Enhancing financial risk prediction with symbolic classifiers: addressing class imbalance and the accuracy–interpretability trade–off

Abstract

Machine learning for financial risk prediction has garnered substantial interest in recent decades. However, the class imbalance problem and the dilemma of accuracy gain by loss interpretability have yet to be widely studied. Symbolic classifiers have emerged as a promising solution for forecasting banking failures and estimating creditworthiness as it addresses class imbalance while maintaining both accuracy and interpretability. This paper aims to evaluate the effectiveness of REMED, a symbolic classifier, in the context of financial risk management, and focuses on its ability to handle class imbalance and provide interpretable decision rules. Through empirical analysis of a real-world imbalanced financial dataset from the Federal Deposit Insurance Corporation, we demonstrate that REMED effectively handles class imbalance, improving performance accuracy metrics while ensuring interpretability through a concise and easily understandable rule system. A comparative analysis is conducted against two well-known rule-generating approaches, J48 and JRip. The findings suggest that, with further development and validation, REMED can be implemented as a competitive approach to improve predictive accuracy on imbalanced financial datasets without compromising model interpretability