["Corporate Social Responsibility and Environmental Management, Volume 33, Issue 2, Page 2906-2927, March 2026. ", "\nABSTRACT\nUsing the environmental quality cost management model, this study examines how fraud risk management (FRM) influences corporate sustainability performance (CSP) and how ownership structures moderate it. The study uses artificial neural networks (ANN) and logistic regression models to test two hypotheses. H1 demonstrates that the prevention and appraisal capabilities embedded in FRM can enhance CSP by suppressing external failures, utilising leakage‐free stratified validation, and incorporating complementary learner systems. In H1, artificial neural networks outperform Logistic Regression, which indicates non‐linear control–outcome relationships (ANN: accuracy 0.942, AUC 0.972, precision 0.944, recall 0.912, F1 0.927; Logistic: accuracy 0.869, AUC 0.932, precision 0.861, recall 0.808, F1 0.834). Using state‐owned/JV and indigenous ownership structures, H2 examines the moderating effects on FRM and CSP (ANN: accuracy 0.93, AUC 0.975, precision 0.923, recall 0.940, F1 0.931; Logistic: accuracy 0.92, AUC 0.973, precision 0.917, recall 0.933, F1 0.919). Results suggest that ownership structure can moderate the relationship by strengthening agency (incentive alignment), legitimacy and disclosure (stakeholder and institutional), and capability deployment (resource‐based view). It is most reliable to achieve sustainability gains by embedding FRM into governance routines and disclosures, calibrated to ownership ethics and local content obligations. Further implications of these findings are presented in the study.\n"]