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Indonesia’s Fraud Detection Algorithms Get a Boost with Machine Learning Model
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A recent study published in the Asian Review of Accounting has shed light on the effectiveness of machine learning algorithms in detecting financial statement fraud in Indonesia. The research, conducted by Moh. Riskiyadi from Universitas Trunojoyo Madura, compared various machine learning models and datasets to identify the best approach for detecting fraudulent activities.
Study Findings
According to the study, the Extremely Randomized Trees (ERT) model outperformed other machine learning algorithms in detecting financial statement fraud. The researchers also found that using an original-sampling dataset and a training-testing splitting ratio of 80:10 resulted in the most accurate predictions.
Implications
The findings have significant implications for regulators, investors, stakeholders, and financial crime experts seeking to combat financial statement fraud in Indonesia. “This study can be used as a reference for regulators, investors, and stakeholders to identify better methods of detecting financial statement fraud,” Riskiyadi said.
Originality and Framework for Further Research
The study’s originality lies in its proposal of a machine learning model that has not been previously discussed in the literature. The findings also provide a framework for further research development in the field.
Methodology
Riskiyadi’s research used secondary data from financial reports of companies listed on the Indonesia Stock Exchange between 2010 and 2019. The study identified indicators of financial statement fraud based on:
- Notes or sanctions from regulators
- Financial statement restatements with special supervision
- Other non-financial variables
Practical Implications
The study’s conclusions have important practical implications for the detection of financial statement fraud in Indonesia. With the increasing sophistication of fraudulent activities, it is essential to develop effective algorithms that can detect and prevent such frauds.
Consequences of Financial Statement Fraud
As a significant contributor to the country’s economy, financial statement fraud can have devastating consequences on investors, stakeholders, and the overall economy. The study’s findings offer valuable insights for regulators, investors, and financial crime experts seeking to stay ahead of fraudulent activities in Indonesia.
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