Decision Trees for Strategic Choice of Augmenting Management Intuition with Machine Learning

Abstract

Strategic financial decision-making is critical for organizational sustainability and competitive advantage. However, traditional approaches that rely solely on human expertise or isolated machine learning (ML) models often fall short in capturing the complex, multifaceted, and often asymmetrical nature of financial data, leading to suboptimal predictions and limited interpretability. This study addresses these challenges by developing an innovative, symmetry-aware integrated ML framework that synergizes decision trees, advanced ensemble techniques, and human expertise to enhance both predictive accuracy and model transparency. The proposed framework employs a symmetrical dual-feature selection process, combining automated methods based on decision trees with expert-guided selections, ensuring the inclusion of both statistically significant and domain-relevant features. Furthermore, the integration of human expertise facilitates rule-based adjustments and iterative feedback loops, refining model performance and aligning it with practical financial insights. Empirical evaluation shows a significant improvement in ROC-AUC by 2% and F1-score by 1.5% compared to baseline and advanced ML models alone. The inclusion of expert-driven rules, such as thresholds for debt-to-equity ratios and profitability margins, enables the model to account for real-world asymmetries that automated methods may overlook. Visualizations of the decision trees offer clear interpretability, providing decision-makers with symmetrical insight into how financial metrics influence bankruptcy predictions. This research demonstrates the effectiveness of combining machine learning with expert knowledge in bankruptcy prediction, offering a more robust, accurate, and interpretable decision-making tool. By incorporating both algorithmic precision and human reasoning, the study presents a balanced and symmetrical hybrid approach, bridging the gap between data-driven analytics and domain expertise. The findings underscore the potential of symmetry-driven integration of ML techniques and expert knowledge to enhance strategic financial decision-making.

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