia
  • Ni Putu Sri Harta Mimba Faculty of Economics and Business, Udayana University, Bali, Indonesia

Keywords:

banking, financial statement fraud, fraud pentagon, multiple linear regression, non-probability sampling

Abstract

This study aims to obtain empirical evidence regarding the elements of fraud pentagon theory on indications of financial statement fraud. This research was conducted on banking companies listed on the Indonesia Stock Exchange (IDX) for the 2017-2020 period. The method of determining the sample used is non-probability sampling with purposive sampling technique. The number of samples that meet the sample selection criteria are 140 samples, namely 35 banking companies during the four years of observation 2017-2020. The data analysis technique used is multiple linear regression. Based on the results of the analysis, it is stated that the nature of industry and change of directors have a negative effect on indications of financial statement fraud, while personal financial need, rationalization, and CEO duality have no effect on indications of financial statement fraud. This study has implications for investors, creditors, the government and other parties who need financial statement information to consider the elements of the fraud pentagon theory to detect indications of fraudulent financial statements in banking companies.

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Published

2021-10-28

How to Cite

Ayuningtyas, N. P. W., Sudana, I. P., Suartana, I. W., & Mimba, N. P. S. H. (2021). Pentagon fraud perspective analysis in detecting indications of financial statement fraud. International Research Journal of Management, IT and Social Sciences, 8(6), 619–629. https://doi.org/10.21744/irjmis.v8n6.1958

Issue

Vol. 8 No. 6 (2021): November

Section

Peer Review Articles
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