  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.
