Chapter 17: Accounting Forecasting With Random Forest as an Industrial Strategy: Enhancing Decision Resilience in Industry

In a rapidly evolving economic environment marked by volatility and increasing data complexity, the need for resilient and strategic financial decision-making is paramount, particularly within industrial sectors. This chapter examines the use of the random decision tree model, known as Random Forest (RF), in strategic accounting forecasting, emphasizing its contribution to more resilient decision-making, particularly in industrial environments. Through a dual methodological approach combining a bibliometric review and a systematic literature review for the period 2015–2025, the analysis highlights two major thematic axes: on the one hand, the forecasting of financial and accounting risks, and on the other hand, the advanced automation of reporting processes. The qualitative study of the 11 selected articles shows that the RF model stands out for its ability to process large, heterogeneous, and unbalanced data, making it a tool particularly suited to the uncertain and complex contexts characterizing many contemporary industries. The results demonstrate its effectiveness in the early detection of weak signals announcing financial distress, improving the quality of accounting information, and supporting proactive financial governance. This work suggests that the integration of the RF model into strategic accounting practices is not only a technical advance but also contributes to a paradigm shift in favor of predictive, dynamic, and resilient accounting. Future research avenues are proposed around the combination of this model with other methods to strengthen its operational scope.