AI-Powered Revaluation of Fixed Assets During Corporate Restructuring: A Machine Learning Approach

Abstract

In the dynamic landscape of corporate restructuring, accurate revaluation of fixed assets plays a critical role in ensuring transparency, financial reliability, and informed decision-making. Traditional valuation methods often suffer from limitations such as subjectivity, inconsistency, and dependence on expert judgment, making them inadequate in complex restructuring scenarios. This study proposes an AI-driven machine learning (ML) framework that integrates advanced algorithms such as Gradient Boosting Machines (GBM), Random Forest, and Artificial Neural Networks (ANN) to model, predict, and optimize asset revaluation processes. Using real corporate financial datasets and restructuring case studies, the model is trained to identify hidden patterns, adjust for market volatility, and mitigate valuation bias. Experimental results demonstrate superior accuracy and robustness compared to conventional appraisal techniques, thereby offering a scalable and objective approach to fixed asset valuation. The findings provide valuable insights for corporate strategists, auditors, and financial regulators by showcasing how AI and ML can enhance asset transparency, compliance, and stakeholder confidence during corporate restructuring. The paper concludes with discussions on challenges, limitations, and future directions for integrating AI in corporate financial management.