Transforming accounting with predictive analytics and machine learning - Khalafi, Sepideh; Bagherpanah, Sasan (Journal of Accounting, Business and Finance Research, 2025)
The integration of predictive analytics and machine learning (ML) is transforming the accounting profession, enabling a shift from traditional, retrospective approaches to proactive, data-driven decision-making. By leveraging historical data, statistical algorithms, and machine learning techniques, accountants can forecast financial trends, enhance fraud detection, and improve regulatory compliance. This paper demonstrates the implementation of advanced ML models, such as Extra Trees Regressor and CatBoost, to optimize financial predictions. Through a comprehensive evaluation of key performance metrics (e.g., MAE, RMSE, R²), the study identifies the Extra Trees Regressor as the most effective model, excelling in both prediction accuracy and reliability. However, challenges such as data quality, algorithmic fairness, and skill gaps remain significant barriers to adoption. The findings underscore the transformative potential of predictive analytics and ML in improving financial reporting, automating repetitive tasks, and ensuring adherence to compliance standards. This research highlights the critical role of interdisciplinary collaboration in overcoming integration challenges and achieving competitive advantages in the evolving financial landscape.