A data-driven explainable case-based reasoning approach for financial risk detection - Wei Li; Florentina Paraschiv; Georgios Sermpinis (Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series", 2021)
The rapid 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, 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.
Keywords: C51; C52; C53; C61; C63; D81; G21; G32; 330; Case-based reasoning; Financial risk detection; Multiple-criteria decision-making; Feature scoring; Particle swarm optimization; Parallel computing