Detecting financial statement fraud using new ensemble learning: evidence during the COVID-19 pandemic in Indonesia

Purpose - This study aims to propose a new ensemble learning model and compare its performance with other ensemble models to obtain the best model for detecting financial statement fraud during the COVID-19 pandemic. Design/methodology/approach - This study uses a quantitative approach, using secondary data from financial reports, annual reports, regulatory reports and other information on the internet. It focuses on all companies listed on the Indonesia Stock Exchange from 2020 to 2023. The independent variables in this study use financial and nonfinancial variables. In contrast, the target variable for fraudulent financial reports is based on sanctions from regulators and the company’s special supervisory status. Findings - This study results show that the ensemble blending model performs best in detecting financial statement fraud compared to the ensemble model that construct it. Research limitations/implications - This study sets ensemble learning to default settings. Setting certain conditions can further improve the performance of ensemble learning models. Practical implications - This study can broaden the insights of practitioners, academics, investors, regulators, stakeholders and corporate finance experts into detecting financial report fraud. Originality/value - This study proposes a new ensemble learning model that previous studies have not discussed. This ensemble learning model performs best compared to other ensemble learning models.