“A Data-driven Explainable Case-based Reasoning Approach for Financial Risk Detection” (2022) by Wei Li , Florentina Paraschiv , Georgios Sermpinis, Quantitative Finance, forthcoming.
development of artificial intelligence methods contributes to their wide
applications for forecasting various financial risks in recent years. This
study introduces a novel explainable case-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 economic
interpretation of results. Indeed, the empirical results emphasize the
interpretability of the CBR system in predicting financial risk, which is
essential for both financial companies and their customers. In addition, our
results show that the proposed automatic design CBR system has a good
prediction performance compared to other artificial intelligence methods,
overcoming the main drawback of a standard CBR system of highly depending on
prior domain knowledge about the corresponding field.