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All Journal Asia Pacific Fraud Journal Scientax: Jurnal Kajian Ilmiah Perpajakan Indonesia
Wibowo, Agung Septia
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Economics, Econometrics & Finance Social Sciences

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Evaluating the Impact of Digital Transformation and Sustainability Strategies on Earnings Management: A Text Mining Approach Wibowo, Agung Septia; Istianah, Iis; Sari, Nia Pramita; Septiari, Dovi
Asia Pacific Fraud Journal Vol. 9 No. 1: 1st Edition (January-June 2024)
Publisher : Association of Certified Fraud Examiners Indonesia Chapter

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21532/apfjournal.v9i1.339

Abstract

Digital transformation has the potential to fundamentally change companies and create value. This study examines the impact of digital transformation and sustainability strategies on the earnings quality of Indonesian listed companies on three sectors: infrastructures, transportation and logistics, and consumer non-cyclicals. Using text mining techniques to measure intensity of digital transformation and sustainability strategies, we found that sustainability strategy shown in the company’s annual report reflects lower level of earnings management especially in accrual earnings management, while companies with digital transformation strategy, particularly in artificial intelligence technology, are less likely to engage real earnings management. Findings of this study provide insights into the effect of digital transformation and sustainability on the quality of accounting information and corporate governance, and offer implications for corporate digital transformation and government regulation.
Data Mining to Detect Fraud Patterns in a Taxpayer’s Financial Statement Ginanjar, Achmad; Wibowo, Agung Septia
Scientax: Jurnal Kajian Ilmiah Perpajakan Indonesia Vol. 6 No. 2 (2025): April: Harnessing Data, Enhancing Compliance, and Empowering Policy
Publisher : Directorate General of Taxes

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52869/st.v6i2.571

Abstract

The application of machine learning in the analysis of financial statements is a relatively underexplored area compared to mainstream data mining fields, such as natural language processing (NLP) and image analysis, yet it holds significant potential. This study investigates the use of advanced linear regression techniques to identify patterns in taxpayers’ financial statements, employing a conceptual approach that combines both vertical and horizontal financial statement analysis methods. Using financial statement data reported to the Indonesian Tax Administration and historical taxation audit records,this study determines the presence of identifiable patterns. This study applies linear regression to financial statement account values to measure changes over the years and uses yearly account values to create unique data points representing each entity. A clustering method is then employed to group entities with similar patterns. The findings indicate that the proposed method can effectively analyse how entities report their financial statements over time and cluster them based on the likelihood of committing fraud, as inferred from historical audit records. These patterns are validated by instances of underpayment or overpayment of corporate income taxes identified during tax audits. By examining the clustering results, the study reveals that certain clusters accurately align with labelled patterns, correctly identifying 2 of 3 labels. The comparison between unsupervised clustering and labelled criteria demonstrates a significant fitness probability.
Co-Authors Dovi Septiari Ginanjar, Achmad Istianah, Iis Sari, Nia Pramita