Predictive Data Mining Analysis of Ownership Structures and their Influence on Corporate Tax Avoidance

This study aims to examine the impact of corporate ownership structures on tax avoidance using a predictive data mining approach. The main challenge addressed is understanding how variations in ownership influence a firm’s strategic financial decisions, particularly its tendency to engage in tax minimization practices. By applying advanced predictive data mining techniques, the research uncovers significant patterns, identifies key ownership features, and models their relationship with tax avoidance outcomes. The dataset, derived from corporate financial statements and ownership records, is systematically preprocessed, feature-selected, and validated to ensure reliable predictive performance. Results demonstrate that differences in ownership structures significantly affect tax avoidance behavior, with certain ownership characteristics consistently emerging as strong predictors. These findings offer computational insights for both academic understanding and practical applications, helping regulators anticipate risky ownership configurations and improve policy oversight. The study highlights the importance of integrating ownership theory with predictive modeling to enhance the transparency, interpretability, and robustness of corporate tax strategy analyses.

@article{Herawati2026,
  title     = {Predictive Data Mining Analysis of Ownership Structures and their Influence on Corporate Tax Avoidance},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {1},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Tuti Herawati and Helmi Yazid and Nurhayati Soleha and Munawar Muchlish},
  doi       = {10.14569/IJACSA.2026.0170197},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170197}
}