Detecting future financial statement fraud using a machine learning model in Indonesia: a comparative study

Purpose - This study aims to compare machine learning models, datasets and splitting training-testing using data mining methods to detect financial statement fraud. Design/methodology/approach - This study uses a quantitative approach from secondary data on the financial reports of companies listed on the Indonesia Stock Exchange in the last ten years, from 2010 to 2019. Research variables use financial and non-financial variables. Indicators of financial statement fraud are determined based on notes or sanctions from regulators and financial statement restatements with special supervision. Findings - The findings show that the Extremely Randomized Trees (ERT) model performs better than other machine learning models. The best original-sampling dataset compared to other dataset treatments. Training testing splitting 80:10 is the best compared to other training-testing splitting treatments. So the ERT model with an original-sampling dataset and 80:10 training-testing splitting are the most appropriate for detecting future financial statement fraud. Practical implications - This study can be used by regulators, investors, stakeholders and financial crime experts to add insight into better methods of detecting financial statement fraud. Originality/value - This study proposes a machine learning model that has not been discussed in previous studies and performs comparisons to obtain the best financial statement fraud detection results. Practitioners and academics can use findings for further research development.