MODELING OF FRAUD DETECTION IN FINANCIAL STATEMENT PRESENTATION IN BANKING COMPANIES USING PANEL DATA REGRESSION WITH THE FRAUD HEXAGON THEORY APPROACH

Feny Ulil Amrina; Sri Pingit Wulandari; Fausania Hibatullah

doi:10.31539/aah6te05

Authors

Feny Ulil Amrina Institut Teknologi Sepuluh Nopember
Sri Pingit Wulandari Institut Teknologi Sepuluh Nopember
Fausania Hibatullah Institut Teknologi Sepuluh Nopember1

DOI:

https://doi.org/10.31539/aah6te05

Keywords:

Fraud Hexagon, Financial Statement Fraud, Banking Sector, Beneish M-Score, Panel Data Regression.

Abstract

The banking sector faces a high risk of financial statement fraud that can harm various parties. This study aims to describe fraud characteristics and analyze the effect of Fraud Hexagon elements on indications of financial statement fraud, measured using the Beneish M-Score. This quantitative research applies panel data regression analysis on 37 conventional banking companies listed on the Indonesia Stock Exchange for the 2022–2024 period, where the Random Effect Model (REM) was selected as the best estimation model. The findings indicate that, on average, companies are in a safe category, but 2022 recorded the highest manipulation risk. Partial tests reveal that only Stimulus and Opportunity variables have a significant effect. Stimulus, proxied by Return on Assets, positively affects fraud indication, while the use of Big Four auditors in the Opportunity variable effectively reduces manipulation risk. Capability, Rationalization, Ego, and Collusion variables are not significant. The implications emphasize the need for strict supervision of banks with high performance pressure and maintaining external audit quality to strengthen financial reporting integrity and prevent manipulative practices in the banking sector.

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