Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

IRTG1792DP2021 010

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

Wei Li
Florentina Paraschiv
Georgios Sermpinis

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

Case-based reasoning, Financial risk detection, Multiple-criteria decision-
making, Feature scoring, Particle swarm optimization, Parallel computing

JEL Classification:
C51, C52, C53, C61, C63, D81, G21, G32