A Data-driven Explainable Case-based Reasoning Approach for Financial Risk Detection

The rapid development of artificial intelligence methods contributes to their wide applicationsfor forecasting various financial risks in recent years. This study introduces a novel explainablecase-based reasoning (CBR) approach without a requirement of rich expertise in financial risk.Compared with other black-box algorithms, the explainable CBR system allows a natural eco-nomic interpretation of results. Indeed, the empirical results emphasize the interpretability ofthe CBR system in predicting financial risk, which is essential for both financial companies andtheir customers. In addition, our results show that the proposed automatic design CBR systemhas a good prediction performance compared to other artificial intelligence methods, overcomingthe main drawback of a standard CBR system of highly depending on prior domain knowledgeabout the corresponding field