A Data-driven Case-based Reasoning in Bankruptcy Prediction
This addresses the need for interpretable bankruptcy prediction models for financial analysts and practitioners, though it is incremental as it builds on existing case-based reasoning methods.
The study tackled the problem of limited interpretability in machine learning models for bankruptcy prediction by proposing a data-driven explainable case-based reasoning system, which achieved competitive accuracy with state-of-the-art models and improved explainability by capturing asymmetrically distributed financial attributes.
There has been intensive research regarding machine learning models for predicting bankruptcy in recent years. However, the lack of interpretability limits their growth and practical implementation. This study proposes a data-driven explainable case-based reasoning (CBR) system for bankruptcy prediction. Empirical results from a comparative study show that the proposed approach performs superior to existing, alternative CBR systems and is competitive with state-of-the-art machine learning models. We also demonstrate that the asymmetrical feature similarity comparison mechanism in the proposed CBR system can effectively capture the asymmetrically distributed nature of financial attributes, such as a few companies controlling more cash than the majority, hence improving both the accuracy and explainability of predictions. In addition, we delicately examine the explainability of the CBR system in the decision-making process of bankruptcy prediction. While much research suggests a trade-off between improving prediction accuracy and explainability, our findings show a prospective research avenue in which an explainable model that thoroughly incorporates data attributes by design can reconcile the dilemma.