Machine Learning-Based Financial Health Assessment Model for Resource-Based Enterprises

Abstract

Resource-based enterprises, such as those in mining, oil, gas, and forestry, face unique financial challenges due to the inherent volatility of commodity prices, environmental regulations, and resource depletion. Traditional financial health models often fail to capture the dynamic, real-time external factors affecting these industries. This paper proposes a novel machine learning-based financial health assessment model tailored to resource-based enterprises. The proposed model integrates both traditional financial metrics and dynamic external variables—such as commodity market trends, regulatory changes, and environmental risks—into a hybrid machine learning framework. The model utilizes a multi-layered architecture comprising Gradient Boosting Machines (GBMs), Long Short-Term Memory (LSTM) networks, and Transformer-based NLP models. A reinforcement learning component enables the model to adapt in real-time, ensuring it remains responsive to changing market conditions. The Dynamic Financial Health Score (DFHS), produced by the model, offers real-time, interpretable financial health assessments that are vital for proactive risk management. Experimental results show that the proposed model outperforms traditional models such as the Altman Z-score and logistic regression, demonstrating higher accuracy, recall, and predictive reliability. This research highlights the importance of incorporating real-time, dynamic data into financial health assessments and provides a robust, adaptable solution for managing financial risks in resource-based industries.